Abstract-Multiple-input multiple-output (MIMO) technology has been widely used as an efficient way to improve the capacity of wireless channels. As an optimal detection for MIMOOFDM systems, the maximum likelihood (ML) algorithm whose complexity rises exponentially with the number of transmit and receive antennas is not practical for real-time detection. Instead, the sphere decoding (SD) algorithm was proposed to find the solution to the ML detection problem, however, the complexity is still large. The choice of the initial radius for SD detector has a significant impact on the complexity. In this paper, an improved depth-first- search sphere decoding (IDFS) algorithm based on the optimization of likelihood ascent search (LAS) predetection is proposed. The simulation results demonstrate that the proposed algorithm can not only achieve almost the same performance as ML detector but also efficiently decrease the complexity. For example, for about 49 visiting nodes for 16QAM and 30 visiting nodes for 4QAM, IDFS algorithm has about 4dB SNR gain than Schnorr-Euchner sphere decoding (SESD) algorithm as Fig.5 shows.
['Zhiheng Qin', 'Jin Xu', 'Xiaofeng Tao', 'Xiang Zhou']
Improved Depth-First-Search Sphere Decoding Based on LAS for MIMO-OFDM Systems