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Bayesian Massive MIMO Channel Estimation with Parameter Estimation Using Low-Resolution ADCs

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Document pages: 5 pages

Abstract: In order to reduce hardware complexity and power consumption, massivemultiple-input multiple-output (MIMO) systems employ low-resolutionanalog-to-digital converters (ADCs) to acquire quantized measurements$ boldsymbol y$. This poses new challenges to the channel estimation problem,and the sparse prior on the channel coefficient vector $ boldsymbol x$ in theangle domain is often used to compensate for the information lost duringquantization. By interpreting the sparse prior from a probabilisticperspective, we can assume $ boldsymbol x$ follows certain sparse priordistribution 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 thenoise distribution parameters. In this paper, we treat both signals andparameters as random variables and recover them jointly within the AMPframework. The proposed approach leads to a much simpler parameter estimationmethod, allowing us to work with the quantization noise model directly.Experimental results show that the proposed approach achieves state-of-the-artperformance under various noise levels and does not require parameter tuning,making it a practical and maintenance-free approach for channel estimation.

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