Reduced-Complexity MIMO Detection via a Slicing Breadth-First Tree Search

Abstract

A bottleneck in multiple-input multiple-output communications systems is the complexity of detection at the receiver. The complexity of optimum maximum-likelihood detection is often prohibitive, especially for large numbers of antennas and large alphabets. A suboptimal tree-search-based detector known as the $K$ -best detector is an effective scheme that provides a flexible performance-complexity tradeoff. In this paper, we identify scalar list detection as a key building block of the $K$ -best detector, and we propose an efficient low-complexity implementation of the scalar list detector for $M$ -ary QAM using a slicing operation. Embedding the slicing list detector into the $K$ -best framework leads to our proposed slicing $K$ -best detector. Simulation results show that the proposed detector offers comparable performance to the conventional $K$ -best detector, but with significantly reduced complexity when $K$ is less than the QAM alphabet size $M$ . Since the slicing list detection is performed at each visited node in the detection tree, the complexity reduction is especially significant when the number of antennas and the alphabet size are large, making the proposed detector a competitive option for high spectral-efficiency wireless systems.