Multi-Sequence Spreading Random Access (MSRA) for Compressive Sensing-Based Grant-Free Communication

Ameha Tsegaye Abebe, Chung G. Kang

Research output: Contribution to journalArticlepeer-review

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 vectors (MMVs) 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 detection (AUD) scheme for both wideband and narrowband implementations. Our theoretical analysis shows that with MSRA activity misdetection falls exponentially while the size of GF-RA frame is increased. Finally, the simulation results show that about 82% increase in utilization of RRs, i.e., more active users, is supported by MSRA than the conventional schemes while achieving the RA failure rate lower bound set by random access collision.

Original languageEnglish
Pages (from-to)7531-7543
Number of pages13
JournalIEEE Transactions on Communications
Volume69
Issue number11
DOIs
Publication statusPublished - 2021 Nov 1

Keywords

  • Compressive sensing
  • grant-free random access
  • machine-type communication
  • multiple measurement vector (MMV)
  • multiple-sequence spreading random access (MSRA)
  • non-orthogonal multiple access (NOMA)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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