Abstract
In this paper, we propose a low-complexity group alternate iterative list (GAIL) detection algorithm for MIMO systems. By utilizing the recursive interference suppression and successive interference cancellation techniques, the symbol vector can be partitioned into many subgroups. Subsequently, symbols in each subgroup are detected in terms of the K-best detector. The inter-group interference is effectively mitigated in the GAIL algorithm by creating a candidate list and iteratively correcting the unreliable symbols for the detection result. We provide the performance-complexity tradeoff based on different feasible parameter settings. The numerical results demonstrate that the GAIL algorithm can achieve close-to-optimal performance while maintaining low computational complexity. In addition, the running speed of the GAIL algorithm can be dramatically increased using parallel processing in real-time communication systems.
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