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Publications (3 of 3) Show all publications
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., 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
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-6866-6595

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