Efficient Memory-Bounded Optimal Detection for GSM-MIMO Systems

TY - JOUR

T1 - Efficient Memory-Bounded Optimal Detection for GSM-MIMO Systems

AU - He, Ke

AU - He, Le

AU - Fan, Lisheng

AU - Lei, Xianfu

AU - Deng, Yansha

AU - Karagiannidis, George K.

N1 - Publisher Copyright: © 1972-2012 IEEE.

PY - 2022/7/1

Y1 - 2022/7/1

N2 - 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.

AB - 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.

KW - generalized spatial modulation

KW - GSM

KW - Indexes

KW - Maximum likelihood detection

KW - maximum likelihood detection

KW - MIMO

KW - MIMO communication

KW - Modulation

KW - Signal detection

KW - sphere decoding

KW - Symbols

KW - Transmitting antennas

KW - tree search algorithm

UR - http://www.scopus.com/inward/record.url?scp=85130484856&partnerID=8YFLogxK

U2 - 10.1109/TCOMM.2022.3176649

DO - 10.1109/TCOMM.2022.3176649

M3 - Article

AN - SCOPUS:85130484856

SN - 0090-6778

VL - 70

SP - 4359

EP - 4372

JO - IEEE TRANSACTIONS ON COMMUNICATIONS

JF - IEEE TRANSACTIONS ON COMMUNICATIONS

IS - 7

ER -