Iterative Approaches for Massive MIMO Uplink Processing Under Imperfect Channel Conditions

Obtaining channel state information in massive multiple-input multiple-output (MIMO) systems is challenging because of significant multiple-access interference and very limited resources available for channel estimation. Most existing equalization schemes for massive MIMO systems assume perfect channel knowledge at the receivers. When channel estimation error is present, however, significant performance degradation will be experienced, which has been demonstrated in the literature. In this paper, we model the maximum-likelihood channel estimation as the sum of real channel and random channel estimation error, and develop a minimum mean-square error (MMSE) based turbo equalization method conditioned on the channel estimate. We demonstrate that our newly developed iterative processing approach is a general expression, whereas existing methods can be viewed as special cases. We further derive an MMSE-sorted QR decomposition (MMSE-SQRD) based turbo equalization to further improve the performance for massive MIMO systems under large user numbers and severe channel estimation error conditions. Numerical results and performance comparison show that the proposed MMSE-based turbo equalization scheme greatly outperforms the existing schemes. With a large number of users being simultaneously served, our proposed MMSE-SQRD-based scheme can further enhance system performance over the MMSE-based turbo equalization scheme.