Reference : Efficient Memory-Bounded Optimal Detection for GSM-MIMO Systems

	Document type :	Scientific journals : Article
	Discipline(s) :	Engineering, computing & technology : Electrical & electronics engineering
	To cite this reference:	http://hdl.handle.net/10993/53132

Title :	Efficient Memory-Bounded Optimal Detection for GSM-MIMO Systems
Language :	English
Author, co-author :	He, Ke [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom > ; Guangzhou University > School of Computer Science and Cyber Engineering]
	He, Le [Guangzhou University > School of Computer Science and Cyber Engineering]
	Fan, Lisheng [Guangzhou University > School of Computer Science and Cyber Engineering]
	Lei, Xianfu [Southwest Jiaotong University > School of Information Science and Technology]
	Deng, Yansha [King’s College London > Department of Engineering]
	Karagiannidis, George [Aristotle University of Thessaloniki > Department of Electrical Computer Engineering]
Publication date :	Jul-2022
Journal title :	IEEE Transactions on Communications
Publisher :	Institute of Electrical and Electronics Engineers
Volume :	70
Issue/season :	7
Pages :	4359-4372
Peer reviewed :	Yes
Audience :	International
ISSN :	0090-6778
Country :	United States
Keywords :	[en] Signal detection ; MIMO ; tree search algorithm
Abstract :	[en] We investigate the optimal signal detection problem in large-scale multiple-input multiple-output (MIMO) system with the generalized spatial modulation (GSM) scheme, which can be formulated as a closest lattice point search (CLPS). To identify invalid signals, an efficient pruning strategy is needed while searching on the GSM decision tree. However, the existing algorithms have exponential complexity, whereas they are infeasible in large-scale GSM-MIMO systems. In order to tackle this problem, we propose a memory-efficient pruning strategy by leveraging the combinatorial nature of the GSM signal structure. Thus, the required memory size is squared to the number of transmit antennas. We further propose an efficient memory-bounded maximum likelihood (ML) search (EM-MLS) algorithm by jointly employing the proposed pruning strategy and the memory-bounded best-first algorithm. Theoretical and simulation results show that our proposed algorithm can achieve the optimal bit error rate (BER) performance, while its memory size can be bounded. Moreover, the expected time complexity decreases exponentially with increasing the signal-to-noise ratio (SNR) as well as the system’s excess degree of freedom, and it often converges to squared time under practical scenarios.
Funders :	Natural Science Foundation of China
Target :	Researchers ; Students
Permalink :	http://hdl.handle.net/10993/53132
DOI :	10.1109/TCOMM.2022.3176649
Other URL :	https://ieeexplore.ieee.org/abstract/document/9779207
Mentions required by the publisher for OA :	0090-6778 © 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information. Authorized licensed use limited to: University of Luxembourg. Downloaded on December 14,2022 at 12:33:48 UTC from IEEE Xplore. Restrictions apply.

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