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Optimal Quantized Multi-Cell MMSE Precoding With Low Resolution Data Converters
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DOI：10.1109/LCOMM.2024.3509916.png)
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Abstract
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This work considers a multi-cell multi-user multiple-input multiple-output (MIMO) system that employs low resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) at each base station (BS) to limit the power consumption. Existing precoder designs for quantization-free systems are sub-optimal for such quantized systems, while the existing precoder designs for quantized systems consider single-cell settings and perfect channel state information (CSI). To address these gaps, we study the downlink linear precoder optimization problem in a cellular system under the distortions introduced by low resolution DACs based on a minimum mean square error (MMSE) approach, while accounting for imperfect CSI obtained in the uplink under distortions introduced by low resolution ADCs. The problem is analytically solved resulting in an optimal quantized multi-cell MMSE precoder that reduces both intra-cell and inter-cell interference under quantization errors, and yields better bit error rate performance than applying the existing conventional multi-cell and quantized single-cell linear precoders to a quantized multi-cell massive MIMO system.
Keywords:
Quantization (signal)
Radio frequency
Training
Signal resolution
Distortion
Channel estimation
Noise
Massive MIMO
Intercell interference
Precoding
Multi-cell massive multiple-input multiple-output
quantization
data converters linear precoding
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4.4
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1.3W
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2.2W
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