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Computationally Efficient Maximum Likelihood Channel Estimation for Coarsely Quantized Massive MIMO Systems
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DOI：10.1109/LCOMM.2021.3133705.png)
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Abstract
En 中文
We consider computationally efficient maximum likelihood (ML) channel estimation for massive multiple-input multiple-output (MIMO) systems using coarsely quantized measurements obtained from low-resolution analog-to-digital converters (ADCs) at the receivers. We first devise a computationally efficient ML estimator (referred to as CQML) by using the cyclic optimization and majorization-minimization (MM) techniques. Then, we show the connections between CQML and the conventional unquantized ML estimator. Numerical examples are provided to demonstrate the effectiveness and computational efficiency of the proposed channel estimation algorithm.
Keywords:
Maximum likelihood estimation
Channel estimation
Massive MIMO
Quantization (signal)
Signal processing algorithms
Convex functions
Convergence
Low-resolution analog-to-digital converters (ADCs)
massive multiple-input multiple-output (MIMO) communications
maximum likelihood (ML) channel estimation
majorization-minimization (MM)
Journal
IF:
4.4
Papers:
1.3W
Citations:
2.2W