arXiv subjects

Michail Gkagkos

Publications and source records attributed to Michail Gkagkos.

6 recordsLinked to original sources

arXiv2023

Scalable Cell-Free Massive MIMO Unsourced Random Access System

Cell-Free Massive MIMO systems aim to expand the coverage area of wireless networks by replacing a single high-performance Access Point (AP) with multiple small, distributed APs connected to a Central Processing Unit (CPU) through a fronthaul. Another novel wireless approach, known as the unsourced random access (URA) paradigm, enables a large number of devices to communicate concurrently on the uplink. We consider a quasi-static Rayleigh fading channel paired to a scalable cell-free system, wherein a small number of receive antennas in the distributed APs serve devices equipped with a single antenna each. The goal of the study is to extend previous URA results to more realistic channels by examining the performance of a scalable cell-free system. To achieve this goal, we construct a coding scheme that adapts the URA paradigm to various cell-free scenarios. Empirical evidence suggests that using a cell-free architecture can improve the performance of a URA system, especially when taking into account large-scale attenuation and fading.

cs.IT↗

arXiv2023

PolarAir: A Compressed Sensing Scheme for Over-the-Air Federated Learning

We explore a scheme that enables the training of a deep neural network in a Federated Learning configuration over an additive white Gaussian noise channel. The goal is to create a low complexity, linear compression strategy, called PolarAir, that reduces the size of the gradient at the user side to lower the number of channel uses needed to transmit it. The suggested approach belongs to the family of compressed sensing techniques, yet it constructs the sensing matrix and the recovery procedure using multiple access techniques. Simulations show that it can reduce the number of channel uses by ~30% when compared to conveying the gradient without compression. The main advantage of the proposed scheme over other schemes in the literature is its low time complexity. We also investigate the behavior of gradient updates and the performance of PolarAir throughout the training process to obtain insight on how best to construct this compression scheme based on compressed sensing.

cs.IT↗

arXiv2022

FASURA: A Scheme for Quasi-Static Massive MIMO Unsourced Random Access Channels

This article considers the massive MIMO unsourced random access problem on a quasi-static Rayleigh fading channel. Given a fixed message length and a prescribed number of channel uses, the objective is to construct a coding scheme that minimizes the energy-per-bit subject to a fixed probability of error. The proposed scheme differs from other state-of-the-art schemes in that it blends activity detection, single-user coding, pilot-aided and temporary decisions-aided iterative channel estimation and decoding, minimum-mean squared error (MMSE) estimation, and successive interference cancellation (SIC). We show that an appropriate combination of these ideas can substantially outperform state-of-the-art coding schemes when the number of active users is more than 100, making this the best performing scheme known for this regime.

cs.IT↗

arXiv2021

Indirect Rate Distortion Functions with Side Information: Structural Properties and Multivariate Gaussian Sources

In this paper, we analyze the indirect source coding problem with side information at both the encoder and decoder, as well as only at the decoder. We first derive structural properties of the two rate distortion functions (RDFs) for general abstract spaces and identify conditions under which the RDFs coincide. For multivariate jointly Gaussian random variables with square-error fidelity, we establish structural properties of the optimal test channels, show that side information at both the encoder and decoder does not reduce compression, and provide water-filling solutions using parallel Gaussian channel realizations. This paper uses a novel realization theory approach to establish achievability of the converse coding theorem lower bounds of the two RDFs.

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arXiv2021

Approximate Support Recovery using Codes for Unsourced Multiple Access

We consider the approximate support recovery (ASR) task of inferring the support of a $K$-sparse vector ${\bf x} \in \mathbb{R}^n$ from $m$ noisy measurements. We examine the case where $n$ is large, which precludes the application of standard compressed sensing solvers, thereby necessitating solutions with lower complexity. We design a scheme for ASR by leveraging techniques developed for unsourced multiple access. We present two decoding algorithms with computational complexities $\mathcal{O}(K^2 \log n+K \log n \log \log n)$ and $\mathcal{O}(K^3 +K^2 \log n+ K \log n \log \log n)$ per iteration, respectively. When $K \ll n$, this is much lower than the complexity of approximate message passing with a minimum mean squared error denoiser% (AMP-MMSE) ,which requires $\mathcal{O}(mn)$ operations per iteration. This gain comes at a slight performance cost. Our findings suggest that notions from multiple access %such as spreading, matched filter receivers and codes can play an important role in the design of measurement schemes for ASR.

cs.IT↗

arXiv2020

Structural Properties of Optimal Test Channels for Distributed Source Coding with Decoder Side Information for Multivariate Gaussian Sources with Square-Error Fidelity

This paper focuses on the structural properties of test channels, of Wyner's operational information rate distortion function (RDF), $\overline{R}(Δ_X)$, of a tuple of multivariate correlated, jointly independent and identically distributed Gaussian random variables (RVs), $\{X_t, Y_t\}_{t=1}^\infty$, $X_t: Ω\rightarrow {\mathbb R}^{n_x}$, $Y_t: Ω\rightarrow {\mathbb R}^{n_y}$, with average mean-square error at the decoder, $\frac{1}{n} {\bf E}\sum_{t=1}^n||X_t - \widehat{X}_t||^2\leq Δ_X$, when $\{Y_t\}_{t=1}^\infty$ is the side information available to the decoder only. We construct optimal test channel realizations, which achieve the informational RDF, $\overline{R}(Δ_X) \triangleq\inf_{{\cal M}(Δ_X)} I(X;Z|Y)$, where ${\cal M}(Δ_X)$ is the set of auxiliary RVs $Z$ such that, ${\bf P}_{Z|X,Y}={\bf P}_{Z|X}$, $\widehat{X}=f(Y,Z)$, and ${\bf E}\{||X-\widehat{X}||^2\}\leq Δ_X$. We show the fundamental structural properties: (1) Optimal test channel realizations that achieve the RDF, $\overline{R}(Δ_X)$, satisfy conditional independence, $ {\bf P}_{X|\widehat{X}, Y, Z}={\bf P}_{X|\widehat{X},Y}={\bf P}_{X|\widehat{X}}, \hspace{.2in} {\bf E}\Big\{X\Big|\widehat{X}, Y, Z\Big\}={\bf E}\Big\{X\Big|\widehat{X}\Big\}=\widehat{X} $ and (2) similarly for the conditional RDF, ${R}_{X|Y}(Δ_X) \triangleq \inf_{{\bf P}_{\widehat{X}|X,Y}:{\bf E}\{||X-\widehat{X}||^2\} \leq Δ_X} I(X; \widehat{X}|Y)$, when $\{Y_t\}_{t=1}^\infty$ is available to both the encoder and decoder, and the equality $\overline{R}(Δ_X)={R}_{X|Y}(Δ_X)$.

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