Adaptive Minimum Variance Detection for Spatial Multiplexing over Time-Varying MIMO Channels

Abstract

In this paper, we present an efficient detection scheme for spatial multiplexing over time-varying multiple-input multiple-output (MIMO) channels. The filter weights for detection are optimized by the minimum variance (MV) criterion. The resultant MV detector is adaptively realized with the generalized sidelobe canceller (GSC) structure. With the up-to-date channel state information (CSI), we show that the signal matched filter and blocking matrix in GSC can be determined through the use of the Householder transformation. The advantage of this approach is that it avoids the abrupt changes in the adaptive process whenever the signal matched filter and blocking matrix are updated according to the changing MIMO channel. All adaptation operations are implemented with the least-mean- square (LMS) algorithm. Simulations reveal that the proposed adaptive MV detector can outperform the conventional adaptive minimum mean-squared-error (MMSE) detector in time-varying channel environments.