Joint channel estimation and soft-symbol detection in massive MIMO systems with low resolution ADCs

In this talk, we present a variational Bayes’ algorithm for joint channel estimation and soft symbol decoding in an uplink massive multiple input multiple output (MIMO) receiver with low resolution analog to digital converters (ADCs). The posterior beliefs obtained from the algorithm can be easily used to compute the bit log likelihood ratios, which can be input to a channel decoder. We evaluate the symbol error probability and the normalized mean squared error of the channel estimates of the proposed algorithm using Monte Carlo simulations, and benchmark it against an unquantized variational Bayesian algorithm with perfect and imperfect channel state information (CSI). Also, we empirically show that the perfect CSI assumption that is considered in a few low-resolution ADC based massive MIMO papers greatly overestimates the performance of the system. This is joint work with Sai Subramanyam Thoota and Ramesh Annavajjala.