Simplified Robust Adaptive Detection and Beamforming for Wireless Communications

4Robust RLS Adaptive Algorithms

4.1 Introduction

A linear receiver can be designed by minimizing some inverse filtering criterion [1–3]. Appropriate constraints are used to avoid the trivial all‐zero solution. A well‐known cost function for the constrained optimization problem is the variance or the power of the output signal. A minimum output energy (MOE) detector has been developed for multiuser detection based on the constrained optimization approach [3]. In an additive white Gaussian noise (AWGN) environment with no multipath effects, this detector provides a blind solution with MMSE performance. Unfortunately, the approach experiences performance degradation in the presence of signal mismatch, inter‐chip interference, and multipath propagation [4]. An improved constrained optimization approach to handle multipath fading uses only the main multipath component [4]; other delayed components are forced to zero. This approach is not the optimal solution. Although it can handle the multipath case, but it does not maximize the signal‐to‐interference noise ratio (SINR). An optimal solution [5] obtains the constrained vector by a max/min approach. The theoretical performance of this method tends to be close to the optimal non‐blind MMSE receiver at high signal‐to‐noise ratios (SNR) in the presence of multipath fading. Adaptive implementation algorithms for this method have been developed [6–8]. Unfortunately, this method requires eigenvalue decomposition, which adds higher complexity. ...