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  • 1.
    Bokaei, Mohammad
    et al.
    Electrical Engineering Department, Sharif University of Technology, Iran; Department of Electronic Systems, Aalborg university, Denmark.
    Razavikia, Saeed
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering. Electrical Engineering Department, Sharif University of Technology, Iran.
    Rini, Stefano
    Electrical and Computer Engineering Department, National Yang-Ming Chao-Tung University (NYCU), Taiwan.
    Amini, Arash
    Electrical Engineering Department, Sharif University of Technology, Iran.
    Behrouzi, Hamid
    Electrical Engineering Department, Sharif University of Technology, Iran.
    Harmonic retrieval using weighted lifted-structure low-rank matrix completion2024In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 216, article id 109253Article in journal (Refereed)
    Abstract [en]

    In this paper, we investigate the problem of recovering the frequency components of a mixture of K complex sinusoids from a random subset of N equally-spaced time-domain samples. Because of the random subset, the samples are effectively non-uniform. Besides, the frequency values of each of the K complex sinusoids are assumed to vary continuously within a given range. For this problem, we propose a two-step strategy: (i) we first lift the incomplete set of uniform samples (unavailable samples are treated as missing data) into a structured matrix with missing entries, which is potentially low-rank; then (ii) we complete the matrix using a weighted nuclear minimization problem. We call the method a weighted lifted-structured (WLi) low-rank matrix recovery. Our approach can be applied to a range of matrix structures such as Hankel and double-Hankel, among others, and provides improvement over the unweighted existing schemes such as EMaC and DEMaC. We provide theoretical guarantees for the proposed method, as well as numerical simulations in both noiseless and noisy settings. Both the theoretical and the numerical results confirm the superiority of the proposed approach.

  • 2.
    Daei, Sajad
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Razavikia, Saeed
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Kountouris, Marios
    Communication Systems Department, EURECOM, 06410 Sophia Antipolis, France.
    Skoglund, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Fodor, Gabor
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). Ericsson Research, Sweden.
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Blind Asynchronous Goal-Oriented Detection for Massive Connectivity2023In: 2023 21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023, Institute of Electrical and Electronics Engineers Inc. , 2023, p. 167-174Conference paper (Refereed)
    Abstract [en]

    Resource allocation and multiple access schemes are instrumental for the success of communication networks, which facilitate seamless wireless connectivity among a growing population of uncoordinated and non-synchronized users. In this paper, we present a novel random access scheme that addresses one of the most severe barriers of current strategies to achieve massive connectivity and ultra reliable and low latency communications for 6G. The proposed scheme utilizes wireless channels’ angular continuous group-sparsity feature to provide low latency, high reliability, and massive access features in the face of limited time-bandwidth resources, asynchronous transmissions, and preamble errors. Specifically, a reconstruction-free goal oriented optimization problem is proposed which preserves the angular information of active devices and is then complemented by a clustering algorithm to assign active users to specific groups. This allows to identify active stationary devices according to their line of sight angles. Additionally, for mobile devices, an alternating minimization algorithm is proposed to recover their preamble, data, and channel gains simultaneously, enabling the identification of active mobile users. Simulation results show that the proposed algorithm provides excellent performance and supports a massive number of devices. Moreover, the performance of the proposed scheme is independent of the total number of devices, distinguishing it from other random access schemes. The proposed method provides a unified solution to meet the requirements of machine-type communications and ultra reliable and low latency communications, making it an important contribution to the emerging 6G networks.

  • 3.
    Hellström, Henrik
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering. Stanford University, Electrical Engineering Department, California, USA.
    Razavikia, Saeed
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Fodor, Viktória
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Optimal Receive Filter Design for Misaligned Over-the-Air Computation2023In: 2023 IEEE Globecom Workshops, GC Wkshps 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 1529-1535Conference paper (Refereed)
    Abstract [en]

    Over-the-air computation (AirComp) is a promising wireless communication method for aggregating data from many devices in dense wireless networks. The fundamental idea of AirComp is to exploit signal superposition to compute functions of multiple simultaneously transmitted signals. However, the time-and phase-alignment of these superimposed signals have a significant effect on the quality of function computation. In this study, we analyze the AirComp problem for a system with unknown random time delays and phase shifts. We show that the classical matched filter does not produce optimal results, and generates bias in the function estimates. To counteract this, we propose a new filter design and show that, under a bound on the maximum time delay, it is possible to achieve unbiased function computation. Additionally, we propose a Tikhonov regularization problem that produces an optimal filter given a tradeoff between the bias and noise-induced variance of the function estimates. When the time delays are long compared to the length of the transmitted pulses, our filter vastly outperforms the matched filter both in terms of bias and mean-squared error (MSE). For shorter time delays, our proposal yields similar MSE as the matched filter, while reducing the bias.

  • 4.
    Razavikia, Saeed
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    ChannelComp: A general framework for computing by digital communication2023Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    The imminent Internet of Things, fueled by 6G networks and machine learning technologies, is set to shift wireless communication to machine-centric paradigms, revolutionizing sectors such as healthcare or industrial automation through efficient data handling. However, this connectivity boom poses challenges, including straining existing communication systems due to increased data traffic and computational demands.

    Over-the-air computation (OAC) presents a feasible solution, allowing the summation of transmitted signals at a common receiver through analog amplitude modulation. Designed to enable concurrent data collection and computation at the network edge, OAC seeks to lessen the central system burden, reducing latency and energy usage while enabling real-time analytics. This approach is particularly beneficial for federated learning, a machine learning technique that operates across decentralized devices. However, OAC's dependence on analog communication poses notable challenges, including signal distortion during transmission and the limited availability of devices supporting analog modulations. Digital modulation is a preferable alternative, recognized for its excellent channel correction capabilities and broad acceptance in modern wireless devices. Nevertheless, its integration into OAC is perceived as a significant hurdle, with overlapping digitally modulated signals threatening the fundamental concept of simultaneous data collection and computation.

    The first part of the thesis provides an overview of communication systems, specifically focusing on the relevant OAC methodologies for analog and digital parts and their application in ML, particularly in training federated learning models. Subsequently, an exhaustive literature review concerning analog OAC techniques is undertaken, identifying existing limitations within this domain. The central thrust of our research is then introduced, proposing an innovative digital OAC approach along with a fresh perspective on the communication systems models designed for executing the computation. The chapter concludes with a summary of the principal contributions of each paper included within the thesis.

    In the second part, we introduce ChannelComp, a groundbreaking computing approach compatible with current digital communication systems, including smartphones and IoT devices. A detailed analysis of ChannelComp's functions reveals how it enables digital modulation schemes to perform computations, addressing a critical gap in previous research. Moreover, introducing pre-coders designed for function computation over the multiple access channel, combined with a feasibility optimization problem framework, allows for seamless integration with current systems. Compared to OAC, restricted to analog modulations, ChannelComp exhibits broader computational capabilities and adherence to strict computation time constraints, thus showcasing its robust potential for future massive machine-type communications. This innovative method signifies a promising direction toward sustainable and efficient future wireless communication.

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  • 5.
    Razavikia, Saeed
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Computing Functions Over-the-Air Using Digital Modulations.2023Manuscript (preprint) (Other academic)
    Abstract [en]

    Over-the-air computation (AirComp) is a known technique in which wireless devices transmit values by analog amplitude modulation so that a function of these values is computed over the communication channel at a common receiver. The physical reason is the superposition properties of the electromagnetic waves, which naturally return sums of analog values. Consequently, the applications of AirComp are almost entirely restricted to analog communication systems. However, the use of digital communications for over-the-air computations would have several benefits, such as error correction, synchronization, acquisition of channel state information, and easier adoption by current digital communication systems. Nevertheless, a common belief is that digital modulations are generally unfeasible for computation tasks because the overlapping of digitally modulated signals returns signals that seem to be meaningless for these tasks. This paper breaks through such a belief and proposes a fundamentally new computing method, named ChannelComp, for performing over-the-air computations by any digital modulation. In particular, we propose digital modulation formats that allow us to compute a wider class of functions than AirComp can compute, and we propose a feasibility optimization problem that ascertains the optimal digital modulation for computing functions over-the-air. The simulation results verify the superior performance of ChannelComp in comparison to AirComp, particularly for the product functions, with around 10 dB improvement of the computation error.

  • 6.
    Razavikia, Saeed
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Barros da Silva Jr., José Mairton
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Blind Federated Learning via Over-the-Air q-QAMManuscript (preprint) (Other academic)
    Abstract [en]

    In this work, we investigate federated edge learning over a fading multiple access channel. To alleviate the communication burden between the edge devices and the access point, we introduce a pioneering digital over-the-air computation strategy employing q-ary quadrature amplitude modulation, culminating in a low latency communication scheme. Indeed, we propose a new federated edge learning framework in which edge devices use digital modulation for over-the-air uplink transmission to the edge server while they have no access to the channel state information. Furthermore, we incorporate multiple antennas at the edge server to overcome the fading inherent in wireless communication.  We analyze the number of antennas required to mitigate the fading impact effectively. We prove a non-asymptotic upper bound for the mean squared error for the proposed federated learning with digital over-the-air uplink transmissions under both noisy and fading conditions.  Leveraging the derived upper bound, we characterize the convergence rate of the learning process of a non-convex loss function in terms of the mean square error of gradients due to the fading channel. Furthermore, we substantiate the theoretical assurances through numerical experiments concerning mean square error and the convergence efficacy of the digital federated edge learning framework. Notably, the results demonstrate that augmenting the number of antennas at the edge server and adopting higher-order modulations improve the model accuracy up to $60\%$.

  • 7.
    Razavikia, Saeed
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Barros da Silva Jr., José Mairton
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    SumComp: Coding for Digital Over-the-AirComputation via the Ring of IntegersManuscript (preprint) (Other academic)
    Abstract [en]

    Communication and computation are traditionally treated as separate entities, allowing for individual optimizations. However, many applications focus on local information's functionality rather than the information itself. For such cases, harnessing interference for computation in a multiple access channel through digital over-the-air computation can notably increase the computation, as established by the ChannelComp method. However, the coding scheme originally proposed in ChannelComp may suffer from high computational complexity because it is general and is not optimized for specific modulation categories. Therefore, this study considers a specific category of digital modulations for over-the-air computations, QAM and PAM, for which we introduce a novel coding scheme called SumComp.

    Furthermore, we derive an MSE analysis for SumComp coding in the computation of the arithmetic mean function and establish an upper bound on the MAE for a set of nomographic functions. Simulation results affirm the superior performance of SumComp coding compared to traditional analog over-the-air computation and the original coding in ChannelComp approaches regarding both MSE and MAE over a noisy multiple access channel. Specifically, SumComp coding shows approximately 10 dB improvements for computing arithmetic and geometric mean on the normalized MSE for low-noise scenarios.

  • 8.
    Razavikia, Saeed
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Da Silva, José Mairton Barros
    Department of Information Technology, Uppsala University, Sweden.
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    ChannelComp: A General Method for Computation by Communications2023In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, p. 1-1Article in journal (Refereed)
    Abstract [en]

    Over-the-air computation (AirComp) is a well-known technique by which several wireless devices transmit by analog amplitude modulation to achieve a sum of their transmit signals at a common receiver. The underlying physical principle is the superposition property of the radio waves. Since such superposition is analog and in amplitude, it is natural that AirComp uses analog amplitude modulations. Unfortunately, this is impractical because most wireless devices today use digital modulations. It would be highly desirable to use digital communications because of their numerous benefits, such as error correction, synchronization, acquisition of channel state information, and widespread use. However, when we use digital modulations for AirComp, a general belief is that the superposition property of the radio waves returns a meaningless overlapping of the digital signals. In this paper, we break through such beliefs and propose an entirely new digital channel computing method named ChannelComp, which can use digital as well as analog modulations. We propose a feasibility optimization problem that ascertains the optimal modulation for computing arbitrary functions over-the-air. Additionally, we propose pre-coders to adapt existing digital modulation schemes for computing the function over the multiple access channel. The simulation results verify the superior performance of ChannelComp compared to AirComp, particularly for the product functions, with more than 10 dB improvement of the computation error.

  • 9.
    Razavikia, Saeed
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Daei, Sajad
    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.
    Fodor, Gabor
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). Ericsson Research, Sweden.
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Off-the-grid Blind Deconvolution and Demixing2023In: GLOBECOM 2023 - 2023 IEEE Global Communications Conference, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 7604-7610Conference paper (Refereed)
    Abstract [en]

    We consider the problem of gridless blind deconvolution and demixing (GB2D) in scenarios where multiple users communicate messages through multiple unknown channels, and a single base station (BS) collects their contributions. This scenario arises in various communication fields, including wireless communications, the Internet of Things, over-the-air computation, and integrated sensing and communications. In this setup, each user's message is convolved with a multi-path channel formed by several scaled and delayed copies of Dirac spikes. The BS receives a linear combination of the convolved signals, and the goal is to recover the unknown amplitudes, continuous-indexed delays, and transmitted waveforms from a compressed vector of measurements at the BS. However, without prior knowledge of the transmitted messages and channels, GB2D is highly challenging and intractable in general. To address this issue, we assume that each user's message follows a distinct modulation scheme living in a known low-dimensional subspace. By exploiting these subspace assumptions and the sparsity of the multipath channels for different users, we transform the nonlinear GB2D problem into a matrix tuple recovery problem from a few linear measurements. To achieve this, we propose a semidefinite programming optimization that exploits the specific low-dimensional structure of the matrix tuple to recover the messages and continuous delays of different communication paths from a single received signal at the BS. Finally, our numerical experiments show that our proposed method effectively recovers all transmitted messages and the continuous delay parameters of the channels with sufficient samples.

  • 10.
    Razavikia, Saeed
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Channel Computing: Computation by Communications2022Patent (Other (popular science, discussion, etc.))
    Abstract [en]

    The invention relates to a computer-implemented method, a system, and a receiver for in-channel function computation.

  • 11.
    Razavikia, Saeed
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Peris, Jaume Anguera
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Barros da Silva Jr., José Mairton
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering. Princeton University, Department of Electrical and Computer Engineering, New Jersey, USA.
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Blind Asynchronous Over-the-Air Federated Edge Learning2022In: 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022: Proceedings, Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 1834-1839Conference paper (Refereed)
    Abstract [en]

    Federated Edge Learning (FEEL) is a distributed machine learning technique where each device contributes to training a global inference model by independently performing local computations with their data. More recently, FEEL has been merged with over-the-air computation (OAC), where the global model is calculated over the air by leveraging the superposition of analog signals. However, when implementing FEEL with OAC, there is the challenge on how to precode the analog signals to overcome any time misalignment at the receiver. In this work, we propose a novel synchronization-free method to recover the parameters of the global model over the air without requiring any prior information about the time misalignments. For that, we construct a convex optimization based on the norm minimization problem to directly recover the global model by solving a convex semi-definite program. The performance of the proposed method is evaluated in terms of accuracy and convergence via numerical experiments. We show that our proposed algorithm is close to the ideal synchronized scenario by 10%, and performs 4times better than the simple case where no recovering method is used.

  • 12.
    Seidi, Mohammadreza
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Micro and Nanosystems.
    Razavikia, Saeed
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Daei, Sajad
    EURECOM, Commun Syst Dept, Biot, F-06904 Sophia Antipolis, France..
    Oberhammer, Joachim
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Micro and Nanosystems.
    A Novel Demixing Algorithm for Joint Target Detection and Impulsive Noise Suppression2022In: IEEE Communications Letters, ISSN 1089-7798, E-ISSN 1558-2558, Vol. 26, no 11, p. 2750-2754Article in journal (Refereed)
    Abstract [en]

    This work considers a collocated radar scenario where a probing signal is emitted toward the targets of interest and records the received echoes. Estimating the relative delay-Doppler shifts of the targets allows determining their relative locations and velocities. However, the received radar measurements are often affected by impulsive non-Gaussian noise which makes a few measurements partially corrupted. While demixing radar signal and impulsive noise is challenging in general by traditional subspace-based methods, atomic norm minimization (ANM) has been recently developed to perform this task in a much more efficient manner. Nonetheless, the ANM cannot identify close delay-Doppler pairs and also requires many measurements. Here, we propose a smoothed l(0) atomic optimization problem encouraging both the sparse features of the targets and the impulsive noise. We design a majorization-minimization algorithm that converges to the solution of the proposed non-convex problem using alternating direction method of multipliers (ADMM). Simulations results verify the superior accuracy of our proposed algorithm even for very close delay-Doppler pairs in comparison to ANM with around 40 dB improvement.

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