Achieving a Continuous Diversity-Complexity Tradeoff in Wireless MIMO Systems via Pre-Equalized Sphere-Decoding

Abstract

In multiple-input multiple-output (MIMO) spatial multiplexing systems, maximum-likelihood (ML) detection achieves maximum diversity at the cost of high computational complexity. In contrast, low-complexity alternatives like zero-forcing (ZF) detection suffer from a significant diversity loss. In this paper, we present a novel detection scheme that allows a continuous tradeoff between diversity order and complexity reduction. The proposed detector consists of two stages: the first stage is a linear pre-equalizer that partially eliminates MIMO interference and improves the channel condition number; the second stage is a mismatched ML detector on the residual channel that ignores the correlation introduced by the equalization stage. This second stage is implemented using a variant of the sphere decoder. ML and ZF detection are extreme special cases of our novel scheme. We give insights into the main effects that determine performance and we provide an explicit expression for the diversity order achieved with the proposed detector. Simulation results confirm that our scheme allows to trade diversity for complexity savings in a continuous manner.