Robust Activity Detection for Massive Random Access

Thumbnail Image

Files

Robust_Activity_Detection_for_Massive_Random_Access.pdf (1.69 MB)

Access rights

openAccess
CC BY
publishedVersion

URL

Journal Title

Journal ISSN

Volume Title

A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.

Authors

Date

2025

Department

Department of Information and Communications Engineering

Major/Subject

Mcode

Degree programme

Language

en

Pages

15

Series

IEEE Transactions on Signal Processing, Volume 73, pp. 3513-3527

Abstract

Massive machine-type communications (mMTC) are fundamental to the Internet of Things (IoT) framework in future wireless networks, involving the connection of a vast number of devices with sporadic transmission patterns. Traditional device activity detection (AD) methods are typically developed for Gaussian noise, but their performance may deteriorate when these conditions are not met, particularly in the presence of heavy-tailed impulsive noise. In this paper, we propose robust statistical techniques for AD that do not rely on the Gaussian assumption and replace the Gaussian loss function with robust loss functions that can effectively mitigate the impact of heavy-tailed noise and outliers. First, we prove that the coordinate-wise (conditional) objective function is geodesically convex and derive a fixedpoint (FP) algorithm for minimizing it, along with convergence guarantees. Building on the FP algorithm, we propose two robust algorithms for solving the full (unconditional) objective function: a coordinate-wise optimization algorithm (RCWO) and a greedy covariance learning-based matching pursuit algorithm (RCL-MP). Numerical experiments demonstrate that the proposed methods significantly outperform existing algorithms in scenarios with non-Gaussian noise, achieving higher detection accuracy and robustness.

Description

Publisher Copyright: © 1991-2012 IEEE.

Keywords

Device activity detection, fixed-point algorithm, machine-type communications, matching pursuit, robust statistics

Other note

DOI

10.1109/TSP.2025.3597931

Citation

Wang, X, Ollila, E & Vorobyov, S A 2025, 'Robust Activity Detection for Massive Random Access', IEEE Transactions on Signal Processing, vol. 73, pp. 3513-3527. https://doi.org/10.1109/TSP.2025.3597931

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-202510228499

Collections

[article-cris] Palvelut / Services