Joint Channel Estimation and MUD for Scalable Grant-Free Random Access

Ameha Tsegaye Abebe, Chung G. Kang

Research output: Contribution to journalArticlepeer-review

Abstract

A scalable grant-free access, which simultaneously reduces access collision rate and signaling overhead, can be enabled by allowing contending users to randomly select a preamble from a very large set of non-orthogonal preamble sequences. By casting the joint channel estimation and multi-user detection (CE-MUD) problem into a multiple measurement vector (MMV)-based compressive sensing problem and employing a two-stage active preambles search over a narrowed subspace, a scheme proposed in this letter shows significant performance improvement over conventional schemes. Particularly, the two-stage search allows the proposed scheme to support multiple order of more active users while reducing receiver's complexity.

Original languageEnglish
Article number8861377
Pages (from-to)2229-2233
Number of pages5
JournalIEEE Communications Letters
Volume23
Issue number12
DOIs
Publication statusPublished - 2019 Dec

Keywords

  • channel estimation
  • compressive sensing
  • grant-free random access
  • MUD
  • Non-orthogonal preamble sequences

ASJC Scopus subject areas

  • Modelling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering

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