Decoding for MIMO systems with correlated channel estimation errors

Abstract

We consider receiver design in uncoded multiple-input multiple-output (MIMO) wireless communication systems. Practical MIMO systems assume an accurate estimate of the channel state information (CSI) at the receiver; failure to estimate the channel accurately or to account for CSI errors in the receiver leads to poor performance in terms of high symbol error rate (SER) in data detection. Since CSI estimation at the receiver is often imperfect due to measurement errors, quantization errors and other sources of error, it is imperative that MIMO receivers account for CSI errors. In this paper, we design a MIMO receiver that considers channel estimation error; the only assumption we make about the error is its Gaussianity. We allow correlation to exist between channel estimation errors and propose a generalized robust maximum-likelihood (ML) decoder that is robust to CSI errors and is near optimal in the SER (i.e probability of symbol error) performance metric. Our proposed decoder has exponential complexity which is undesirable in practical systems. Hence, we also propose a recursive search algorithm to implement our generalized robust ML decoder with substantially lower computational complexity. For a 4×4 MIMO system with 256-QAM modulation and at SER of 10−3, our proposed generalized robust ML decoder has a coding loss of only 0.5 dB while searching through 2360 nodes (or less) compared to 65536 using the exact ML metric. This translates to up to 228 times fewer real multiplications and additions in the implementation.