A memory reduced stack algorithm for MIMO detection

Multiple-input multiple-output (MIMO) technology can enhance the spectral efficiency significantly at the cost of high detection complexity. The stack algorithm can minimize the average complexity and achieve the optimal performance, but it suffers from the large memory size to store the candidate nodes. In this paper, we propose a memory reduced stack algorithm for soft-output MIMO detection. With the leaf enumeration scheme and parallel hypotheses update method, the proposed algorithm only stores non-leaf nodes in the stack and the leaf nodes are used for updating the soft-output. The proposed node pruning rule can simplify the search process and reduce the memory size further. The simulation results show that the proposed algorithm can reduce the demanded memory size of the advanced stack algorithm by 50% to achieve the same BER performance with the STS-SD for a 4 × 4 64QAM MIMO system.