Probabilistic Massive MIMO Channel Estimation with Built-in Parameter Estimation
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In order to reduce hardware complexity and power consumption, massive multiple-input multiple-output (MIMO) system uses low-resolution analog-to-digital converters (ADCs) to acquire quantized measurements y. This poses new challenges to the channel estimation problem, and the sparse prior on the channel coefficients x in the angle domain is used to compensate for the information lost during quantization. By interpreting the sparse prior from a probabilistic perspective, we can assume x follows some sparse prior distribution and recover it using approximate message passing (AMP). However, the distribution parameters are unknown in practice and need to be estimated. Due to the increased computational complexity in the quantization noise model, previous works either use an approximated noise model or manually tune the noise distribution parameters. In this paper we treat both the signal and parameters as random variables and recover them jointly within the AMP framework. This leads to a much simpler parameter estimation method and allows us to work with the true quantization noise model. Experimental results show that the proposed approach achieves state-of-the-art performance under various noise levels and does not require parameter tuning, making it a practical and carefree approach for channel estimation.
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