Reference : Efficient Memory-Bounded Optimal Detection for GSM-MIMO Systems
Scientific journals : Article
Engineering, computing & technology : Electrical & electronics engineering
Efficient Memory-Bounded Optimal Detection for GSM-MIMO Systems
He, Ke mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom > ; Guangzhou University > School of Computer Science and Cyber Engineering]
He, Le mailto [Guangzhou University > School of Computer Science and Cyber Engineering]
Fan, Lisheng mailto [Guangzhou University > School of Computer Science and Cyber Engineering]
Lei, Xianfu mailto [Southwest Jiaotong University > School of Information Science and Technology]
Deng, Yansha mailto [King’s College London > Department of Engineering]
Karagiannidis, George mailto [Aristotle University of Thessaloniki > Department of Electrical Computer Engineering]
IEEE Transactions on Communications
Institute of Electrical and Electronics Engineers
United States
[en] Signal detection ; MIMO ; tree search algorithm
[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.
Natural Science Foundation of China
Researchers ; Students
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