[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.
Disciplines :
Electrical & electronics engineering
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
External co-authors :
yes
Language :
English
Title :
Efficient Memory-Bounded Optimal Detection for GSM-MIMO Systems
Publication date :
July 2022
Journal title :
IEEE Transactions on Communications
ISSN :
0090-6778
Publisher :
Institute of Electrical and Electronics Engineers, United States
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