Low complexity Massive MIMO detection algorithm based on improved LAS

Abstract

Massive multiple-input multiple-output (M-MIMO) technology is a key technology for 5G communications and future mobile wireless networks. Although the likelihood ascent search (LAS) algorithm in the existing detection algorithms is relatively low in complexity, the algorithm is easy to fall into the local optimum, resulting in poor global performance. Therefore, based on this algorithm, this paper proposes an improved detection scheme based on the LAS algorithm in the reduced neighborhood. This algorithm combines the idea of a reduced neighborhood and iteratively improves the LAS algorithm. The algorithm is designed by reducing the size of the neighborhood and increasing the number of iterations. The algorithm compares the BER performance of different neighborhood parameters, and obtains a set of parameters to significantly reduce BER through simulation comparison.