用压缩技术让深度学习预编码更省电，实测能效提升35倍。
Compression of Site-Specific Deep Neural Networks for Massive MIMO Precoding
- 结合精度量化与神经网络搜索，压缩模型降低能耗。
- 在相同性能下，能效比WMMSE高35倍，依场景而定。
- 适合研究低功耗通信系统或部署AI预编码的工程师。
将深度学习模型用于大规模多输入多输出（mMIMO）系统预编码时，常受限于高计算开销和能耗。本文研究基于DL的mMIMO预编码器的计算能效，对比传统方法如零强迫（zero forcing）和加权最小均方误差（WMMSE）。所提出的能量消耗模型涵盖DL加速器中的内存访问与计算能耗。我们提出一个框架，融合混合精度量化感知训练与神经架构搜索，在不损失精度的前提下降低能耗。利用覆盖多个基站站点的射线追踪数据集，分析了站点特异性条件对压缩模型能效的影响。结果表明，在同等性能下，深度神经网络压缩可使预编码器能效最高达WMMSE的35倍，具体取决于场景与目标速率。这些成果为高效能的基于深度学习的mMIMO预编码器开发奠定了基础与基准。
原文摘要 · Abstract (English)
The deployment of deep learning (DL) models for precoding in massive multiple-input multiple-output (mMIMO) systems is often constrained by high computational demands and energy consumption. In this paper, we investigate the compute energy efficiency of mMIMO precoders using DL-based approaches, comparing them to conventional methods such as zero forcing and weighted minimum mean square error (WMMSE). Our energy consumption model accounts for both memory access and calculation energy within DL accelerators. We propose a framework that incorporates mixed-precision quantization-aware training and neural architecture search to reduce energy usage without compromising accuracy. Using a ray-tracing dataset covering various base station sites, we analyze how site-specific conditions affect the energy efficiency of compressed models. Our results show that deep neural network compression generates precoders with up to 35 times higher energy efficiency than WMMSE at equal performance, depending on the scenario and the desired rate. These results establish a foundation and a benchmark for the development of energy-efficient DL-based mMIMO precoders.
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