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Bokaei, M., Razavikia, S., Rini, S., Amini, A. & Behrouzi, H. (2024). Harmonic retrieval using weighted lifted-structure low-rank matrix completion. Signal Processing, 216, Article ID 109253.
Open this publication in new window or tab >>Harmonic retrieval using weighted lifted-structure low-rank matrix completion
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2024 (English)In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 216, article id 109253Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier BV, 2024
Keywords
Hankel structure, Lifting operator, Low-rank matrix completion
National Category
Signal Processing Control Engineering Fluid Mechanics and Acoustics
Identifiers
urn:nbn:se:kth:diva-339042 (URN)10.1016/j.sigpro.2023.109253 (DOI)2-s2.0-85174693122 (Scopus ID)
Note

QC 20231128

Available from: 2023-11-28 Created: 2023-11-28 Last updated: 2023-11-30Bibliographically approved
Daei, S., Razavikia, S., Kountouris, M., Skoglund, M., Fodor, G. & Fischione, C. (2023). Blind Asynchronous Goal-Oriented Detection for Massive Connectivity. In: 2023 21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023: . Paper presented at 21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023, Singapore, Singapore, Aug 24 2023 - Aug 27 2023 (pp. 167-174). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Blind Asynchronous Goal-Oriented Detection for Massive Connectivity
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2023 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2023
Keywords
atomic norm minimization, goal-oriented optimization, Internet of Things, MIMO communications systems, Random access, reconstruction-free inference
National Category
Communication Systems
Identifiers
urn:nbn:se:kth:diva-343751 (URN)10.23919/WiOpt58741.2023.10349818 (DOI)2-s2.0-85184668805 (Scopus ID)
Conference
21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023, Singapore, Singapore, Aug 24 2023 - Aug 27 2023
Note

QC 20240222

Part of ISBN 978-390317655-3

Available from: 2024-02-22 Created: 2024-02-22 Last updated: 2024-02-22Bibliographically approved
Razavikia, S. (2023). ChannelComp: A general framework for computing by digital communication. (Licentiate dissertation). Stockholm: KTH Royal Institute of Technology
Open this publication in new window or tab >>ChannelComp: A general framework for computing by digital communication
2023 (English)Licentiate 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.

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.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2023. p. 161
Series
TRITA-EECS-AVL ; 2023:75
National Category
Communication Systems
Research subject
Telecommunication
Identifiers
urn:nbn:se:kth:diva-338940 (URN)978-91-8040-741-0 (ISBN)
Presentation
2023-11-20, E32 https://kth-se.zoom.us/j/61848116543, Osquars backe 2, E-huset, huvudbyggnaden, Lindstedtsvägen 3, floor 3, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

QC 20231031

Available from: 2023-10-31 Created: 2023-10-31 Last updated: 2023-11-13Bibliographically approved
Razavikia, S., Da Silva, J. M. & Fischione, C. (2023). ChannelComp: A General Method for Computation by Communications. IEEE Transactions on Communications, 1-1
Open this publication in new window or tab >>ChannelComp: A General Method for Computation by Communications
2023 (English)In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, p. 1-1Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
National Category
Communication Systems
Identifiers
urn:nbn:se:kth:diva-338912 (URN)10.1109/tcomm.2023.3324999 (DOI)001164695100015 ()
Note

QC 20231031

Available from: 2023-10-30 Created: 2023-10-30 Last updated: 2024-04-05Bibliographically approved
Razavikia, S. (2023). Computing Functions Over-the-Air Using Digital Modulations.. Paper presented at IEEE International Conference on Communications.
Open this publication in new window or tab >>Computing Functions Over-the-Air Using Digital Modulations.
2023 (English)Manuscript (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.

Publisher
p. 5780-5786
National Category
Computer Systems
Identifiers
urn:nbn:se:kth:diva-338914 (URN)
Conference
IEEE International Conference on Communications
Note

QC 20231031

Available from: 2023-10-30 Created: 2023-10-30 Last updated: 2023-11-01Bibliographically approved
Razavikia, S., Daei, S., Skoglund, M., Fodor, G. & Fischione, C. (2023). Off-the-grid Blind Deconvolution and Demixing. In: GLOBECOM 2023 - 2023 IEEE Global Communications Conference: . Paper presented at 2023 IEEE Global Communications Conference, GLOBECOM 2023, Kuala Lumpur, Malaysia, Dec 4 2023 - Dec 8 2023 (pp. 7604-7610). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Off-the-grid Blind Deconvolution and Demixing
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2023 (English)In: GLOBECOM 2023 - 2023 IEEE Global Communications Conference, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 7604-7610Conference paper, Published 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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Atomic norm minimization, blind channel estimation, blind data recovery, blind deconvolution, blind demixing
National Category
Telecommunications
Identifiers
urn:nbn:se:kth:diva-344558 (URN)10.1109/GLOBECOM54140.2023.10437392 (DOI)001178562008030 ()2-s2.0-85187336253 (Scopus ID)
Conference
2023 IEEE Global Communications Conference, GLOBECOM 2023, Kuala Lumpur, Malaysia, Dec 4 2023 - Dec 8 2023
Note

Part of ISBN 979-8-3503-1090-0

QC 20240326

Available from: 2024-03-20 Created: 2024-03-20 Last updated: 2024-04-12Bibliographically approved
Seidi, M., Razavikia, S., Daei, S. & Oberhammer, J. (2022). A Novel Demixing Algorithm for Joint Target Detection and Impulsive Noise Suppression. IEEE Communications Letters, 26(11), 2750-2754
Open this publication in new window or tab >>A Novel Demixing Algorithm for Joint Target Detection and Impulsive Noise Suppression
2022 (English)In: IEEE Communications Letters, ISSN 1089-7798, E-ISSN 1558-2558, Vol. 26, no 11, p. 2750-2754Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
Keywords
Radar, impulsive noise cancellation, compressed sensing, l(0) function, non-convex optimization, atomic norm minimization
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-322150 (URN)10.1109/LCOMM.2022.3199460 (DOI)000881981500051 ()2-s2.0-85136894654 (Scopus ID)
Note

QC 20221202

Available from: 2022-12-02 Created: 2022-12-02 Last updated: 2023-11-01Bibliographically approved
Razavikia, S., Peris, J. A., Barros da Silva Jr., J. M. & Fischione, C. (2022). Blind Asynchronous Over-the-Air Federated Edge Learning. In: 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022: Proceedings. Paper presented at 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022, Virtual, Online, Brazil, Dec 4 2022 - Dec 8 2022 (pp. 1834-1839). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Blind Asynchronous Over-the-Air Federated Edge Learning
2022 (English)In: 2022 IEEE GLOBECOM Workshops, GC Wkshps 2022: Proceedings, Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 1834-1839Conference paper, Published 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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
Keywords
Asynchronous, federated edge learning, over-the-air computation, time misalignment
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-333461 (URN)10.1109/GCWkshps56602.2022.10008588 (DOI)2-s2.0-85146155208 (Scopus ID)
Conference
2022 IEEE GLOBECOM Workshops, GC Wkshps 2022, Virtual, Online, Brazil, Dec 4 2022 - Dec 8 2022
Note

Part of ISBN 9781665459754

QC 20230802

Available from: 2023-08-02 Created: 2023-08-02 Last updated: 2023-11-01Bibliographically approved
Razavikia, S. & Fischione, C. (2022). Channel Computing: Computation by Communications. .
Open this publication in new window or tab >>Channel Computing: Computation by Communications
2022 (English)Patent (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.

National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-338939 (URN)
Note

QC 20231031

Available from: 2023-10-31 Created: 2023-10-31 Last updated: 2023-10-31Bibliographically approved
Razavikia, S., Barros da Silva Jr., J. M. & Fischione, C. Blind Federated Learning via Over-the-Air q-QAM.
Open this publication in new window or tab >>Blind Federated Learning via Over-the-Air q-QAM
(English)Manuscript (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 $60\%$.

National Category
Communication Systems
Identifiers
urn:nbn:se:kth:diva-338917 (URN)
Note

QC 20231031

Available from: 2023-10-30 Created: 2023-10-30 Last updated: 2023-11-01Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-4519-9204

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