TY - JOUR
AU - Nafkha, Amor
AB - 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.
TI - Lightweight Learning for Grant-Free Activity Detection in Cell-Free Massive MIMO Networks
JF - Electrical Engineering and Systems Science
DO - 10.48550/arxiv.2503.11305
DA - 2025-04-04
UR - https://www.deepdyve.com/lp/arxiv-cornell-university/lightweight-learning-for-grant-free-activity-detection-in-cell-free-0vAFU4n0ow
VL - 2025
IS - 2503
DP - DeepDyve
ER -