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This is a Quantitative Study study.

October 9, 2025Open Access

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach

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A graph neural network outputs precoded quantized vectors, significantly increasing the achievable sum rate.
Using one-bit digital-to-analog converters achieves the same sum rate as maximum ratio transmission, reducing power consumption by a factor of 4-7.
The method is trained in a self-supervised manner, directly maximizing the achievable rate while addressing non-differentiability of DAC functions.
The approach maintains power reduction for bandwidths up to 3.5 MHz for baseband DACs and 2.9 for RF DACs, despite increased digital signal processing consumption.

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Implication

This analysis demonstrates that a graph neural network can optimize precoding in massive MIMO systems, highlighting significant reductions in power consumption.

Cite This Study

Feys et al. (2025) studied this question.

synapsesocial.com/papers/68e8439a9989581a2fd4e2a9 https://doi.org/10.48550/arxiv.2507.10634

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Implication

This analysis demonstrates that a graph neural network can optimize precoding in massive MIMO systems, highlighting significant reductions in power consumption.

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach

Key Points

Discussion

Implication

Cite This Study

Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach

Key Points

Discussion

Implication

Cite This Study

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