Linear Minimum Mean-Squared Error Channel Estimation for Per-Subcarrier Antenna Selection

Abstract

The linear minimum mean-squared error (MSE) channel estimator for systems employing per-subcarrier transmit antenna selection is developed. It is shown that the frequency domain correlations after the selection process is approximated well using a simple explicit function. Performance of resultant estimators, which assume a certain correlation model, deteriorates quickly at high signal-to-noise ratios (SNR) when model errors exist. To overcome this deficiency, robust estimators based on the principles of minimising the worst case and expected MSEs over the possible model set are developed. Solution to the latter is given in closed form, and is seen to achieve MSE optimal performance at low SNR while the high SNR performance is the same as that of the least-squares (LS) estimator. The optimal estimator with the proposed correlation function and correct model knowledge performs about 3 dB better than the LS estimator at a packet-error-rate of 0.01 for a channel coded system employing 16-QAM modulations, whereas in the presence of model errors, the latter robust estimator is performing about 2 dB better than the LS estimator.