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  • 1.
    A. Mouris, Boules
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Ghauch, Hadi
    Department of COMELEC, Institut Mines-Telecom, Telecom-ParisTech, Paris, 91120, France.
    Thobaben, Ragnar
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Jonsson, B. Lars G.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering.
    Multi-tone Signal Optimization for Wireless Power Transfer in the Presence of Wireless Communication Links2020In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 19, no 5, p. 3575-3590Article in journal (Refereed)
    Abstract [en]

    In this paper, we study optimization of multi-tone signals for wireless power transfer (WPT) systems. We investigate different non-linear energy harvesting models. Two of them are adopted to optimize the multi-tone signal according to the channel state information available at the transmitter. We show that a second-order polynomial curve-fitting model can be utilized to optimize the multi-tone signal for any RF energy harvester design. We consider both single-antenna and multi-antenna WPT systems. In-band co-existing communication links are also considered in this work by imposing a constraint on the received power at the nearby information receiver to prevent its RF front end from saturation. We emphasize the importance of imposing such constraint by explaining how inter-modulation products, due to saturation, can cause high interference at the information receiver in the case of multi-tone signals. The multi-tone optimization problem is formulated as a non-convex linearly constrained quadratic program. Two globally optimal solution approaches using mixed-integer linear programming and finite branch-and-bound techniques are proposed to solve the problem. The achieved improvement resulting from applying both solution methods to the multi-tone optimization problem is highlighted through simulations and comparisons with other solutions existing in the literature.

  • 2.
    A. Mouris, Boules
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Kolitsidas, Christos
    Ericsson, Systems and Technology-HW Research, Kista, 164 80, Sweden.
    Thobaben, Ragnar
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    A Dual-Polarized Multi-Antenna Structure for Simultaneous Transmission of Wireless Information and Power2019In: 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, APSURSI 2019 - Proceedings, IEEE, 2019, p. 1805-1806, article id 8889079Conference paper (Refereed)
    Abstract [en]

    In this paper, a dual-polarized multi-antenna structure is designed at 2.45 GHz with the goal of allowing simultaneous transmission of wireless information and power. Differential feeding was used to minimize the mutual coupling due to radiation leakage in addition to a mushroom-type EBG structure for suppressing the surface waves. Simulation results for the proposed structure show a mutual coupling level lower than -40 dB between the information transmitting antenna and the power transmitting antennas for both polarizations. The isolation level between the antennas is improved by at least 22 dB and 14 dB for the E-plane and H-plane coupling, respectively.

  • 3.
    Adolphson, Katja
    et al.
    Karolinska Inst, Stockholm, Sweden.;Karolinska Univ Hosp, Astrid Lindgren Childrens Hosp, Stockholm, Sweden..
    Honore, Antoine
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering. Karolinska Inst, Stockholm, Sweden.;Karolinska Univ Hosp, Astrid Lindgren Childrens Hosp, Stockholm, Sweden..
    Forsberg, David
    Karolinska Inst, Stockholm, Sweden.;Karolinska Univ Hosp, Astrid Lindgren Childrens Hosp, Stockholm, Sweden..
    Stålhammar, Alexander Mildalen
    Karolinska Inst, Stockholm, Sweden.;Karolinska Univ Hosp, Astrid Lindgren Childrens Hosp, Stockholm, Sweden..
    Jost, Kerstin
    Karolinska Inst, Stockholm, Sweden.;Karolinska Univ Hosp, Astrid Lindgren Childrens Hosp, Stockholm, Sweden..
    Herlenius, Eric
    Karolinska Inst, Stockholm, Sweden.;Karolinska Univ Hosp, Astrid Lindgren Childrens Hosp, Stockholm, Sweden..
    Predicting acute adverse events in neonates using automated vital sign pattern analysis2021In: Pediatric Research, ISSN 0031-3998, E-ISSN 1530-0447, Vol. 90, no SUPPL 1, p. 22-22Article in journal (Other academic)
  • 4.
    Aidi, Laili
    et al.
    KTH, School of Information and Communication Technology (ICT), Centres, Center for Wireless Systems, Wireless@kth.
    Markendahl, Jan
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Communication Systems, CoS, Radio Systems Laboratory (RS Lab). KTH, School of Information and Communication Technology (ICT), Centres, Center for Wireless Systems, Wireless@kth.
    Tollmar, Konrad
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Communication Systems, CoS. KTH, School of Information and Communication Technology (ICT), Centres, Center for Wireless Systems, Wireless@kth.
    Selvakumar, Ekambar
    KTH, School of Information and Communication Technology (ICT).
    Huang, Jin
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Blennerud, Greger
    Ericsson, Torshamnsgatan 48, S-16440, Sweden., Torshamnsgatan 48.
    Mobile music business models in Asia’s emerging markets2013In: 12th International Conference on Mobile Business, ICMB 2013, Association for Information Systems , 2013Conference paper (Refereed)
    Abstract [en]

    In the telecom business, there has been a heavy competition from Internet, media and handset vendors companies. These over-the-top (OTT) players offer compiling telecom services, cause a transformation in the telecom business ecosystem, and the most challenging services posed here are media services. China, India and Indonesia, as world’s emerging markets in Asia, are predicted to take the largest share in the global mobile traffic explosion by 2015. It is critical for mobile network operators (MNOs) in this region to explore strategy for mobile media services, as mobile broadband is likely preferred compared to fixed broadband. In this paper, we analyze and compare mobile music business models used in these markets and structure the relation models between the key actors, using Actors, Relations and Business Activities (ARA) model. We present the economic models that are emerging, and an insight of why and how these multitudes actors are betting on currently. We found that the MNOs generally have a much stronger position compared to their counterparts in the developed markets, and the personalization services, like ring-back tone, are still a huge success. The actors tend to deliver the services by their own, rather than to collaborate in a horizontal business setting.

  • 5.
    Ainomae, Ahti
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Bengtsson, Mats
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Trump, Tonu
    Tallinn Univ Technol, Dept Radio & Telecommun Engn, EE-12616 Tallinn, Estonia..
    Distributed Largest Eigenvalue-Based Spectrum Sensing Using Diffusion LMS2018In: IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS, ISSN 2373-776X, Vol. 4, no 2, p. 362-377Article in journal (Refereed)
    Abstract [en]

    In this paper, we propose a distributed detection scheme for cognitive radio (CR) networks, based on the largest eigenvalues (LEs) of adaptively estimated correlation matrices (CMs), assuming that the primary user signal is temporally correlated. The proposed algorithm is fully distributed, there by avoiding the potential single point of failure that a fusion center would imply. Different forms of diffusion least mean square algorithms are used for estimating and averaging the CMs over the CR network for the LE detection and the resulting estimation performance is analyzed using a common framework. In order to obtain analytic results on the detection performance, the exact distribution of the CM estimates are approximated by a Wishart distribution, by matching the moments. The theoretical findings are verified through simulations.

  • 6.
    Al-Atat, Ghina
    et al.
    IMDEA Networks Institute, Madrid, Spain.
    Fresa, Andrea
    IMDEA Networks Institute, Madrid, Spain.
    Behera, Adarsh Prasad
    IMDEA Networks Institute, Madrid, Spain.
    Moothedath, Vishnu Narayanan
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Gross, James
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Champati, Jaya Prakash
    IMDEA Networks Institute, Madrid, Spain.
    The Case for Hierarchical Deep Learning Inference at the Network Edge2023In: NetAISys 2023 - Proceedings of the 1st International Workshop on Networked AI Systems, Part of MobiSys 2023, Association for Computing Machinery (ACM) , 2023, p. 13-18Conference paper (Refereed)
    Abstract [en]

    Resource-constrained Edge Devices (EDs), e.g., IoT sensors and microcontroller units, are expected to make intelligent decisions using Deep Learning (DL) inference at the edge of the network. Toward this end, developing tinyML models is an area of active research - DL models with reduced computation and memory storage requirements - that can be embedded on these devices. However, tinyML models have lower inference accuracy. On a different front, DNN partitioning and inference offloading techniques were studied for distributed DL inference between EDs and Edge Servers (ESs). In this paper, we explore Hierarchical Inference (HI), a novel approach proposed in [19] for performing distributed DL inference at the edge. Under HI, for each data sample, an ED first uses a local algorithm (e.g., a tinyML model) for inference. Depending on the application, if the inference provided by the local algorithm is incorrect or further assistance is required from large DL models on edge or cloud, only then the ED offloads the data sample. At the outset, HI seems infeasible as the ED, in general, cannot know if the local inference is sufficient or not. Nevertheless, we present the feasibility of implementing HI for image classification applications. We demonstrate its benefits using quantitative analysis and show that HI provides a better trade-off between offloading cost, throughput, and inference accuracy compared to alternate approaches.

  • 7.
    Ali, Sadiq
    et al.
    Electrical Engineering, University of Engineering and Technology of Peshawar, Pakistan.
    Jansson, Magnus
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Seco-Granados, Gonzalo
    Telecommunications and Systems Engineering, Universitat Autònoma de Barcelona, Spain.
    Lopez-Salcedo, Jose A.
    Universitat Autònoma de Barcelona, Spain.
    Kronecker-Based Fusion Rule for Cooperative Spectrum Sensing with Multi-Antenna Receivers2014In: Electronics, E-ISSN 2079-9292, Vol. 3, no 4, p. 675-688Article in journal (Refereed)
    Abstract [en]

    This paper considers a novel fusion rule for spectrum sensing scheme for a cognitive radio network with multi-antenna receivers. The proposed scheme exploits the fact that when any primary signal is present, measurements are spatially correlated due to presence of inter-antenna and inter-receiver spatial correlation. In order to exploit this spatial structure, the generalized likelihood ratio test (GLRT) operates with the determinant of the sample covariance matrix. Therefore, it depends on the sample size N and the dimensionality of the received data (i.e., the number of receivers K and antennas L). However, when the dimensionality fK; Lg is on the order, or larger than the sample size N, the GLRT degenerates due to the ill-conditioning of the sample covariance matrix. In order to circumvent this issue, we propose two techniques that exploit the inner spatial structure of the received observations by using single pair and multi-pairs Kronecker products. The performance of the proposed detectors is evaluated by means of numerical simulations, showing important advantages with respect to the traditional (i.e., unstructured) GLRT approach.

  • 8.
    Alizadeh, Mahmoud
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering. University of Gävle.
    Characterisation, Modelling and Digital Pre-DistortionTechniques for RF Transmitters in Wireless Systems2019Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Wireless systems have become an inevitable part of modern technologies serving humankind. The rapid growth towards large dimensional systems, e.g. 5th generation (5G) technologies, incurs needs for improving the performance of the systems and considering aspects to make them as far as possible environmentally friendly in terms of power efficiency, cost, and so on. One of the key parts of every wireless communication system is the radio frequency (RF) power amplifier (PA), which consumes the largest percentage of the total energy. Hence, accurate models of RF PAs can be used to optimize their design and to compensate for signal distortions. This thesis starts with two methods for frequency-domain characterisation to analyse the dynamic behaviour of PAs in 3rd-order non-linear systems. Firstly, two-tone signals superimposed on large-signals are used to analyse the frequency-domain symmetry properties of inter-modulation (IM) distortions and Volterra kernels in different dynamic regions of RF PAs in a single-input single-output (SISO) system. Secondly, three-tone signals are used to characterise the 3rd-order self- and cross-Volterra kernels of RF PAs in a 3 × 3 multiple-input multiple-output (MIMO) system. The main block structures of the models are determined by analysing the frequency-domain symmetry properties of the Volterra kernels in different three-dimensional (3D) frequency spaces. This approach significantly simplifies the structure of the 3rd-order non-linear MIMO model.

    The following parts of the thesis investigate techniques for behavioural modelling and linearising RF PAs. A piece-wise modelling technique is proposed to characterise the dynamic behaviour and to mitigate the impairments of non-linear RF PAs at different operating points (regions). A set of thresholds decompose the input signal into several sub-signals that drive the RF PAs at different operating points. At each operating point, the PAs are modelled by one sub-model, and hence, the complete model consists of several sub-models. The proposed technique reduces the model errors compared to conventional piece-wise modelling techniques.

    A block structure modelling technique is proposed for RF PAs in a MIMO system based on the results of the three-tone characterisation technique. The main structures of the 3rd- and higher-order systems are formulated based on the frequency dependence of each block. Hence, the model can describe more relevant interconnections between the inputs and outputs than conventional polynomial-type models.

    This thesis studies the behavioural modelling and compensation techniques in both the time and the frequency domains for RF PAs in a 3 × 3MIMO system. The 3D time-domain technique is an extension of conventional 2D generalised memory polynomial (GMP) techniques. To reduce the computational complexity, a frequency-domain technique is proposed that is efficient and feasible for systems with long memory effects. In this technique, the parameters of the model are estimated within narrow sub-bands. Each sub-band requires only a few parameters, and hence the size of the model for each sub-band is reduced.

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  • 9.
    Alizadeh, Mahmoud
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering. University of Gävle.
    Händel, Peter
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Rönnow, Daniel
    University of Gävle.
    Basis Function Decomposition Approach in Piece-Wise Modeling for RF Power Amplifiers2018In: 6th Telecommunications forum TELFOR 2018, Belgrade, Serbia, 2018Conference paper (Refereed)
    Abstract [en]

    In this paper, a new approach is proposed to decompose the basis functions in a piece-wise modeling technique for nonlinear radio frequency (RF) power amplifiers. The proposed technique treats the discontinuity problem of the model output at the joint points between different operating points, whereas preserves the linear and nonlinear properties of the original model within each region. Experimental results show that the proposed technique outperforms the conventional piece-wise model in terms of model errors.

  • 10.
    Alizadeh, Mahmoud
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering. Department of Electrical Engineering, Mathematics and Science, University of Gävle, Gävle, Sweden.
    Händel, Peter
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Rönnow, Daniel
    Univ Gavle, Dept Elect Engn Math & Sci, Gavle, Sweden..
    Behavioral modeling and digital pre-distortion techniques for RF PAs in a 3 x 3 MIMO system2019In: International journal of microwave and wireless technologies, ISSN 1759-0795, E-ISSN 1759-0787, Vol. 11, no 10, p. 989-999Article in journal (Refereed)
    Abstract [en]

    Modern telecommunications are moving towards (massive) multi-input multi-output (MIMO) systems in 5th generation (5G) technology, increasing the dimensionality of the systems dramatically. In this paper, the impairments of radio frequency (RF) power amplifiers (PAs) in a 3 x 3 MIMO system are compensated in both the time and the frequency domains. A three-dimensional (3D) time-domain memory polynomial-type model is proposed as an extension of conventional 2D models. Furthermore, a 3D frequency-domain technique is formulated based on the proposed time-domain model to reduce the dimensionality of the model, while preserving the performance in terms of model errors. In the 3D frequency-domain technique, the bandwidth of the system is split into several narrow sub-bands, and the parameters of the model are estimated for each sub-band. This approach requires less computational complexity, and also the procedure of the parameters estimation for each sub-band can be implemented independently. The device-under-test consists of three RF PAs including input and output cross-talk channels. The proposed techniques are evaluated in both behavioral modeling and digital pre-distortion (DPD) perspectives. The experimental results show that the proposed DPD technique can compensate the errors of non-linearity and memory effects in the both time and frequency domains.

  • 11.
    Alizadeh, Mahmoud
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering. University of Gävle.
    Händel, Peter
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Rönnow, Daniel
    University of Gävle.
    Behavioural modelling and digital pre-distortion techniques for RF PAs in a 3x3 MIMO system2018In: International journal of microwave and wireless technologies, ISSN 1759-0795, E-ISSN 1759-0787Article in journal (Refereed)
    Abstract [en]

    Modern telecommunications are moving towards (massive) multi-input multi-output systems in 5th generation (5G) technology, increasing the dimensionality of the system dramatically. In this paper, the impairments of radio frequency (RF)power amplifiers (PAs) in a 3x3 MIMO system are compensated in both time and frequency domains. A three-dimensional(3D) time-domain memory polynomial-type model is proposed as an extension of conventional 2D models. Furthermore, a 3D frequency-domain technique is formulated based on the proposed time-domain model to reduce the dimensionality of the model, while preserving the performance in terms of model errors. In the 3D frequency-domain technique, the bandwidth of a system is split into several narrow sub-bands, and the parameters of the system are estimated for each subband. This approach requires less computational complexity, and also the procedure of the parameters estimation for each sub-band can be implemented independently. The device-under-test (DUT) consists of three RF PAs including input and output cross-talk channels. The proposed techniques are evaluated in both behavioural modelling and digital pre-distortion(DPD) perspectives. The results show that the proposed DPD technique can compensate the errors of non-linearity and memory effects by about 23.5 dB and 7 dB in terms of the normalized mean square error and adjacent channel leakage ratio, respectively.

  • 12.
    Alizadeh, Mahmoud
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering. Department of Electronics, Mathematics and Natural Sciences, University of Gävle (HiG), Gävle, Sweden.
    Rönnow, D.
    Händel, Peter
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Characterization of Volterra Kernels for RF Power Amplifiers Using a Two-Tone Signal and a Large-Signal2018In: 2018 12th International Conference on Communications, COMM 2018 - Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 351-356, article id 8430119Conference paper (Refereed)
    Abstract [en]

    The 3rd-order Volterra kernels of a radio frequency (RF) power amplifier (PA) are characterized using a large-signal and a two-tone probing-signal. In this technique, the magnitude and phase asymmetries of the kernels of the PA excited by the probing-signal are analyzed in different amplitude regions of the large-signal. The device under test is a class-AB PA operating at 2.14 GHz. The maximum sweeping frequency space of the probing-signal is 20 MHz. The results indicate that the Volterra kernels of the PA show different behaviors (frequency dependency and asymmetry) in different regions.

  • 13.
    Alizadeh, Mahmoud
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering. University of Gävle.
    Rönnow, Daniel
    University of Gävle.
    Händel, Peter
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    A new Block-Structure Modeling Technique for RF Power Amplifiers in a 2×2 MIMO System2017In: 2017 13th International Conference on Advanced Technologies, Systems and Services in Telecommunications, TELSIKS 2017 - Proceeding, Nis, Serbia: IEEE, 2017, Vol. 2017, p. 224-227Conference paper (Refereed)
    Abstract [en]

    A new block-structure behavioral model is proposed for radio frequency power amplifiers in a 2×2 multiple-input multiple-output system including input cross-talk. The proposed model forms kernels of blocks of different nonlinear order that correspond to the significant frequency response of measured frequency domain Volterra kernels. The model can therefore well describe the input-output relationships of the nonlinear dynamic behavior of PAs. The proposed model outperforms conventional models in terms of model errors.

  • 14.
    Amini, Mehdi
    et al.
    the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.
    Mostafaei, Shayan
    the Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden.
    Poursamimi, Mohamad
    the Department of Medical Physics, School of Medicine, Semnan University of Medical Sciences, Semnan, Iran.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Mansouri, Zahra
    the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.
    Ghorbani, Mehdi
    the Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of medical sciences, Tehran, Iran.
    Shiri, Isaac
    the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.
    Zaidi, Habib
    the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland; Geneva University Neurocenter, Geneva University, CH-1205 Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, Department of Nuclear Medicine and Molecular Imaging, University of Groningen; University Medical Center Groningen, 9700 RB Groningen, Netherlands; Department of Nuclear Medicine, University of Southern Denmark, DK-500, Odense, Denmark..
    Interpretable PET/CT Radiomic Based Prognosis Modeling of NSCLC Recurrent Following Complete Resection2022In: 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference, Institute of Electrical and Electronics Engineers (IEEE) , 2022Conference paper (Refereed)
    Abstract [en]

    This study aimed to develop an interpretable prognostic model with a nomogram for Non-Small Cell Lung Cancer (NSCLC) recurrence prediction following complete resection, using multi-modality PET/CT fusion radiomics and patients' clinical features. Retrospectively, 181 NSCLC patients who had undergone18F-FDG PET/CT scan were enrolled and split into training (2/3) and testing (1/3) partitions. Before image fusion, PET and CT images were registered, resized to equal isotropic voxel size, and clipped and normalized. Guided Filtering Fusion GFF algorithm was used for image fusion. Two hundred eighteen radiomic features were extracted from each PET, CT, and fused image, including morphological, first-order statistical, and texture features. Clinical features included age, sex, smoking status, weight, radiation, chemotherapy, pathological stage, etc. Feature selection and univariate and multivariate modeling were performed using the CoxBoost algorithm. Harrell's Concordance index (C-index) was used to evaluate the performance of the models, and compare C test was used to statistically compare the performance of the models (p-values < 0.05 were considered significant). Clinical, Clinical+PET, Clinical+CT, and Clinical+GFF resulted in c-indices (confidence interval) of 0.701 (0.589-0.812), 0.757 (0.647-0.867), 0.706 (0.607, 0.807), and 0.824 (0.751-0.896), respectively. Statistical comparison of the performance of different models with the Clinical model revealed that while PET and GFF features can significantly increase the performance (p-values 0.009 and 0.001, respectively), CT features did not significantly improve the performance of the Clinical model (p-value 0.279). Therefore, the nomogram was developed based on the Clinical+GFF model (with the best performance). Radiomic features extracted from PET and PET/CT fusion images can improve the recurrence prognosis in NSCLC patients compared to the conventional clinical factors alone.

  • 15. Ansari, J.
    et al.
    Aktas, I.
    Brecher, C.
    Pallasch, C.
    Hoffmann, N.
    Obdenbusch, M.
    Serror, M.
    Wehrle, K.
    Gross, James
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Demo: A realistic use-case for wireless industrial automation and control2017In: 2017 International Conference on Networked Systems, NetSys 2017, Institute of Electrical and Electronics Engineers (IEEE) , 2017Conference paper (Refereed)
    Abstract [en]

    This demo showcases a typical industrial automation scenario of a robot picking and placing work pieces from a moving conveyor belt. It involves sensory data inputs to a Programmable Logic Controller (PLC), and instructions from the PLC to a robot for the pick and place operation. The scenario requires communication from sensors to the PLC and from the PLC to a robot with ultra-low latency and extremely high reliability. While none of today's wireless standards is capable of satisfying these stringent communication demands, our early prototype implementation of some of the design features of the future 5G standard enables industrial control using wireless communication. Our demo will show the live performance characteristics of the 5G design features for low latency and high reliability.

  • 16.
    Arian, Fatemeh
    et al.
    Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
    Amini, Mehdi
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, CH-1211, Switzerland.
    Mostafaei, Shayan
    Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
    Rezaei Kalantari, Kiara
    Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran; Cardio-Oncology Research Center, Rajaei Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.
    Haddadi Avval, Atlas
    School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
    Shahbazi, Zahra
    Department of Biostatistics, School of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran.
    Kasani, Kianosh
    Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran.
    Bitarafan Rajabi, Ahmad
    Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran; Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran; Echocardiography Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran; Cardiovascular interventional research center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.
    Chatterjee, Saikat
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Oveisi, Mehrdad
    Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, Kings College London, London, UK; Department of Computer Science, University of British Columbia, Vancouver BC, Canada.
    Shiri, Isaac
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, CH-1211, Switzerland.
    Zaidi, Habib
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, CH-1211, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
    Myocardial Function Prediction After Coronary Artery Bypass Grafting Using MRI Radiomic Features and Machine Learning Algorithms2022In: Journal of digital imaging, ISSN 0897-1889, E-ISSN 1618-727X, Vol. 35, no 6, p. 1708-1718Article in journal (Refereed)
    Abstract [en]

    The main aim of the present study was to predict myocardial function improvement in cardiac MR (LGE-CMR) images in patients after coronary artery bypass grafting (CABG) using radiomics and machine learning algorithms. Altogether, 43 patients who had visible scars on short-axis LGE-CMR images and were candidates for CABG surgery were selected and enrolled in this study. MR imaging was performed preoperatively using a 1.5-T MRI scanner. All images were segmented by two expert radiologists (in consensus). Prior to extraction of radiomics features, all MR images were resampled to an isotropic voxel size of 1.8 × 1.8 × 1.8 mm3. Subsequently, intensities were quantized to 64 discretized gray levels and a total of 93 features were extracted. The applied algorithms included a smoothly clipped absolute deviation (SCAD)–penalized support vector machine (SVM) and the recursive partitioning (RP) algorithm as a robust classifier for binary classification in this high-dimensional and non-sparse data. All models were validated with repeated fivefold cross-validation and 10,000 bootstrapping resamples. Ten and seven features were selected with SCAD-penalized SVM and RP algorithm, respectively, for CABG responder/non-responder classification. Considering univariate analysis, the GLSZM gray-level non-uniformity-normalized feature achieved the best performance (AUC: 0.62, 95% CI: 0.53–0.76) with SCAD-penalized SVM. Regarding multivariable modeling, SCAD-penalized SVM obtained an AUC of 0.784 (95% CI: 0.64–0.92), whereas the RP algorithm achieved an AUC of 0.654 (95% CI: 0.50–0.82). In conclusion, different radiomics texture features alone or combined in multivariate analysis using machine learning algorithms provide prognostic information regarding myocardial function in patients after CABG.

  • 17.
    Asif, Rizwan
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Löffel, Hendrik Jan
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Assavasangthong, Vorapol
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Martinelli, Giulio
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Gajland, Phillip
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Theoretical Computer Science, TCS.
    Rodríguez Gálvez, Borja
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Aerial path planning for multi-vehicles2019In: Proceedings - IEEE 2nd International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2019, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 267-272, article id 8791733Conference paper (Refereed)
    Abstract [en]

    Unmanned Aerial Vehicles (UAV) are a potential solution to fast and cost efficient package delivery services. There are two types of UAVs, namely fixed wing (UAV-FW) and rotor wing (UAV-RW), which have their own advantages and drawbacks. In this paper we aim at providing different solutions to a collaborating multi-agent scenario combining both UAVs types. We show the problem can be reduced to the facility location problem (FLP) and propose two local search algorithms to solve it: Tabu search and simulated annealing.

  • 18.
    Avula, Ramana R.
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Chin, Jun-Xing
    Power Systems Laboratory, ETH Zurich, Switzerland.
    Oechtering, Tobias J.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Hug, Gabriela
    Power Systems Laboratory, ETH Zurich, Switzerland.
    Månsson, Daniel
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering.
    Design Framework for Privacy-Aware Demand-Side Management with Realistic Energy Storage Model2021In: IEEE Transactions on Smart Grid, ISSN 1949-3053, E-ISSN 1949-3061, Vol. 12, no 4, p. 3503-3513Article in journal (Refereed)
    Abstract [en]

    Demand-side management (DSM) is a process by which the user demand patterns are modified to meet certain desired objectives. Traditionally, DSM was utility-driven, but with an increase in the integration of renewable sources and privacy-conscious consumers, it also becomes a “consumer-driven" process. Promising theoretical studies have shown that privacy can be achieved by shaping the user demand using an energy storage system (ESS). In this paper, we present a framework for utility-driven DSM while considering the user privacy and the ESS operational cost due to its energy losses and capacity degradation. We propose an ESS model using a circuit-based and data-driven approach that can be used to capture the ESS characteristics in control strategy designs. We measure privacy leakage using the Bayesian risk of a hypothesis testing adversary and present a novel recursive algorithm to compute the optimal privacy control strategy. Further, we design an energy-flow control strategy that achieves the Pareto-optimal trade-off between privacy leakage, deviation of demand from a DSM target profile, and the ESS cost. With numerical experiments using real household data and an emulated lithium-ion battery, we show that the desired level of privacy and demand shaping performance can be achieved while reducing the ESS degradation.

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  • 19.
    Avula, Ramana R.
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Oechtering, Tobias J.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    On design of optimal smart meter privacy control strategy against adversarial MAP detection2020In: Proceedings of the ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain: Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 5845-5849, article id 9054755Conference paper (Refereed)
    Abstract [en]

    We study the optimal control problem of the maximum a posteriori (MAP) state sequence detection of an adversary using smart meter data. The privacy leakage is measured using the Bayesian risk and the privacy-enhancing control is achieved in real-time using an energy storage system. The control strategy is designed to minimize the expected performance of a non-causal adversary at each time instant. With a discrete-state Markov model, we study two detection problems: when the adversary is unaware or aware of the control. We show that the adversary in the former case can be controlled optimally. In the latter case, where the optimal control problem is shown to be non-convex, we propose an adaptive-grid approximation algorithm to obtain a sub-optimal strategy with reduced complexity. Although this work focuses on privacy in smart meters, it can be generalized to other sensor networks. 

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    fulltext
  • 20.
    Avula, Ramana R.
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Oechtering, Tobias J.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Optimal privacy-by-design strategy for user demand shaping in smart grids2020In: Proceedings of the 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies, Institute of Electrical and Electronics Engineers (IEEE) , 2020Conference paper (Refereed)
    Abstract [en]

    In this work, we propose an optimal privacy-by-design strategy using an energy storage system (ESS) that is capable of shaping the user demand to follow a time-varying target profile. In addition, we consider the ESS usage cost due to its energy losses and capacity degradation. We measure the privacy leakage in terms of the Bayesian risk. The proposed strategy is computed by solving a multi-objective optimization problem using the Markov decision process framework. With numerical simulations using real household consumption data and a lithium-ion battery model, we study the trade-off between the achievable Bayesian risk, the variations in the user demand from the target profile and the energy storage cost. The results show that by trading-off some privacy, the variations in the user demand can be reduced while improving the battery lifetime.

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  • 21.
    Avula, Ramana R.
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Oechtering, Tobias J.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Privacy-Enhancing Appliance Filtering For Smart Meters2022In: International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Institute of Electrical and Electronics Engineers (IEEE) , 2022Conference paper (Refereed)
    Abstract [en]

    Non-intrusive load monitoring (NILM) is the process of disaggregating total electricity consumption measured by a smart meter into individual appliances’ contributions. In this paper, we present a privacy control strategy that selectively filters appliances’ consumption from the smart meter measurements to hinder NILM disaggregation performance. The privacy controller uses charging and discharging operations of an energy storage to achieve desired smart meter measurements. We model the household consumption using both additive and difference factorial hidden Markov models and design a control strategy to minimize privacy leakage measured in terms of Bayesian risk due to maximum a posteriori detection. Due to the high computational complexity of the optimal control strategy, we propose a computationally efficient sub-optimal strategy. We evaluate the proposed approaches using the ECO data set and show their privacy improvements against the Viterbi disaggregation algorithm.

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  • 22.
    Avula, Ramana R.
    et al.
    Department of Electrification and Reliability, RISE Research Institutes of Sweden, Sweden.
    Oechtering, Tobias J.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Månsson, Daniel
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering and Fusion Science.
    Adversarial Inference Control in Cyber-Physical Systems: A Bayesian Approach With Application to Smart Meters2024In: IEEE Access, E-ISSN 2169-3536, Vol. 12, p. 24933-24948Article in journal (Refereed)
    Abstract [en]

    With the emergence of cyber-physical systems (CPSs) in utility systems like electricity, water, and gas networks, data collection has become more prevalent. While data collection in these systems has numerous advantages, it also raises concerns about privacy as it can potentially reveal sensitive information about users. To address this issue, we propose a Bayesian approach to control the adversarial inference and mitigate the physical-layer privacy problem in CPSs. Specifically, we develop a control strategy for the worst-case scenario where an adversary has perfect knowledge of the user’s control strategy. For finite state-space problems, we derive the fixed-point Bellman’s equation for an optimal stationary strategy and discuss a few practical approaches to solve it using optimization-based control design. Addressing the computational complexity, we propose a reinforcement learning approach based on the Actor-Critic architecture. To also support smart meter privacy research, we present a publicly accessible “Co-LivEn” dataset with comprehensive electrical measurements of appliances in a co-living household. Using this dataset, we benchmark the proposed reinforcement learning approach. The results demonstrate its effectiveness in reducing privacy leakage. Our work provides valuable insights and practical solutions for managing adversarial inference in cyber-physical systems, with a particular focus on enhancing privacy in smart meter applications.

  • 23.
    Avula, Ramana R.
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Oechtering, Tobias J.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Månsson, Daniel
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering.
    Privacy-preserving smart meter control strategy including energy storage losses2018In: Proceedings - 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2018, Institute of Electrical and Electronics Engineers (IEEE), 2018, article id 8571537Conference paper (Refereed)
    Abstract [en]

    Privacy-preserving smart meter control strategies proposed in the literature so far make some ideal assumptions such as instantaneous control without delay, lossless energy storage systems etc. In this paper, we present a one-step-ahead predictive control strategy using Bayesian risk to measure and control privacy leakage with an energy storage system. The controller estimates energy state using a three-circuit energy storage model to account for steady-state energy losses. With numerical experiments, the controller is evaluated with real household consumption data using a state-of-the-art adversarial algorithm. Results show that the state estimation of the energy storage system significantly affects the controller's performance. The results also show that the privacy leakage can be effectively reduced using an energy storage system but at the expense of energy loss.

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  • 24.
    Avula, Ramana Reddy
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Towards Realistic Smart Meter Privacy against Bayesian Inference2023Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Smart meters, now an essential component of modern power grids, allow energy providers to remotely monitor users' energy consumption in near real-time. While this technology offers numerous advantages for energy management and system efficiency, it also poses significant privacy concerns. High-resolution energy consumption data can reveal sensitive information about users' routines and activities, thus potentially jeopardizing their privacy. In particular, research has demonstrated that Bayesian inference attacks can effectively disaggregate smart meter data to deduce household appliance states and subsequently obtain sensitive user information.

    This thesis investigates the use of energy storage systems to protect smart meter data privacy against Bayesian inference attacks. Although several methods have been proposed in the literature that employ energy storage systems for this purpose, many rely on ideal assumptions such as lossless energy storage systems. To address this issue, a data-driven energy storage model that considers energy losses and capacity degradation has been proposed. Privacy leakage is quantified in terms of Bayesian risk, and control strategies are devised to minimize Bayesian risk while accounting for the energy storage system's operational constraints and economic implications. The findings reveal that non-idealities in energy storage systems significantly affect the privacy-preserving performance of control strategies. Moreover, incorporating degradation losses in the design of privacy-enhancing control strategies considerably improves battery life, albeit with some privacy loss.

    Taking into account the non-idealities of energy storage, this thesis introduces novel privacy-preserving control strategies using various adversarial models, which are classified based on their knowledge of the control system. These models include controller-aware and controller-unaware adversaries employing sequential hypothesis testing or maximum a posteriori detection. The proposed control strategies are evaluated through numerical simulations using real data and emulated energy storage systems. Additionally, the thesis provides a reference dataset of appliance power consumption, featuring detailed electrical measurements to support future smart meter privacy research. In summary, this work offers valuable insights and practical solutions for managing adversarial inference in cyber-physical systems, with potential applications extending to other sensor networks beyond smart meters.

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    Thesis
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    Errata
  • 25.
    Avula, Ramana Reddy
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Oechtering, Tobias J.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Chin, Jun-Xing
    Power Systems Laboratory, ETH Zurich, Switzerland.
    Hug, Gabriela
    Power Systems Laboratory, ETH Zurich, Switzerland.
    Smart Meter Privacy Control Strategy Including Energy Storage Degradation2019In: 2019 IEEE Milan PowerTech, IEEE, 2019Conference paper (Refereed)
    Abstract [en]

    In this paper, we present a degradation-aware privacy control strategy for smart meters by taking into account the capacity fade and energy loss of the battery, which has not been included previously. The energy management strategy is designed by minimizing the weighted sum of both privacy loss and total energy storage losses, where the weightage is set using a trade-off parameter. The privacy loss is measured in terms of Bayesian risk of an unauthorized hypothesis test. By making first-order Markov assumptions, the stochastic parameters of energy loss and capacity fade of the energy storage system are modelled using degradation maps. Using household power consumption data from the ECO dataset, the proposed control strategy is numerically evaluated for different trade-off parameters. Results show that, by including the degradation losses in the design of the privacy-enhancing control strategy, significant improvement in battery life can be achieved, in general, at the expense of some privacy loss.

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  • 26.
    Bao, Jinchen
    et al.
    Southwest Jiaotong Univ, Prov Key Lab Informat Coding & Transmiss, Chengdu 611731, Sichuan, Peoples R China..
    Ma, Zheng
    Southwest Jiaotong Univ, Prov Key Lab Informat Coding & Transmiss, Chengdu 611731, Sichuan, Peoples R China..
    Xiao, Ming
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering. Royal Inst Technol, Dept Commun Theory, S-10044 Stockholm, Sweden..
    Ding, Zhiguo
    Univ Lancaster, Sch Comp & Commun, Lancaster LA1 4YW, England..
    Zhu, Zhongliang
    Southwest Jiaotong Univ, Prov Key Lab Informat Coding & Transmiss, Chengdu 611731, Sichuan, Peoples R China..
    Performance Analysis of Uplink SCMA With Receiver Diversity and Randomly Deployed Users2018In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 67, no 3, p. 2792-2797Article in journal (Refereed)
    Abstract [en]

    This paper considers the performance analysis of sparse code multiple access (SCMA) with receive diversity arrays and randomly deployed users in a cellular uplink scenario. The impact of path loss on the performance of SCMA is characterized, by assuming independent Rayleigh fading and joint maximum likelihood (ML) receivers. A tight upper bound on the probability of symbol detection error is derived, and the achievable diversity and coding gains are investigated. The analytical results are validated by using simulations, and show that a diversity order which is equal to the product of the number of receive antennas and the signal-space diversity can be achieved, and the large-scale path-loss decreases only the coding gain.

  • 27.
    Bao, Jinchen
    et al.
    Southwest Jiaotong Univ, Prov Key Lab Informat Coding & Transmiss, Chengdu 611756, Sichuan, Peoples R China..
    Ma, Zheng
    Southwest Jiaotong Univ, Prov Key Lab Informat Coding & Transmiss, Chengdu 611756, Sichuan, Peoples R China..
    Xiao, Ming
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Tsiftsis, Theodoros A.
    Jinan Univ, Sch Elect & Informat Engn, Zhuhai 519070, Peoples R China..
    Zhu, Zhongliang
    Southwest Jiaotong Univ, Prov Key Lab Informat Coding & Transmiss, Chengdu 611756, Sichuan, Peoples R China..
    Bit-Interleaved Coded SCMA With Iterative Multiuser Detection: Multidimensional Constellations Design2018In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 66, no 11, p. 5292-5304Article in journal (Refereed)
    Abstract [en]

    This paper investigates the constellation/codebook design of a promising uplink multiple access technique, sparse code multiple access (SCMA), proposed for the fifth generation mobile networks. The application of bit-interleaved coded modulation with iterative multiuser detection is considered for uplink SCMA over fading channels. Extrinsic information transfer chart is used to aid the analysis and the design of multidimensional constellations, and the design criteria for multidimensional constellations and labelings optimization are thus established. Furthermore, a new and simple approach of multi-stage optimization for the multidimensional constellation design is proposed for SCMA, to improve the bit-error rate performance and alleviate the complexity of turbo multiuser detection. Numerical and simulation results are also provided to demonstrate the performance and verify the efficiency of the proposed scheme, compared with the state of the art.

  • 28.
    Bao, Yicheng
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Zhou, Linghui
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Oechtering, Tobias J.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Proof-of-Concept of Polar Codes for Biometric Identification and Authentication2022In: 2022 IEEE INTERNATIONAL WORKSHOP ON INFORMATION FORENSICS AND SECURITY (WIFS), Institute of Electrical and Electronics Engineers (IEEE) , 2022Conference paper (Refereed)
    Abstract [en]

    In this work, a complete biometrics identification and authentication system considered in [1] is implemented. In the considered system, polar codes are applied and binary symmetric memoryless channels are used for noisy enrollment and observation. The fundamental limits can be achieved with sufficiently long block length for iid binary source sequence. Fingerprints are used as the biometric source and an autoencoder is designed for pre-processing so that images are compressed to nearly uniformly distributed binary sequences with similar correlation and entropy properties to iid binary sequence. The identification and authentication system with generated secret key in [1] is implemented and simulated using pre-processed fingerprints as biometric source and polar code-based design. The proposed system design approach is systematic and flexible in choosing the optimal trade-off. The results show that identification error rates become smaller with longer code length and when the successive cancellation list algorithm is applied. Thus, it is shown by these first promising experiments that polar codes can be used in real identification and authentication systems.

  • 29.
    Barforooshan, Mohsen
    et al.
    Department of Electronic Systems, Aalborg University, DK-9220, Aalborg, Denmark.
    Derpich, Milan Stefan
    Electronic Engineering Department, Universidad Tecnica Federico Santa Maria, Valparaiso, Chile, 2390123 .
    Stavrou, Fotios
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Ostergaard, Jan
    Department of Electronic Systems, Aalborg University, Aalborg, Denmark.
    The Effect of Time Delay on the Average Data Rate and Performance in Networked Control Systems2022In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 67, no 1, p. 16-31Article in journal (Refereed)
    Abstract [en]

    This paper studies the performance of a feedback control loop closed via an error-free digital communication channel with transmission delay. The system comprises a discrete-time noisy linear time-invariant (LTI) plant whose single measurement output is mapped into its single control input by a causal, but otherwise arbitrary, coding and control scheme. We consider a single-input multiple-output (SIMO) channel between the encoder-controller and the decoder-controller which is lossless and imposes random time delay. We derive a lower bound on the minimum average feedback data rate that guarantees achieving a certain level of average quadratic performance over all possible realizations of the random delay. For the special case of a constant channel delay, we obtain an upper bound by proposing linear source-coding schemes that attain desired performance levels with rates that are at most 1.254 bits per sample greater than the lower bound. We supplement our results with a numerical experiment demonstrating that the obtained bounds and operational rates are increasing functions of the constant delay.

  • 30.
    Barros da Silva Jr., José Mairton
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Ghauch, Hadi
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Fodor, Gabor
    Ericsson Research, Kista, Sweden..
    Skoglund, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Smart Antenna Assignment is Essential in Full-Duplex Communications2021In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 69, no 5, p. 3450-3466Article in journal (Refereed)
    Abstract [en]

    Full-duplex communications have the potential to almost double the spectralefficiency. To realize such a potentiality, the signal separation at base station’s antennasplays an essential role. This paper addresses the fundamentals of such separationby proposing a new smart antenna architecture that allows every antenna to beeither shared or separated between uplink and downlink transmissions. The benefitsof such architecture are investigated by an assignment problem to optimally assignantennas, beamforming and power to maximize the weighted sum spectral efficiency.We propose a near-to-optimal solution using block coordinate descent that divides theproblem into assignment problems, which are NP-hard, a beamforming and powerallocation problems. The optimal solutions for the beamforming and power allocationare established while near-to-optimal solutions to the assignment problems are derivedby semidefinite relaxation. Numerical results indicate that the proposed solution isclose to the optimum, and it maintains a similar performance for high and low residualself-interference powers. With respect to the usually assumed antenna separationtechnique and half-duplex transmission, the sum spectral efficiency gains increase withthe number of antennas. We conclude that our proposed smart antenna assignment forsignal separation is essential to realize the benefits of multiple antenna full-duplexcommunications.

  • 31.
    Bassi, German
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering. Univ Paris Sud, CNRS, Cent Supelec, Lab Signaux & Syst,L2S,UMR 8506, F-91192 Gif Sur Yvette, France.
    Piantanida, Pablo
    Shamai (Shitz), Shlomo
    The Wiretap Channel With Generalized Feedback: Secure Communication and Key Generation2019In: IEEE Transactions on Information Theory, ISSN 0018-9448, E-ISSN 1557-9654, Vol. 65, no 4, p. 2213-2233Article in journal (Refereed)
    Abstract [en]

    It is a well-known fact that feedback does not increase the capacity of point-to-point memoryless channels, however, its effect in secure communications is not fully understood yet. In this paper, an achievable scheme for the wiretap channel with generalized feedback is presented. This scheme, which uses the feedback signal to generate a shared secret key between the legitimate users, encrypts the message to be sent at the bit level. New capacity results for a class of channels are provided, as well as some new insights into the secret key agreement problem. Moreover, this scheme recovers previously reported rate regions from the literature, and thus it can be seen as a generalization that unifies several results in the field.

  • 32.
    Bassi, Germán
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Nekouei, Ehsan
    City University of Hong KongKowloon TongHong Kong.
    Skoglund, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Statistical Parameter Privacy2020In: Privacy in Dynamical Systems / [ed] Farhad Farokhi, Springer Nature, 2020, p. 65-82Chapter in book (Refereed)
    Abstract [en]

    We investigate the problem of sharing the outcomes of a parametric source with an untrusted party while ensuring the privacy of the parameters. We propose privacy mechanisms which guarantee parameter privacy under both Bayesian statistical as well as information-theoretic privacy measures. The properties of the proposed mechanisms are investigated and the utility-privacy trade-off is analyzed.

  • 33.
    Bassi, Germán
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Piantanida, Pablo
    Shamai, Shlomo
    The Secret Key Capacity of a Class of Noisy Channels with Correlated Sources2019In: Entropy, E-ISSN 1099-4300, Vol. 21, no 8, article id 732Article in journal (Refereed)
    Abstract [en]

    This paper investigates the problem of secret key generation over a wiretap channel when the terminals observe correlated sources. These sources are independent of the main channel and the users overhear them before the transmission takes place. A novel outer bound is proposed and, employing a previously reported inner bound, the secret key capacity is derived under certain less-noisy conditions on the channel or source components. This result improves upon the existing literature where the more stringent condition of degradedness is required. Furthermore, numerical evaluation of the achievable scheme and previously reported results for a binary model are presented; a comparison of the numerical bounds provides insights on the benefit of the chosen scheme.

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  • 34.
    Bassi, Germán
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Piantanida, Pablo
    Skoglund, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Lossy Communication Subject to Statistical Parameter Privacy2018In: 2018 IEEE International Symposium on Information Theory (ISIT) - Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 1031-1035, article id 8437690Conference paper (Refereed)
    Abstract [en]

    We investigate the problem of sharing (communi-cating) the outcomes of a memoryless source when some of its statistical parameters must be kept private. Privacy is measured in terms of the Bayesian statistical risk according to a desired loss function while the quality of the reconstruction is measured by the average per-letter distortion. We first bound -uniformly over all possible estimators- the expected risk from below. This information-theoretic bound depends on the mutual information between the parameters and the disclosed (noisy) samples. We then present an achievable scheme that guarantees an upper bound on the average distortion while keeping the risk above a desired threshold, even when the length of the sample increases.

  • 35.
    Bassi, Germán
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Skoglund, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    On the Mutual Information of Two Boolean Functions, with Application to Privacy2019In: Proceedings 2019 IEEE International Symposium on Information Theory (ISIT), IEEE , 2019, p. 1197-1201Conference paper (Refereed)
    Abstract [en]

    We investigate the behavior of the mutual information between two Boolean functions of correlated binary strings. The covariance of these functions is found to be a crucial parameter in the aforementioned mutual information. We then apply this result in the analysis of a specific privacy problem where a user observes a random binary string. Under particular conditions, we characterize the optimal strategy for communicating the outcomes of a function of said string while preventing to leak any information about a different function.

  • 36.
    Bhattacharya, Arani
    et al.
    IIIT Delhi, India.
    Maji, Abhishek
    Hitachi Energy.
    Champati, J. P. V.
    IMDEA Networks Institute.
    Gross, James
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Fast and Efficient Online Selection of Sensors for Transmitter Localization2022In: 2022 14th International Conference on COMmunication Systems and NETworkS, COMSNETS 2022, Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 604-612Conference paper (Refereed)
    Abstract [en]

    The increase in cost and usage of RF spectrum has made it increasingly necessary to monitor its usage and protect it from unauthorized use. A number of prior studies have designed algorithms to localize unauthorized transmitters using crowdsourced sensors. To reduce the cost of crowdsourcing, these studies select the most relevant sensors a priori to localize such transmitters. In this work, we instead argue for online selection to localize such transmitters. Online selection can lead to more accurate localization using limited number of sensors, as compared to selecting sensors a priori, albeit at the cost of higher latency. To account for the trade-off between accuracy and latency, we add a constraint on the number of selection rounds. For the case where the number of rounds is equal to the number of selected sensors, we propose a heuristic based on Thompson Sampling and show using trace-driven simulation that it provides 23 % better accuracy compared to a number of proposed baseline algorithms. For restricted number of rounds, we show that using conventional parallel version of the modified Thompson Sampling which selects equal number of sensors in each round results in a substantial reduction in accuracy. To this end, we propose a strategy of selecting decreasing number of sensors in subsequent rounds of the modified Parallel Thompson Sampling. Our evaluation shows that the proposed heuristic leads to only 3 % reduction in accuracy in contrast to 22 % using modified Parallel Thompson Sampling, when we select 50 sensors in 20 rounds.

  • 37.
    Bhattacharya, Arani
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Zhan, Caitao
    SUNY Stony Brook, Stony Brook, NY 11794 USA..
    Gupta, Himanshu
    SUNY Stony Brook, Stony Brook, NY 11794 USA..
    Das, Samir R.
    SUNY Stony Brook, Stony Brook, NY 11794 USA..
    Djuric, Petar M.
    SUNY Stony Brook, Stony Brook, NY 11794 USA..
    Selection of Sensors for Efficient Transmitter Localization2020In: IEEE INFOCOM 2020 - IEEE conference on computer communications, IEEE , 2020, p. 2410-2419Conference paper (Refereed)
    Abstract [en]

    We address the problem of localizing an (illegal) transmitter using a distributed set of sensors. Our focus is on developing techniques that perform the transmitter localization in an efficient manner, wherein the efficiency is defined in terms of the number of sensors used to localize. Localization of illegal transmitters is an important problem which arises in many important applications, e.g., in patrolling of shared spectrum systems for any unauthorized users. Localization of transmitters is generally done based on observations from a deployed set of sensors with limited resources, thus it is imperative to design techniques that minimize the sensors' energy resources. In this paper, we design greedy approximation algorithms for the optimization problem of selecting a given number of sensors in order to maximize an appropriately defined objective function of localization accuracy. The obvious greedy algorithm delivers a constant-factor approximation only for the special case of two hypotheses (potential locations). For the general case of multiple hypotheses, we design a greedy algorithm based on an appropriate auxiliary objective function-and show that it delivers a provably approximate solution for the general case. We develop techniques to significantly reduce the time complexity of the designed algorithms, by incorporating certain observations and reasonable assumptions. We evaluate our techniques over multiple simulation platforms, including an indoor as well as an outdoor testbed, and demonstrate the effectiveness of our designed techniques-our techniques easily outperform prior and other approaches by up to 50-60% in large-scale simulations.

  • 38. Bhuiyan, M. Z. A.
    et al.
    Miao, W.
    Xiao, Ming
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Message from the IUCC-2020 program chairs ISPA-BDCloud-SocialCom-SustainCom 20202020In: Proceedings - 2020 IEEE International Symposium on Parallel and Distributed Processing with Applications, 2020 IEEE International Conference on Big Data and Cloud Computing, 2020 IEEE International Symposium on Social Computing and Networking and 2020 IEEE International Conference on Sustainable Computing and Communications, ISPA-BDCloud-SocialCom-SustainCom 2020, Institute of Electrical and Electronics Engineers Inc. , 2020, article id 9443700Conference paper (Refereed)
  • 39.
    Borpatra Gohain, Prakash
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering. KTH Royal Institute of Technology, Sweden.
    The Quest for Robust Model Selection Methods in Linear Regression2022Doctoral thesis, monograph (Other academic)
    Abstract [en]

    A fundamental requirement in data analysis is fitting the data to a model that can be used for the purpose of prediction and knowledge discovery. A typical and favored approach is using a linear model that explains the relationship between the response and the independent variables. Linear models are simple, mathematically tractable, and have sound explainable attributes that make them widely ubiquitous in many different fields of applications. Nonetheless, finding the best model (or true model if it exists) is a challenging task that requires meticulous attention. In this PhD thesis, we consider the problem of model selection (MS) in linear regression with a greater focus on the high-dimensional setting when the parameter dimension is quite large compared to the number of available observations. Most of the existing methods of MS struggle in two major areas, viz., consistency and scale-invariance. Consistency refers to the property of the MS method to be able to pick the true model as the sample size grows large or/and when the signal-to-noise-ratio (SNR) increases. Scale-invariance indicates that the performance of the MS method is invariant and stable to any kind of data scaling. These two properties are very crucial for any MS method. In the field of MS employing information criteria, the BayesianInformation Criterion (BIC) is undoubtedly the most popular and widely-used method. However, the new BIC forms including the extended versions designed for the high-SNR scenarios are not invariant to data-scaling and our results indicate that their performance is quite unstable under different scaling scenarios. To eradicate this problem we proposed improved versions of the BIC criterion viz., BICR and EBICR where the subscript ‘R’ stands for robust. BICR is based on the classical setting of order selection, whereas EBICR is the extended version of BICR to handle MS in the high-dimensional setting where it is quite possible that the parameter dimension p also grows with the sample size N. We analyze their performance as N grows large as well as when the noise variance diminishes towards zero, and provide detailed analytical proofs to guarantee their consistency in both cases. Simulation results indicate that the performance of the proposed MS criteria is robust to any data scaling and offers significant improvement in correctly picking the true model. Additionally, we generalize EBICR to handle the problem of MS in block-sparse high-dimensional general linear regression. Block-sparsity is a phenomenon that is seen in many applications. Nevertheless, the existing MS methods based on information criteria are not designed to handle the block structure of the linear model. The proposed generalization handles the block structure effortlessly and can be employed for MS in any type of linear regression framework.

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    Doctoral Thesis
  • 40.
    Borpatra Gohain, Prakash
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Jansson, Magnus
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Model Selection in High-Dimensional Block-Sparse General Linear Regression2023Conference paper (Refereed)
    Abstract [en]

    Model selection is an indispensable part of data analysis dealing very frequently with fitting and prediction purposes. In this paper, we tackle the problem of model selection in a general linear regression where the parameter matrix possesses a block-sparse structure, i.e., the non-zero entries occur in clusters or blocks and the number of such non-zero blocks is very small compared to the parameter dimension. Furthermore, a high-dimensional setting is considered where the parameter dimension is quite large compared to the number of available measurements. To perform model selection in this setting, we present an information criterion that is a generalization of the Extended Bayesian Information Criterion-Robust (EBIC-R) and it takes into account both the block structure and the high-dimensionality scenario. We name it Generalized EBIC-R (GEBIC-R). The analytical steps for deriving the GEBIC-R are provided. Simulation results show that the proposed method performs considerably better than the existing state-of-the-art methods and achieves empirical consistency at large sample sizes and/or at high-SNR.

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  • 41.
    Borpatra Gohain, Prakash
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Jansson, Magnus
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    New Improved Criterion for Model Selection in Sparse High-Dimensional Linear Regression Models2022In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 5692-5696Conference paper (Refereed)
    Abstract [en]

    Extended Bayesian information criterion (EBIC) and extended Fisher information criterion (EFIC) are two popular criteria for model selection in sparse high-dimensional linear regression models. However, EBIC is inconsistent in scenarios when the signal-to-noise-ratio (SNR) is high but the sample size is small, and EFIC is not invariant to data scaling, which affects its performance under different signal and noise statistics. In this paper, we present a refined criterion called EBIC R where the ‘R’ stands for robust. EBIC R is an improved version of EBIC and EFIC. It is scale-invariant and a consistent estimator of the true model as the sample size grows large and/or when the SNR tends to infinity. The performance of EBIC R is compared to existing methods such as EBIC, EFIC and multi-beta-test (MBT). Simulation results indicate that the performance of EBIC R in identifying the true model is either at par or superior to that of the other considered methods.

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  • 42.
    Borpatra Gohain, Prakash
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Jansson, Magnus
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Relative cost based model selection for sparse high-dimensional linear regression models2020In: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2020, p. 5515-5519Conference paper (Refereed)
    Abstract [en]

    In this paper, we propose a novel model selection method named multi-beta-test (MBT) for the sparse high-dimensional linear regression model. The estimation of the correct subset in the linear regression problem is formulated as a series of hypothesis tests where the test statistic is based on the relative least-squares cost of successive parameter models. The performance of MBT is compared to existing model selection methods for high-dimensional parameter space such as extended Bayesian information criterion (EBIC), extended Fisher Information criterion (EFIC), residual ratio thresholding (RRT) and orthogonal matching pursuit (OMP) with a priori knowledge of the sparsity. Simulation results indicate that the performance of MBT in identifying the true support set surpasses that of EBIC, EFIC and RRT in certain regions of the considered parameter settings.

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  • 43.
    Borpatra Gohain, Prakash
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Jansson, Magnus
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Robust Information Criterion for Model Selection in Sparse High-Dimensional Linear Regression Models2023In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 71, p. 2251-2266Article in journal (Refereed)
    Abstract [en]

    Model selection in linear regression models is a major challenge when dealing with high-dimensional data where the number of available measurements (sample size) is much smaller than the dimension of the parameter space. Traditional methods for model selection such as Akaike information criterion, Bayesian information criterion (BIC), and minimum description length are heavily prone to overfitting in the high-dimensional setting. In this regard, extended BIC (EBIC), which is an extended version of the original BIC, and extended Fisher information criterion (EFIC), which is a combination of EBIC and Fisher information criterion, are consistent estimators of the true model as the number of measurements grows very large. However, EBIC is not consistent in high signal-to-noise-ratio (SNR) scenarios where the sample size is fixed and EFIC is not invariant to data scaling resulting in unstable behaviour. In this article, we propose a new form of the EBIC criterion called EBIC-Robust, which is invariant to data scaling and consistent in both large sample sizes and high-SNR scenarios. Analytical proofs are presented to guarantee its consistency. Simulation results indicate that the performance of EBIC-Robust is quite superior to that of both EBIC and EFIC.

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  • 44.
    Borpatra Gohain, Prakash
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Jansson, Magnus
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Scale-Invariant and consistent Bayesian information criterion for order selection in linear regression models2022In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 196, article id 108499Article in journal (Refereed)
    Abstract [en]

    The Bayesian information criterion (BIC) is one of the most well-known criterion used for model order estimation in linear regression models. However, in its popular form, BIC is inconsistent as the noise variance tends to zero given that the sample size is small and fixed. Several modifications of the original BIC have been proposed that takes into account the high-SNR consistency, but it has been recently observed that the performance of the high-SNR forms of BIC highly depends on the scaling of the data. This scaling problem is a byproduct of the data dependent penalty design, which generates irregular penalties when the data is scaled and often leads to greater underfitting or overfitting losses in some scenarios when the noise variance is too small or large. In this paper, we present a new form of the BIC for order selection in linear regression models where the parameter vector dimension is small compared to the sample size. The proposed criterion eliminates the scaling problem and at the same time is consistent for both large sample sizes and high-SNR scenarios.

  • 45. Calvo-Palomino, R.
    et al.
    Bhattacharya, Arani
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Bovet, G.
    Giustiniano, D.
    Short: LSTM-based GNSS Spoofing Detection Using Low-cost Spectrum Sensors2020In: Proceedings - 21st IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2020, Institute of Electrical and Electronics Engineers Inc. , 2020, p. 273-276Conference paper (Refereed)
    Abstract [en]

    GNSS/GPS is a positioning system widely used nowadays in our lives for real-time localization in Earth. This technology is highly vulnerable to spoofing/jamming attacks caused by malicious intruders. In the recent years, commodity and low-cost radio-frequency hardware have been used to interfere with the legitimate GPS signal. Existing spoofing detection solutions use costly receivers and computationally expensive algorithms which limit the large-scale deployment. In this work we propose a GNSS spoofing detection system that can run on spectrum sensors with Software-Defined Radio (SDR) capabilities and cost in the order of 20 euros. Our approach exploits the predictability of the Doppler characteristics of the received GPS signals to determine the presence of anomalies or malicious attackers. We propose an artificial recurrent neural network (RNN) based on Long short-term memory (LSTM) for anomaly detection. We use data received by low-cost SDR receivers that are processed locally by low-cost embedded machines such as Nvidia Jetson Nano to provide inference capabilities. We show that our solution predicts very accurately the Doppler shift of GNSS signals and can determine the presence of a spoofing transmitter.

  • 46.
    Cao, Le Phuong
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Optimal Transmit Strategies for Multi-antenna Systems with Joint Sum and Per-antenna Power Constraints2019Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Nowadays, wireless communications have become an essential part of our daily life. During the last decade, both the number of users and their demands for wireless data have tremendously increased. Multi-antenna communication is a promising solution to meet this ever-growing traffic demands. In this dissertation, we study the optimal transmit strategies for multi-antenna systems with advanced power constraints, in particular joint sum and per-antenna power constraints. We focus on three different models including multi-antenna point-to-point channels, wiretap channels and massive multiple-input multiple-output (MIMO) setups. The solutions are provided either in closed-form or efficient iterative algorithms, which are ready to be implemented in practical systems.

    The first part is concerned with the optimal transmit strategies for point-to-point multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) channels with joint sum and per-antenna power constraints. For the Gaussian MISO channels, a closed-form characterization of an optimal beamforming strategy is derived. It is shown that we can always find an optimal beamforming transmit strategy that allocates the maximal sum power with phases matched to the complex channel coefficients. An interesting property of the optimal power allocation is that whenever the optimal power allocation of the corresponding problem with sum power constraint only exceeds per-antenna power constraints, it is optimal to allocate maximal per-antenna power to those antennas to satisfy the per-antenna power constraints. The remaining power is distributed among the other antennas whose optimal allocation follows from a reduced joint sum and per-antenna power constraints problem with fewer channel coefficients and a reduced sum power constraint. For the Gaussian MIMO channels, it is shown that if an unconstraint optimal power allocation for an antenna exceeds a per-antenna power constraint, then the maximal power for this antenna is used in the constraint optimal transmit strategy. This observation is then used in an iterative algorithm to compute the optimal transmit strategy in closed-form.

    In the second part of the thesis, we investigate the optimal transmit strategies for Gaussian MISO wiretap channels. Motivated by the fact that the non-secure capacity of the MISO wiretap channels is usually larger than the secrecy capacity, we study the optimal trade-off between those two rates with different power constraint settings, in particular, sum power constraint only, per-antenna power constraints only, and joint sum and per-antenna power constraints. To characterize the boundary of the optimal rate region, which describes the optimal trade-off between non-secure transmission and secrecy rates, related problems to find optimal transmit strategies that maximize the weighted rate sum with different power constraints are derived. Since these problems are not necessarily convex, equivalent problem formulation is used to derive optimal transmit strategies. A closed-formsolution is provided for sum power constraint only problem. Under per-antenna power constraints, necessary conditions to find the optimal power allocation are provided. Sufficient conditions, however, are available for the case of two transmit antennas only. For the special case of parallel channels, the optimal transmit strategies can deduced from an equivalent point-to-point channel problem. In this case, there is no trade-off between secrecy and non-secrecy rate, i.e., there is onlya transmit strategy that maximizes both rates.

    Finally, the optimal transmit strategies for large-scale MISO and massive MIMO systems with sub-connected hybrid analog-digital beamforming architecture, RF chain and per-antenna power constraints are studied. The system is configured such that each RF chain serves a group of antennas. For the large-scale MISO system, necessary and sufficient conditions to design the optimal digital and analog precoders are provided. It is optimal that the phase at each antenna is matched tothe channel so that we have constructive alignment. Unfortunately, for the massive MIMO system, only necessary conditions are provided. The necessary conditions to design the digital precoder are established based on a generalized water-filling and joint sum and per-antenna optimal power allocation solution, while the analog precoder is based on a per-antenna power allocation solution only. Further, we provide the optimal power allocation for sub-connected setups based on two properties: (i) Each RF chain uses full power and (ii) if the optimal power allocation of the unconstraint problem violates a per-antenna power constraint then it is optimal to allocate the maximal power for that antenna. The results in the dissertation demonstrate that future wireless networks can achieved higher data rates with less power consumption. The designs of optimal transmit strategies provided in this dissertation are valuable for ongoing implementations in future wireless networks. The insights offered through the analysis and design of the optimal transmit strategies in the dissertation also provide the understanding of the optimal power allocation on practical multi-antenna systems.

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  • 47.
    Cao, Le Phuong
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Oechtering, Tobias J.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Skoglund, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Transmit Beamforming for Single-User Large-Scale MISO Systems With Sub-Connected Architecture and Power Constraints2018In: IEEE Communications Letters, ISSN 1089-7798, E-ISSN 1558-2558, Vol. 22, no 10, p. 2096-2099Article in journal (Refereed)
    Abstract [en]

    This letter considers optimal transmit beamforming for a sub-connected large-scale MISO system with RF chain and per-antenna power constraints. The system is configured such that each RF chain serves a group of antennas. For the hybrid scheme, necessary and sufficient conditions to design the optimal digital and analog precoders are provided. It is shown that, in the optimum, the optimal phase shift at each antenna has to match the channel coefficient and the phase of the digital precoder. In addition, an iterative algorithm is provided to find the optimal power allocation. We study the case where the power constraint on each RF chain is smaller than the sum of the corresponding per-antenna power constraints. Then, the optimal power is allocated based on two properties: each RF chain uses full power and if the optimal power allocation of the unconstraint problem violates a per-antenna power constraint then it is optimal to allocate the maximal power for that antenna.

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  • 48.
    Cao, Phuong
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Oechtering, Tobias J.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Optimal Transmit Strategies for Gaussian MISO Wiretap Channels2018In: IEEE Transactions on Information Forensics and Security, ISSN 1556-6013, E-ISSN 1556-6021Article in journal (Other academic)
    Abstract [en]

    This paper studies the optimal tradeoff between secrecy and non-secrecy rates of the MISO wiretap channels for different power constraint settings:sum power constraint only, per-antenna power constraints only and joint sum and per-antenna power constraints. The problem is motivated by the fact thatchannel capacity and secrecy capacity are generally achieved by different transmit strategies. First, a necessary and sufficient condition to ensure a positive secrecy capacity is shown. The optimal tradeoff between secrecy rate and transmission rate is characterized by a weighted rate sum maximization problem. Since this problem is not necessarily convex, equivalent problem formulations are introduced to derive the optimal transmit strategies. Under sum power constraint only, a closed-form solution is provided. Under per-antenna power constraints, necessary conditions to find the optimal power allocation are provided. Sufficient conditions are provided for the special case of two transmit antennas. For the special case of parallel channels, the optimal transmit strategies can deduced from an equivalent point-to-point channel problem. Lastly, the theoretical results are illustrated by numerical simulations.

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  • 49.
    Cao, Phuong
    et al.
    Ericsson AB, Kista, Sweden.
    Oechtering, Tobias J.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Optimal Transmit Strategies for Gaussian MISO Wiretap Channels2020In: IEEE Transactions on Information Forensics and Security, ISSN 1556-6013, E-ISSN 1556-6021, Vol. 15, p. 829-838Article in journal (Refereed)
    Abstract [en]

    This paper studies the optimal tradeoff between secrecy and non-secrecy rates of the MISO wiretap channels for different power constraint settings: sum power constraint only, per-antenna power constraints only, and joint sum and per-antenna power constraints. The problem is motivated by the fact that channel capacity and secrecy capacity are generally achieved by different transmit strategies. First, a necessary and sufficient condition to ensure a positive secrecy capacity is shown. The optimal tradeoff between secrecy rate and transmission rate is characterized by a weighted rate sum maximization problem. Since this problem is not necessarily convex, equivalent problem formulations are introduced to derive the optimal transmit strategies. Under sum power constraint only, a closed-form solution is provided. Under per-antenna power constraints, necessary conditions to find the optimal power allocation are derived. Sufficient conditions are provided for the special case of two transmit antennas. For the special case of aligned channels, the optimal transmit strategies can deduced from an equivalent point-to-point channel problem. Last, the theoretical results are illustrated by numerical simulations.

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  • 50.
    Carlsson, Håkan
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Inertial Sensor Arrays: Sensor Fusion and Calibration2022Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Motion estimation using inertial sensors is today used in a wide range of applications, from aircraft navigation to inflatable bicycle helmets. The accuracy with which the motion can be estimated using inertial sensors depends on how large the measurement errors are. One approach to reducing the inertial sensors' measurement errors is to use more sensors than what is necessary for motion estimation. By averaging the measurements from a redundant amount of sensors, the impact of independent errors can be reduced. But by placing multiple inertial sensors on a rigid body, more information about the motion is available than what can be obtained from simple averaging. For instance, point-wise accelerations of a rigid body contain information on the rotation of the rigid body. This thesis examines and proposes methods for how to fuse the measurements from an inertial sensor array and how systematic measurement errors present in the sensors can be estimated and calibrated.

    The inertial sensor array contains multiple accelerometers and multiple gyroscopes. In motion estimation applications, it is common to estimate the angular velocity from the gyroscopes measurements and then integrate the angular velocity into an orientation. The angular velocity can also be estimated from multiple accelerometers. This thesis proposes different models for fusing the accelerometer and gyroscope measurements for more accurate orientation estimation. By increasing the accuracy with which the orientation can be estimated, the integrated error in the position and velocity estimates can be decreased.

    The performance of the fusion algorithms for multiple inertial sensors depends on how large the systematic measurement errors are. The amount of rotational information from multiple accelerometers depends on how well the locations of the accelerometers are known. Other calibration parameters in the inertial sensor array are sensor biases. These calibration parameters are estimated in conventional calibration by exposing the inertial sensors to a known reference motion. However, creating such reference motion requires external equipment that may not be available to the user. Therefore, this thesis proposes methods to jointly estimate the motion and the sensor parameters, thereby omitting the need for external calibration equipment.

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