One-Bit Quantization Design and Channel Estimation for Massive MIMO Systems

We consider the problem of channel estimation for uplink multiuser massive MIMO systems, where, in order to significantly reduce the hardware cost and power consumption, one-bit analog-to-digital converters (ADCs) are used at the base station to quantize the received signal. In this paper, we study the problem of optimal one-bit quantization design for channel estimation in one-bit massive MIMO systems. Our analysis reveals that, if the quantization thresholds are optimally devised, using one-bit ADCs can achieve an estimation error close to (with an increase by a factor of $\pi /2$) that of an ideal estimator that has access to the unquantized data. Since the optimal quantization thresholds are dependent on the unknown channel parameters, we introduce an adaptive quantization scheme in which the thresholds are adaptively adjusted, and a random quantization scheme that randomly generates a set of thresholds based on some statistical prior knowledge of the channel. Simulation results show that our proposed schemes presents a significant performance improvement over the conventional fixed quantization scheme that uses a fixed (typically zero) threshold.