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Performance analysis of massive MIMO assisted semi-grant-free random access

Version 2 2024-06-05, 01:20
Version 1 2021-04-01, 10:06
conference contribution
posted on 2024-06-05, 01:20 authored by J Ding, M Feng, M Nemati, Jinho ChoiJinho Choi
Massive multiple input multiple output (mMIMO) assisted grant-free random access (RA) (mGFRA) has been considered a promising RA scheme for future machine-type communication (MTC). In mGFRA, the nature that RA user equipments (UEs) blindly interplay each other in the presence of preamble collision degrades the performance of RA UEs. To address the issue, a mMIMO assisted semi-grant-free RA (mSGFRA) scheme is considered in this paper, where a downlink feedback based on the preamble detection after the preamble phase is introduced. With such a feedback, RA UEs experiencing preamble collision are enforced to keep silent in data-transmission phase, which in turn enhances the performance of RA UEs without experiencing preamble collision. To understand the performance behaviours of mSGFRA, we first analyse the preamble detection performance in mSGFRA and reveal that accurate collided-preamble detection can be achieved with the assistance of mMIMO. Then, we analyse and compare the performance of mSGFRA and mGFRA in terms of success probability. Simulation results validate theoretical analysis and confirm the potential superiority of mSGFRA to mGFRA.

History

Pagination

1-7

Location

Las Vegas, Nevada

Start date

2021-01-09

End date

2021-01-12

ISBN-13

9781728197944

Language

eng

Publication classification

E1 Full written paper - refereed

Title of proceedings

CCNC 2021 : Proceedings of the 2021 IEEE 18th Annual Consumer Communications and Networking Conference

Event

Consumer Communications & Networking. Conference (2021 : 18th : Las Vegas, Nevada)

Publisher

IEEE

Place of publication

Piscataway, N.J.

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