A new dynamic K-best SD algorithm for MIMO detection

Multiple Input Multiple Output (MIMO) system is considered as an unalterable technology in wireless communication for its advantages in the spectral efficiency. Among the detection algorithms, maximum likelihood (ML) detection can achieve the best bit error rate performance, but the computational complexity of ML detection is too huge to be acceptable. In order to solve this problem, numbers of algorithms have been proposed. The K-Best SD sphere decoding (K-Best SD) algorithm is one of them. As K increase, the K-Best SD algorithm will approach the bit error rate of ML detection. However, if the K is large, the computational complexity will be unacceptable. In this paper, we propose a modified K-Best SD algorithm, in which the difference between the partial Euclidean distance of best and second best solution at each level of the tree search can be used to calculate the dynamic K, which can reduce the computational complexity considerably with a negligible BER performance loss.