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

Ameha Tsegaye Abebe, Chung G. Kang

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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
Issue number12
Publication statusPublished - 2019 Dec

Bibliographical note

Funding Information:
Manuscript received June 25, 2019; revised August 27, 2019; accepted September 17, 2019. Date of publication October 7, 2019; date of current version December 10, 2019. This work was supported in part by Samsung Research in Samsung Electronics and in part by Korea University. The associate editor coordinating the review of this letter and approving it for publication was K. E. Psannis. (Corresponding author: Chung G. Kang.) A. T. Abebe is with the School of Electrical and Computer Engineering, Korea University, Seoul 136-713, South Korea (e-mail:

Publisher Copyright:
© 1997-2012 IEEE.


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

ASJC Scopus subject areas

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


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