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The use of low precision (e.g., 1-3 bits) analog-to-digital convenors (ADCs)in very large multiple-input multiple-output (MIMO) systems is a technique toreduce cost and power consumption. In this context, nevertheless, it has beenshown that the training duration is required to be {\em very large} just toobtain an acceptable channel state information (CSI) at the receiver. Apossible solution to the quantized MIMO systems is joint channel-and-data (JCD)estimation. This paper first develops an analytical framework for studying thequantized MIMO system using JCD estimation. In particular, we use theBayes-optimal inference for the JCD estimation and realize this estimatorutilizing a recent technique based on approximate message passing. Large-systemanalysis based on the replica method is then adopted to derive the asymptoticperformances of the JCD estimator. Results from simulations confirm ourtheoretical findings and reveal that the JCD estimator can provide asignificant gain over conventional pilot-only schemes in the quantized MIMOsystem.

Joint Channel-and-Data Estimation for Large-MIMO Systems with Low-Precision ADCs