Change search
Refine search result
1 - 16 of 16
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Ghauch, Hadi
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Kim, Taejoon
    Bengtsson, Mats
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Skoglund, Mikael
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Subspace Estimation and Decomposition for Large Millimeter-Wave MIMO Systems2016In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 10, no 3, p. 528-542Article in journal (Refereed)
    Abstract [en]

    Channel estimation and precoding in hybrid analog-digital millimeter-wave (mmWave) MIMO systems is a fundamental problem that has yet to be addressed, before any of the promised gains can be harnessed. For that matter, we propose a method (based on the well-known Arnoldi iteration) exploiting channel reciprocity in TDD systems and the sparsity of the channel's eigenmodes, to estimate the right (resp. left) singular subspaces of the channel, at the BS (resp. MS). We first describe the algorithm in the context of conventional MIMO systems, and derive bounds on the estimation error in the presence of distortions at both BS and MS. We later identify obstacles that hinder the application of such an algorithm to the hybrid analog-digital architecture, and address them individually. In view of fulfilling the constraints imposed by the hybrid analog-digital architecture, we further propose an iterative algorithm for subspace decomposition, whereby the above estimated subspaces, are approximated by a cascade of analog and digital precoder/combiner. Finally, we evaluate the performance of our scheme against the perfect CSI, fully digital case (i.e., an equivalent conventional MIMO system), and conclude that similar performance can be achieved, especially at medium-to-high SNR (where the performance gap is less than 5%), however, with a drastically lower number of RF chains (similar to 4 to 8 times less).

  • 2. Kalantari, A.
    et al.
    Soltanalian, M.
    Maleki, S.
    Chatzinotas, S.
    Ottersten, Björn
    Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg.
    Directional Modulation Via Symbol-Level Precoding: A Way to Enhance Security2016In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 10, no 8, p. 1478-1493Article in journal (Refereed)
    Abstract [en]

    Wireless communication provides a wide coverage at the cost of exposing information to unintended users. As an information-theoretic paradigm, secrecy rate derives bounds for secure transmission when the channel to the eavesdropper is known. However, such bounds are shown to be restrictive in practice and may require exploitation of specialized coding schemes. In this paper, we employ the concept of directional modulation and follow a signal processing approach to enhance the security of multiuser multi-input multioutput (MIMO) communication systems when a multiantenna eavesdropper is present. Security enhancement is accomplished by increasing the symbol error rate at the eavesdropper. Unlike the information-theoretic secrecy rate paradigm, we assume that the legitimate transmitter is not aware of its channel to the eavesdropper, which is a more realistic assumption. We examine the applicability of MIMO receiving algorithms at the eavesdropper. Using the channel knowledge and the intended symbols for the users, we design security enhancing symbol-level precoders for different transmitter and eavesdropper antenna configurations. We transform each design problem to a linearly constrained quadratic program and propose two solutions, namely the iterative algorithm and one based on nonnegative least squares, at each scenario for a computationally efficient modulation. Simulation results verify the analysis and show that the designed precoders outperform the benchmark scheme in terms of both power efficiency and security enhancement.

  • 3. Kammoun, Abla
    et al.
    Mueller, Axel
    Björnson, Emil
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. Alcatel-Lucent Department of Flexible Radio, France.
    Debbah, Merouane
    Linear Precoding Based on Polynomial Expansion: Large-Scale Multi-Cell MIMO Systems2014In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 8, no 5, p. 861-875Article in journal (Refereed)
    Abstract [en]

    Large-scale MIMO systems can yield a substantial improvements in spectral efficiency for future communication systems. Due to the finer spatial resolution and array gain achieved by a massive number of antennas at the base station, these systems have shown to be robust to inter-user interference and the use of linear precoding appears to be asymptotically optimal. However, from a practical point of view, most precoding schemes exhibit prohibitively high computational complexity as the system dimensions increase. For example, the near-optimal regularized zero forcing (RZF) precoding requires the inversion of a large matrix. To solve this issue, we propose in this paper to approximate the matrix inverse by a truncated polynomial expansion (TPE), where the polynomial coefficients are optimized to maximize the system performance. This technique has been recently applied in single cell scenarios and it was shown that a small number of coefficients is sufficient to reach performance similar to that of RZF, while it was not possible to surpass RZF. In a realistic multi-cell scenario involving large-scale multi-user MIMO systems, the optimization of RZF precoding has, thus far, not been feasible. This is mainly attributed to the high complexity of the scenario and the non-linear impact of the necessary regularizing parameters. On the other hand, the scalar coefficients in TPE precoding give hope for possible throughput optimization. To this end, we exploit random matrix theory to derive a deterministic expression of the asymptotic signal-to-interference-and-noise ratio for each user based on channel statistics. We also provide an optimization algorithm to approximate the coefficients that maximize the network-wide weighted max-min fairness. The optimization weights can be used to mimic the user throughput distribution of RZF precoding. Using simulations, we compare the network throughput of the proposed TPE precoding with that of the suboptimal RZF scheme and show that our scheme can achieve higher throughput using a TPE order of only 5.

  • 4.
    Li, Zuxing
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Oechtering, Tobias
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Privacy-Aware Distributed Bayesian Detection2015In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 9, no 7, p. 1345-1357Article in journal (Refereed)
    Abstract [en]

    We study the eavesdropping problem in the remotely distributed sensing of a privacy-sensible hypothesis from the Bayesian detection perspective. We consider a parallel distributed detection network where remote decision makers independently make local decisions defined on finite domains and forward them to the fusion center which makes the final decision. An eavesdropper is assumed to intercept a specific set of local decisions to make also a guess on the hypothesis. We propose a novel Bayesian detection-operational privacy metric given by the minimal achievable Bayesian risk of the eavesdropper. Further, we introduce two privacy-aware distributed Bayesian detection formulations, namely the privacy-constrained distributed Bayesian detection problem and the privacy-concerned distributed Bayesian detection problem where the detection performance is optimized under a privacy guarantee constraint and a weighted sum objective of the detection performance and privacy risk is minimized respectively. For an optimal privacy-aware distributed Bayesian detection design, the optimal decision strategy of employing a deterministic likelihood test or a randomized strategy thereof is identified. Further, it is shown that equivalent problems of different formulations always exist and lead to the same optimal privacy-aware distributed Bayesian detection design. The results are illustrated and discussed by numerical examples. The idea of privacy-aware distributed Bayesian detection design provides a novel solution to realize future trustworthy Internet of Things applications.

  • 5.
    Magnusson, Sindri
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Enyioha, Chinwendu
    Harvard Univ, Sch Engn & Appl Sci, Cambridge, MA 02138 USA..
    Li, Na
    Harvard Univ, Sch Engn & Appl Sci, Cambridge, MA 02138 USA..
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Tarokh, Vahid
    Duke Univ, Elect & Comp Engn, Durham, NC 27708 USA..
    Communication Complexity of Dual Decomposition Methods for Distributed Resource Allocation Optimization2018In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 12, no 4, p. 717-732Article in journal (Refereed)
    Abstract [en]

    Dual decomposition methods are among the most prominent approaches for finding primal/dual saddle point solutions of resource allocation optimization problems. To deploy these methods in the emerging Internet of things networks, which will often have limited data rates, it is important to understand the communication overhead they require. Motivated by this, we introduce and explore twomeasures of communication complexity of dual decomposition methods to identify the most efficient communication among these algorithms. The first measure is epsilon-complexity, which quantifies the minimal number of bits needed to find an epsilon-accurate solution. The second measure is b-complexity, which quantifies the best possible solution accuracy that can be achieved from communicating b bits. We find the exact epsilon -and b-complexity of a class of resource allocation problems where a single supplier allocates resources to multiple users. For both the primal and dual problems, the epsilon-complexity grows proportionally to log(2) (1/epsilon) and the b-complexity proportionally to 1/2(b). We also introduce a variant of the epsilon- and b-complexity measures where only algorithms that ensure primal feasibility of the iterates are allowed. Such algorithms are often desirable because overuse of the resources can overload the respective systems, e.g., by causing blackouts in power systems. We provide upper and lower bounds on the convergence rate of these primal feasible complexity measures. In particular, we show that the b-complexity cannot converge at a faster rate than O(1/b). Therefore, the results demonstrate a tradeoff between fast convergence and primal feasibility. We illustrate the result by numerical studies.

  • 6.
    Mai, Vien V.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. Dankook Univ, South Korea.
    Shin, Won-Yong
    Ishibashi, Koji
    Wireless Power Transfer for Distributed Estimation in Sensor Networks2017In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 11, no 3, p. 549-562Article in journal (Refereed)
    Abstract [en]

    This paper studies power allocation for distributed estimation of an unknown scalar random source in sensor networks with a multiple-antenna fusion center (FC), where wireless sensors are equipped with radio-frequency-based energy harvesting technology. The sensors' observation is locally processed by using an uncoded amplify-and-forward scheme. The processed signals are then sent to the FC, and are coherently combined at the FC, at which the best linear unbiased estimator (BLUE) is adopted for reliable estimation. We aim to solve the following two power allocation problems: 1) minimizing distortion under various power constraints; and 2) minimizing total transmit power under distortion constraints, where the distortion is measured in terms of mean-squared error of the BLUE. Two iterative algorithms are developed to solve the nonconvex problems, which converge at least to a local optimum. In particular, the above algorithms are designed to jointly optimize the amplification coefficients, energy beamforming, and receive filtering. For each problem, a suboptimal design, a single-antenna FC scenario, and a common harvester deployment for collocated sensors, are also studied. Using the powerful semidefinite relaxation framework, our result is shown to be valid for any number of sensors, each with different noise power, and for an arbitrarily number of antennas at the FC.

  • 7.
    Maurer, Johannes
    et al.
    Technical University of Vienna.
    Jaldén, Joakim
    Technical University of Vienna.
    Seethaler, Dominik
    Communication Technology Lab, Zurich.
    Matz, Gerald
    Technical University of Vienna.
    Achieving a Continuous Diversity-Complexity Tradeoff in Wireless MIMO Systems via Pre-Equalized Sphere-Decoding2009In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 3, no 6, p. 986-999Article in journal (Refereed)
  • 8. Mohammed, Saif K.
    et al.
    Zaki, Ahmed
    Chockalingam, A.
    Rajan, B. Sundar
    High-Rate Space-Time Coded Large-MIMO Systems: Low-Complexity Detection and Channel Estimation2009In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 3, no 6, p. 958-974Article in journal (Refereed)
    Abstract [en]

    In this paper, we present a low-complexity algorithm for detection in high-rate, non-orthogonal space-time block coded (STBC) large-multiple-input multiple-output (MIMO) systems that achieve high spectral efficiencies of the order of tens of bps/Hz. We also present a training-based iterative detection/channel estimation scheme for such large STBC MIMO systems. Our simulation results show that excellent bit error rate and nearness-to-capacity performance are achieved by the proposed multistage likelihood ascent search (M-LAS) detector in conjunction with the proposed iterative detection/channel estimation scheme at low complexities. The fact that we could show such good results for large STBCs like 16 X 16 and 32 X 32 STBCs from Cyclic Division Algebras (CDA) operating at spectral efficiencies in excess of 20 bps/Hz (even after accounting for the overheads meant for pilot based training for channel estimation and turbo coding) establishes the effectiveness of the proposed detector and channel estimator. We decode perfect codes of large dimensions using the proposed detector. With the feasibility of such a low-complexity detection/channel estimation scheme, large-MIMO systems with tens of antennas operating at several tens of bps/Hz spectral efficiencies can become practical, enabling interesting high data rate wireless applications.

  • 9. Mueller, Ralf R.
    et al.
    Cottatellucci, Laura
    Vehkaperä, Mikko
    KTH, School of Electrical Engineering (EES), Communication Theory. Aalto University, Finland .
    Blind Pilot Decontamination2014In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 8, no 5, p. 773-786Article in journal (Refereed)
    Abstract [en]

    A subspace projection to improve channel estimation in massive multi-antenna systems is proposed and analyzed. Together with power-controlled hand-off, it can mitigate the pilot contamination problem without the need for coordination among cells. The proposed method is blind in the sense that it does not require pilot data to find the appropriate subspace. It is based on the theory of large random matrices that predicts that the eigenvalue spectra of large sample covariance matrices can asymptotically decompose into disjoint bulks as the matrix size grows large. Random matrix and free probability theory are utilized to predict under which system parameters such a bulk decomposition takes place. Simulation results are provided to confirm that the proposed method outperforms conventional linear channel estimation if bulk separation occurs.

  • 10.
    Rana, Pravin Kumar
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Taghia, Jalil
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Ma, Zhanyu
    Flierl, Markus
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Probabilistic Multiview Depth Image Enhancement Using Variational Inference2015In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 9, no 3, p. 435-448Article in journal (Refereed)
    Abstract [en]

    An inference-based multiview depth image enhancement algorithm is introduced and investigated in this paper. Multiview depth imagery plays a pivotal role in free-viewpoint television. This technology requires high-quality virtual view synthesis to enable viewers to move freely in a dynamic real world scene. Depth imagery of different viewpoints is used to synthesize an arbitrary number of novel views. Usually, the depth imagery is estimated individually by stereo-matching algorithms and, hence, shows inter-view inconsistency. This inconsistency affects the quality of view synthesis negatively. This paper enhances the multiview depth imagery at multiple viewpoints by probabilistic weighting of each depth pixel. First, our approach classifies the color pixels in the multiview color imagery. Second, using the resulting color clusters, we classify the corresponding depth values in the multiview depth imagery. Each clustered depth image is subject to further subclustering. Clustering based on generative models is used for assigning probabilistic weights to each depth pixel. Finally, these probabilistic weights are used to enhance the depth imagery at multiple viewpoints. Experiments show that our approach consistently improves the quality of virtual views by 0.2 dB to 1.6 dB, depending on the quality of the input multiview depth imagery.

  • 11. Sadanandan, Sajith Kecheril
    et al.
    Baltekin, Ozden
    Magnusson, Klas E. G.
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Signal Processing.
    Boucharin, Alexis
    Ranefall, Petter
    Jalden, Joakim
    Elf, Johan
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Signal Processing.
    Wahlby, Carolina
    Segmentation and Track-Analysis in Time-Lapse Imaging of Bacteria2016In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 10, no 1, p. 174-184Article in journal (Refereed)
    Abstract [en]

    In this paper, we have developed tools to analyze prokaryotic cells growing in monolayers in a microfluidic device. Individual bacterial cells are identified using a novel curvature based approach and tracked over time for several generations. The resulting tracks are thereafter assessed and filtered based on track quality for subsequent analysis of bacterial growth rates. The proposed method performs comparable to the state-of-the-art methods for segmenting phase contrast and fluorescent images, and we show a 10-fold increase in analysis speed.

  • 12.
    Shariati, Nafiseh
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Björnson, Emil
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. Alcatel-Lucent Chair on Flexible Radio, Supélec, Gif-sur-Yvette, France; Department of Electrical Engineering (ISY), Linköping University, Linköping, Sweden.
    Bengtsson, Mats
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Debbah, Merouane
    Alcatel-Lucent Chair on Flexible Radio, SUPELEC, Gif-sur-Yvette, France.
    Low-Complexity Polynomial Channel Estimation in Large-Scale MIMO with Arbitrary Statistics2014In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 8, no 5, p. 815-830Article in journal (Refereed)
    Abstract [en]

    This paper considers pilot-based channel estimation in large-scale multiple-input multiple-output (MIMO) communication systems, also known as “massive MIMO”, where there are hundreds of antennas at one side of the link. Motivated by the fact that computational complexity is one of the main challenges in such systems, a set of low-complexity Bayesian channel estimators, coined Polynomial ExpAnsion CHannel (PEACH) estimators, are introduced for arbitrary channel and interference statistics. While the conventional minimum mean square error (MMSE) estimator has cubic complexity in the dimension of the covariance matrices, due to an inversion operation, our proposed estimators significantly reduce this to square complexity by approximating the inverse by a L-degree matrix polynomial. The coefficients of the polynomial are optimized to minimize the mean square error (MSE) of the estimate. We show numerically that near-optimal MSEs are achieved with low polynomial degrees. We also derive the exact computational complexity of the proposed estimators, in terms of the floating-point operations (FLOPs), by which we prove that the proposed estimators outperform the conventional estimators in large-scale MIMO systems of practical dimensions while providing a reasonable MSEs. Moreover, we show that L needs not scale with the system dimensions to maintain a certain normalized MSE. By analyzing different interference scenarios, we observe that the relative MSE loss of using the low-complexity PEACH estimators is smaller in realistic scenarios with pilot contamination. On the other hand, PEACH estimators are not well suited for noise-limited scenarios with high pilot power; therefore, we also introduce the low-complexity diagonalized estimator that performs well in this regime. Finally, we also investigate numerically how the estimation performance is affected by having imperfect statistical knowledge. High robustness is achieved for large-dimensional matrices by us- ng a new covariance estimate which is an affine function of the sample covariance matrix and a regularization term.

  • 13.
    Stavrou, Photios A.
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Ostergaard, Jan
    Aalborg Univ, Dept Elect Syst, DK-9220 Aalborg, Denmark..
    Charalambous, Charalambos D.
    Univ Cyprus, Dept Elect & Comp Engn, CY-3060 Nicosia, Cyprus..
    Zero-Delay Rate Distortion via Filtering for Vector-Valued Gaussian Sources2018In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 12, no 5, p. 841-856Article in journal (Refereed)
    Abstract [en]

    We deal with zero-delay source coding of a vector-valued Gauss-Markov source subject to a mean-squared error (MSE) fidelity criterion characterized by the operational zero-delay vector-valued Gaussian rate distortion function (RDF). We address this problem by considering the nonanticipative RDF (NRDF), which is a lower bound to the causal optimal performance theoretically attainable function (or simply causal RDF) and operational zero-delay RDF. We recall the realization that corresponds to the optimal "test-channel" of the Gaussian NRDF, when considering a vector Gauss-Markov source subject to a MSE distortion in the finite time horizon. Then, we introduce sufficient conditions to show existence of solution for this problem in the infinite time horizon (or asymptotic regime). For the asymptotic regime, we use the asymptotic characterization of the Gaussian NRDF to provide a new equivalent realization scheme with feedback, which is characterized by a resource allocation (reverse-waterfilling) problem across the dimension of the vector source. We leverage the new realization to derive a predictive coding scheme via lattice quantization with subtractive dither and joint memoryless entropy coding. This coding scheme offers an upper bound to the operational zero-delay vector-valued Gaussian RDF. When we use scalar quantization, then for r active dimensions of the vector Gauss-Markov source the gap between the obtained lower and theoretical upper bounds is less than or equal to 0.254r + 1 bits/vector. However, we further show that it is possible when we use vector quantization, and assume infinite dimensional Gauss-Markov sources to make the previous gap to be negligible, i.e., Gaussian NRDF approximates the operational zero-delay Gaussian RDF. We also extend our results to vector-valued Gaussian sources of any finite memory under mild conditions. Our theoretical framework is demonstrated with illustrative numerical experiments.

  • 14. Tsakmalis, A.
    et al.
    Chatzinotas, S.
    Ottersten, Björn
    University of Luxembourg, Luxembourg.
    Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks2018In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 12, no 1, p. 6-19Article in journal (Refereed)
    Abstract [en]

    In this paper, a sequential probing method for interference constraint learning is proposed to allow a centralized cognitive radio network (CRN) accessing the frequency band of a primary user (PU) in an underlay cognitive scenario with a designed PU protection specification. The main idea is that the CRN probes the PU, and subsequently, eavesdrops the reverse PU link to acquire the binary ACK/NACK packet. This feedback indicates whether the probing-induced interference is harmful or not and can be used to learn the PU interference constraint. The cognitive part of this sequential probing process is the selection of the power levels of the secondary users that aims to learn the PU interference constraint with a minimum number of probing attempts while setting a limit on the number of harmful probing-induced interference events or equivalently of NACK packet observations over a time window. This constrained design problem is studied within the active learning (AL) framework and an optimal solution is derived and implemented with a sophisticated, accurate, and fast Bayesian learning method, the expectation propagation. The performance of this solution is also demonstrated through numerical simulations and compared with modified versions of AL techniques we developed in earlier work.

  • 15.
    Yang, Ping
    et al.
    Univ Elect Sci & Technol China, Ctr Intelligent Networking & Commun, Chengdu 611731, Sichuan, Peoples R China.;Univ Elect Sci & Technol China, Natl Key Lab Sci & Technol Commun, Chengdu 611731, Sichuan, Peoples R China..
    Xiao, Yue
    Univ Elect Sci & Technol China, Ctr Intelligent Networking & Commun, Chengdu 611731, Sichuan, Peoples R China.;Univ Elect Sci & Technol China, Natl Key Lab Sci & Technol Commun, Chengdu 611731, Sichuan, Peoples R China..
    Xiao, Ming
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Ma, Zheng
    Southwest Jiaotong Univ, Sch Informat Sci & Technol, Key Lab Informat Coding & Transmiss, Chengdu 610031, Sichuan, Peoples R China..
    NOMA-Aided Precoded Spatial Modulation for Downlink MIMO Transmissions2019In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 13, no 3, p. 729-738Article in journal (Refereed)
    Abstract [en]

    In this paper, a novel multi-user multiple-input multiple-output (MIMO) transmission scheme, called non-orthogonal multiple access (NOMA) aided precoded spatial modulation (PSM) (NOMA-PSM) is proposed for overloaded downlink transmissions. NOMA-PSM beneficially amalgamates the concept of index modulation (IM) and NOMA techniques, and therefore it inherits both the merits of IM with low-complexity transceiver and the advantages of NOMA with high bandwidth efficiency. For the proposed scheme, we develop a pair of low-complexity yet effective detection algorithms by combining the spatial index demodulation and successive interference cancelation. The spectral efficiency (SE), implementation cost, and multi-user interference of NOMA-PSM are evaluated and compared with conventional designs. Furthermore, we derive the mutual information (MI) of the proposed NOMA-PSM to characterize its achievable SE and also obtain a lower bound for simplifying the measurement of MI. Our simulation results show that the proposed NOMA-PSM scheme is capable of achieving considerable performance gains over conventional orthogonal multiple access aid PSM and antenna-groupingbased PSM in wireless MIMO fading channels.

  • 16. Yang, Y.
    et al.
    Pesavento, M.
    Chatzinotas, S.
    Ottersten, Björn
    University of Luxembourg.
    Successive Convex Approximation Algorithms for Sparse Signal Estimation With Nonconvex Regularizations2018In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 12, no 6, p. 1286-1302Article in journal (Refereed)
    Abstract [en]

    In this paper, we propose a successive convex approximation framework for sparse optimization where the nonsmooth regularization function in the objective function is nonconvex and it can be written as the difference of two convex functions. The proposed framework is based on a nontrivial combination of the majorization-minimization framework and the successive convex approximation framework proposed in literature for a convex regularization function. The proposed framework has several attractive features, namely, first, flexibility, as different choices of the approximate function lead to different types of algorithms; second, fast convergence, as the problem structure can be better exploited by a proper choice of the approximate function and the stepsize is calculated by the line search; third, low complexity, as the approximate function is convex and the line search scheme is carried out over a differentiable function; fourth, guaranteed convergence to a stationary point. We demonstrate these features by two example applications in subspace learning, namely the network anomaly detection problem and the sparse subspace clustering problem. Customizing the proposed framework by adopting the best-response type approximation, we obtain soft-thresholding with exact line search algorithms for which all elements of the unknown parameter are updated in parallel according to closed-form expressions. The attractive features of the proposed algorithms are illustrated numerically.

1 - 16 of 16
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf