A Low Complexity Post-Processing SNR Estimator for Non-Linear Receiver in MIMO Systems

The use of spatial multiplexing in multi-input multi-output (MIMO) systems can provide a substantial gain in system throughout. In such systems it is desirable to obtain the post-processing signal to noise ratios (SNRs) of the multiplexed substreams so that channel state information (CSI) can be further derived. A related study shows that if a non-linear receiver is adopted, the post-processing SNRs are determined by the minimum squared Euclidian distance between signal points at the receiver side. In this paper, we design a low complexity algorithm to estimate the minimum squared Euclidian distance using QR decomposition and M-algorithm. Post-processing SNRs are then calculated based on the estimates. Monte Carlo simulations show that under different randomly generated MIMO channel matrices, the new algorithm provides reliable measurements of the post-processing SNRs.