Multi-sequence Spreading Random Access (MSRA) for Compressive Sensing-based Grant-free Communication
Ameha Tsegaye Abebe, Chung G. Kang

TL;DR
This paper introduces Multi-sequence Spreading Random Access (MSRA), a novel scheme that enhances grant-free communication by employing multiple spreading sequences, improving resource utilization and reducing collision-related failures.
Contribution
MSRA employs multiple spreading sequences per user, providing code diversity and modeling multi-user detection as a well-conditioned MMV CS problem, which is a novel approach.
Findings
Supports 82% more active users than conventional schemes.
Activity misdetection decreases exponentially with frame size.
Achieves lower RA failure rate close to collision limit.
Abstract
The performance of grant-free random access (GF-RA) is limited by the number of accessible random access resources (RRs) due to the absence of collision resolution. Compressive sensing (CS)-based RA schemes scale up the RRs at the expense of increased non-orthogonality among transmitted signals. This paper presents the design of multi-sequence spreading random access (MSRA) which employs multiple spreading sequences to spread the different symbols of a user as opposed to the conventional schemes in which a user employs the same spreading sequence for each symbol. We show that MSRA provides code diversity, enabling the multi-user detection (MUD) to be modeled into a well-conditioned multiple measurement vector (MMV) CS problem. The code diversity is quantified by the decrease in the average Babel mutual coherence among the spreading sequences. Moreover, we present a two-stage active user…
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