Joint cHANNEL-AND-dATA estimation for large-MIMO systems with low-precision ADCs

The use of low precision (e.g., 1 - 3 bits) analog-to-digital converters (ADCs) in very large multiple-input multiple-output (MIMO) systems is a technique to reduce cost and power consumption. In this context, nevertheless, it has been shown that the training duration is required to be very large just to obtain an acceptable channel state information (CSI) at the receiver. A possible solution to the MIMO system with low precision ADCs is joint channel-and-data (JCD) estimation. This paper first develops an analytical framework for studying the MIMO system using JCD estimation. In particular, we use the Bayes-optimal inference for the JCD estimation and realize this estimator utilizing a recent technique based on approximate message passing. Large-system analysis based on the replica method is then adopted to derive the asymptotic performances of the JCD estimator. Results from simulations confirm our theoretical findings and reveal that the JCD estimator can provide a significant gain over conventional pilot-only schemes in the MIMO system.