Adaptive iterative decision feedback with constellation constraints and soft-output detection for MIMO systems

Abstract

A low-complexity adaptive iterative decision feedback detection algorithm with constellation constraints (DFCC) is proposed for MIMO systems. In order to reduce the complexity of the DF processing with time-varying channels, the proposed receiver updates the filter weights by using a recursive least squares (RLS) based algorithm. An enhanced adaptive detection and interference cancellation is achieved by introducing multiple constellation points as decision candidates. A complexity reduction strategy is also developed to avoid redundant processing with reliable decisions. By using the tentative decisions, the soft-output is obtained. This highly efficient detector is also incorporated with a multiple branch (MB) architecture to achieve a higher detection diversity order. Simulations show that the proposed DFCC technique has a complexity as low as the adaptive DF detector while it achieves a significant performance gain and approaches the optimal performance.