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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, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Nätverk och systemteknik. 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 completion2024Ingår i: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 216, artikel-id 109253Artikel i tidskrift (Refereegranskat)
    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, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Teknisk informationsvetenskap.
    Razavikia, Saeed
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Nätverk och systemteknik.
    Kountouris, Marios
    Communication Systems Department, EURECOM, 06410 Sophia Antipolis, France.
    Skoglund, Mikael
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Teknisk informationsvetenskap.
    Fodor, Gabor
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Reglerteknik. Ericsson Research, Sweden.
    Fischione, Carlo
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Nätverk och systemteknik.
    Blind Asynchronous Goal-Oriented Detection for Massive Connectivity2023Ingår i: 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, s. 167-174Konferensbidrag (Refereegranskat)
    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.
    Razavikia, Saeed
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Nätverk och systemteknik.
    ChannelComp: A general framework for computing by digital communication2023Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [sv]

    Den nära förestående Internet of Things, drivet av 6G-nätverk och maskininlärningsteknologier, är på väg att förändra trådlös kommunikation till maskincentrerade paradigm, revolutionerande sektorer som hälso- och sjukvård samt industriell automatisering genom effektiv datahantering. Dock medför denna uppkopplingsboom utmaningar, inklusive påfrestningar på befintliga kommunikationssystem på grund av ökad datatrafik och beräkningsbehov.

    Over-the-air-beräkning (OAC) framstår som en genomförbar lösning, genom att tillåta summering av överförda signaler hos en gemensam mottagare genom analog amplitudmodulering. Utformad för att möjliggöra samtidig datainsamling och beräkning vid nätverkskanten, strävar OAC efter att minska den centrala systembelastningen, minska latens och energiförbrukning samtidigt som det möjliggör realtidsanalys. Denna metod är särskilt fördelaktig för federerad inlärning, en maskininlärningsteknik som fungerar över decentraliserade enheter. Dock medför OAC:s beroende av analog kommunikation märkbara utmaningar, inklusive signal distortion under överföring och begränsad tillgänglighet av enheter som stöder analoga moduleringar. Digital modulering är ett föredraget alternativ, erkänt för dess utmärkta kanalkorrigeringsegenskaper och bred acceptans i moderna trådlösa enheter. Trots detta uppfattas dess integration i OAC som ett betydande hinder, med överlappande digitalt modulerade signaler som hotar den grundläggande konceptet med samtidig datainsamling och beräkning.

    Den första delen av avhandlingen ger en översikt över kommunikationssystem, med särskilt fokus på relevanta OAC-metodiker för analoga och digitala delar och deras tillämpning i ML, särskilt vid träning av federerade inlärningsmodeller. Därefter genomförs en omfattande litteraturöversikt angående analoga OAC-tekniker, där befintliga begränsningar inom detta område identifieras. Forskningens centrala drivkraft introduceras sedan, med förslag på en innovativ digital OAC-metod tillsammans med ett nytt perspektiv på kommunikationssystemmodeller utformade för att utföra beräkningen. Kapitlet avslutas med en sammanfattning av de huvudsakliga bidragen från varje artikel inkluderad i avhandlingen.

    I den andra delen introducerar vi ChannelComp, en ny och banbrytande beräkningsmetod som är kompatibel med nuvarande digitala kommunikationssystem, inklusive smartphones och IoT-enheter. En detaljerad analys av ChannelComp:s funktioner avslöjar hur den möjliggör digitala moduleringsscheman för att utföra beräkningar, vilket adresserar en kritisk lucka i tidigare forskning. Dessutom möjliggör introduktionen av förkodare utformade för funktionsberäkning över den fleraccessa kanalen, kombinerat med ett ramverk för genomförbarhetsoptimeringsproblem, en sömlös integration med nuvarande system. Jämfört med OAC, begränsad till analoga moduleringar, uppvisar ChannelComp bredare beräkningsmöjligheter och efterlevnad av strikta beräkningstidsbegränsningar, vilket visar dess robusta potential för framtida massiva maskintypkommunikationer. Denna innovativa metod signalerar en lovande riktning mot hållbar och effektiv framtida trådlös kommunikation.

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  • 4.
    Razavikia, Saeed
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Nätverk och systemteknik.
    Computing Functions Over-the-Air Using Digital Modulations.2023Manuskript (preprint) (Övrigt vetenskapligt)
    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.

  • 5.
    Razavikia, Saeed
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Nätverk och systemteknik.
    Barros da Silva Jr., José Mairton
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Nätverk och systemteknik.
    Fischione, Carlo
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Nätverk och systemteknik. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre.
    Blind Federated Learning via Over-the-Air q-QAMManuskript (preprint) (Övrigt vetenskapligt)
    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 $60\%$.

  • 6.
    Razavikia, Saeed
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Nätverk och systemteknik.
    Barros da Silva Jr., José Mairton
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Nätverk och systemteknik.
    Fischione, Carlo
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Nätverk och systemteknik.
    SumComp: Coding for Digital Over-the-AirComputation via the Ring of IntegersManuskript (preprint) (Övrigt vetenskapligt)
    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.

  • 7.
    Razavikia, Saeed
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Nätverk och systemteknik.
    Da Silva, José Mairton Barros
    Department of Information Technology, Uppsala University, Sweden.
    Fischione, Carlo
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Nätverk och systemteknik.
    ChannelComp: A General Method for Computation by Communications2023Ingår i: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, s. 1-1Artikel i tidskrift (Refereegranskat)
    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.

  • 8.
    Razavikia, Saeed
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Nätverk och systemteknik.
    Daei, Sajad
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Teknisk informationsvetenskap.
    Skoglund, Mikael
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Teknisk informationsvetenskap.
    Fodor, Gabor
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Reglerteknik. Ericsson Research, Sweden.
    Fischione, Carlo
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Nätverk och systemteknik.
    Off-the-grid Blind Deconvolution and Demixing2023Ingår i: GLOBECOM 2023 - 2023 IEEE Global Communications Conference, Institute of Electrical and Electronics Engineers (IEEE) , 2023, s. 7604-7610Konferensbidrag (Refereegranskat)
    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.

  • 9.
    Razavikia, Saeed
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Nätverk och systemteknik.
    Fischione, Carlo
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Nätverk och systemteknik. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre.
    Channel Computing: Computation by Communications2022Patent (Övrig (populärvetenskap, debatt, mm))
    Abstract [en]

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

  • 10.
    Razavikia, Saeed
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Nätverk och systemteknik.
    Peris, Jaume Anguera
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Teknisk informationsvetenskap.
    Barros da Silva Jr., José Mairton
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Nätverk och systemteknik. Princeton University, Department of Electrical and Computer Engineering, New Jersey, USA.
    Fischione, Carlo
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Nätverk och systemteknik.
    Blind Asynchronous Over-the-Air Federated Edge Learning2022Ingår i: 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022: Proceedings, Institute of Electrical and Electronics Engineers (IEEE) , 2022, s. 1834-1839Konferensbidrag (Refereegranskat)
    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.

  • 11.
    Seidi, Mohammadreza
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Mikro- och nanosystemteknik.
    Razavikia, Saeed
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Nätverk och systemteknik.
    Daei, Sajad
    EURECOM, Commun Syst Dept, Biot, F-06904 Sophia Antipolis, France..
    Oberhammer, Joachim
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Mikro- och nanosystemteknik.
    A Novel Demixing Algorithm for Joint Target Detection and Impulsive Noise Suppression2022Ingår i: IEEE Communications Letters, ISSN 1089-7798, E-ISSN 1558-2558, Vol. 26, nr 11, s. 2750-2754Artikel i tidskrift (Refereegranskat)
    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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