Lightweight Learning for Grant-Free Activity Detection in Cell-Free Massive MIMO Networks

• URL: http://arxiv.org/abs/2503.11305v2
• Date: Fri, 04 Apr 2025 15:24:36 GMT
• Title: Lightweight Learning for Grant-Free Activity Detection in Cell-Free Massive MIMO Networks
• Authors: Ali Elkeshawy, Haifa Fares, Amor Nafkha,
• Abstract summary: Grant-free random access (GF-RA) is a promising access technique for massive machine-type communications (mMTC) in future wireless networks.<n>This study investigates the efficiency of employing supervised machine learning techniques to tackle the challenges on the device activity detection (AD)<n>We propose a novel lightweight data-driven algorithmic framework specifically designed for activity detection in GF-RA for mMTC.
• Score: 0.19662978733004596
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Grant-free random access (GF-RA) is a promising access technique for massive machine-type communications (mMTC) in future wireless networks, particularly in the context of 5G and beyond (6G) systems. Within the context of GF-RA, this study investigates the efficiency of employing supervised machine learning techniques to tackle the challenges on the device activity detection (AD). GF-RA addresses scalability by employing non-orthogonal pilot sequences, which provides an efficient alternative comparing to conventional grant-based random access (GB-RA) technique that are constrained by the scarcity of orthogonal preamble resources. In this paper, we propose a novel lightweight data-driven algorithmic framework specifically designed for activity detection in GF-RA for mMTC in cell-free massive multiple-input multiple-output (CF-mMIMO) networks. We propose two distinct framework deployment strategies, centralized and decentralized, both tailored to streamline the proposed approach implementation across network infrastructures. Moreover, we introduce optimized post-detection methodologies complemented by a clustering stage to enhance overall detection performances. Our 3GPP-compliant simulations have validated that the proposed algorithm achieves state-of-the-art model-based activity detection accuracy while significantly reducing complexity. Achieving 99% accuracy, it demonstrates real-world viability and effectiveness.

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