Unsourced massive random access scheme exploiting Reed-Muller sequences
Unsourced massive random access scheme exploiting Reed-Muller sequences
The challenge in massive Machine Type Communication (mMTC) is to support reliable and instant access for an enormous number of machine-type devices (MTDs). In some particular applications of mMTC, the access point (AP) only has to know the messages received, but not where they source from, thus giving rise to the concept of unsourced random access (URA). In this paper, we propose a novel URA scheme exploiting the elegant properties of Reed-Muller (RM) sequences. Specifically, after dividing the message of an active user into several information chunks, RM sequences are used to carry those chunks, for exploiting the vast sequence space to improve the spectral efficiency, and their nested structure to enable reliable and efficient sequence detection. Next, we further explore a novel structural property of RM sequences for designing sparse patterns which carry part of the information and serve as the hints of coupling the information chunks of a single user. The factors affecting the performance of our slot-based RM detection are characterized. Besides, the complexity of the proposed message stitching method is analyzed and compared to the commonly used tree coding approach. Our simulation results verify the enhanced performance of the proposed URA scheme in error probability and computational complexity compared to the existing counterpart.
Wang, Jue
e3b89b63-81d2-49b2-a668-a3c54bc2090f
Zhang, Zhaoyang
5951d239-6a4e-41d1-a2e3-033e7696a939
Chen, Xiaoming
593009e9-709e-42d5-b6f3-4adaef0cefc0
Zhong, Caijun
ec150f86-eb08-4765-a642-23f2c94f1920
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Wang, Jue
e3b89b63-81d2-49b2-a668-a3c54bc2090f
Zhang, Zhaoyang
5951d239-6a4e-41d1-a2e3-033e7696a939
Chen, Xiaoming
593009e9-709e-42d5-b6f3-4adaef0cefc0
Zhong, Caijun
ec150f86-eb08-4765-a642-23f2c94f1920
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Wang, Jue, Zhang, Zhaoyang, Chen, Xiaoming, Zhong, Caijun and Hanzo, Lajos
(2021)
Unsourced massive random access scheme exploiting Reed-Muller sequences.
IEEE Transactions on Communications.
(doi:10.1109/TCOMM.2021.3139606).
Abstract
The challenge in massive Machine Type Communication (mMTC) is to support reliable and instant access for an enormous number of machine-type devices (MTDs). In some particular applications of mMTC, the access point (AP) only has to know the messages received, but not where they source from, thus giving rise to the concept of unsourced random access (URA). In this paper, we propose a novel URA scheme exploiting the elegant properties of Reed-Muller (RM) sequences. Specifically, after dividing the message of an active user into several information chunks, RM sequences are used to carry those chunks, for exploiting the vast sequence space to improve the spectral efficiency, and their nested structure to enable reliable and efficient sequence detection. Next, we further explore a novel structural property of RM sequences for designing sparse patterns which carry part of the information and serve as the hints of coupling the information chunks of a single user. The factors affecting the performance of our slot-based RM detection are characterized. Besides, the complexity of the proposed message stitching method is analyzed and compared to the commonly used tree coding approach. Our simulation results verify the enhanced performance of the proposed URA scheme in error probability and computational complexity compared to the existing counterpart.
Text
RM_based_URA_double_column
- Accepted Manuscript
More information
Accepted/In Press date: 22 December 2021
e-pub ahead of print date: 30 December 2021
Identifiers
Local EPrints ID: 453318
URI: http://eprints.soton.ac.uk/id/eprint/453318
ISSN: 0090-6778
PURE UUID: b82f722a-64d3-45ba-ba7e-ff2835e039ff
Catalogue record
Date deposited: 12 Jan 2022 17:42
Last modified: 18 Mar 2024 02:36
Export record
Altmetrics
Contributors
Author:
Jue Wang
Author:
Zhaoyang Zhang
Author:
Xiaoming Chen
Author:
Caijun Zhong
Author:
Lajos Hanzo
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics