Low‐complexity detection method for spatial modulation based on M ‐algorithm

Abstract

A novel M-algorithm-based detection method called M-algorithm to maximum likelihood (M-ML) is proposed for spatial modulation. The proposed algorithm has fixed complexity and reduces complexity by using a parallel structure to limit the search space of an optimum decoder. The simulation results show that by carefully choosing proper values of M, the new algorithm can reduce the computational complexity significantly while maintaining a near-optimum bit error rate performance.Introduction: To meet the development of future mobile communication demands, the researches on wireless systems continue to strive for higher data rates. The multi-antenna technique is an important way to improve the channel capacity, but blindly pursuing excessively high rates will make multiple-input-multiple-output (MIMO) systems lose their edge in energy consumption and complexity reduction. To solve this problem, a single-radio-frequency large-scale MIMO technique called spatial modulation (SM) has emerged [1,2]. Compared with the conventional MIMO systems, SM has obvious advantages in transmission/reception complexity and energy efficiency, which make it a hot candidate technique for future wireless systems, especially the massive MIMO.In SM systems, the optimum detection method is the maximumlikelihood (ML) detection [3]. The complexity of the optimum receiver increases linearly with the number of transmit and receive antennas, and exponentially with the order of constellation. Therefore, the complexity of SM with large-scale antennas or high-order signalling constellation is still great, which motivates researchers to further reduce the complexity of the SM receiver. In [4-6], a novel detection algorithm for SM based on the modified sphere decoder (SM-SD) has been proposed. This algorithm applies the existing SD algorithm in MIMO systems to reduce complexity. However, like the conventional SD, the SM-SD has the same drawbacks, such as variable complexity and a serial detection structure, which make it difficult for practical applications [7]. However, there are many ways to reduce the complexity of ML detection in MIMO systems. A series of algorithms applying the M-algorithm to ML (M-ML) with fixed complexity and a breadth-first parallel structure are conducive to practical implementation [8].The M-ML algorithm combined with the limited tree search structure avoids exhaustive search by selecting only M candidates with the smallest accumulated metrics at each level of the tree. For achieving the near ML performance, M should be large enough for the selected points to include the correct one. The conventional M-algorithm in MIMO systems can be applied to SM by adding zero point to constellation without considering the characteristics of SM; however, the Euclidean distances between constellation points decrease so that the performance of the system degrades correspondingly.In this Letter, a modified M-ML method that considers the attributes of SM is proposed. Like the conventional M-ML, this algorithm with fixed complexity and a breadth...

Fast Scalable Architecture of a Near-ML Detector for a MIMO-QSM Receiver

“…In this section, a modified low complexity Near-ML detector for MIMO-QSM signals is presented. The proposed detector is based on a tree search and a spherical algorithm [20,21].…”

“…The second part corresponds to an optimized detector based on the detector for SM signals published in [21]. Since the first part of the Near-ML algorithm estimated the real part of the transmitted QSM symbol, ˆ (l) ,ŝ (l) , the second part considered that only one Tx antenna was active, which corresponded to the imaginary part of the QSM transmitted symbol ˆ (m) ,ŝ (m) .…”

M-Algorithm-Based Optimal Detectors for Spatial Modulation

“…4, ML detector of SM system also has tree search structure, thus the M-algorithm can be applied to SM. However, In [17], the expression of the M-ML procedures has a mistake. The authors forgot to calculate the sum value of the metrics where the metrics of each layer should be accumulated metrics.…”

“…The main purpose of M-algorithm is that limit the candidate constellation points to a fixed number M to reduce the exhaustive search space. In [17], a novel M-algorithm-based detection method called M-ML applied to spatial modulation was proposed, the proposed algorithm has fixed complexity and reduces complexity by using parallel structure to limit the search space of optimum decoder. However, follow the procedures described in [17], the M-ML detector performs far away from the optimal detector actually.…”

Low-Complexity Detection Algorithms for Spatial Modulation MIMO Systems

“…In [18,19], the M-algorithm to maximum likelihood (MML) detector with prioritized tree-search structure was presented. e detection is considered as a breadth-first search tree with N T N M branches and N R layers, in which the ith layer corresponds to the ith receive antenna (RA).…”