arXiv 2211.147752022-11eess.SP

基于GNN与CNN的预编码策略学习性能理解

Understanding the Performance of Learning Precoding Policy with GNN and CNNs

Baichuan Zhao, Jia Guo, Chenyang Yang

AI总结：

该研究分析预编码策略性质与神经网络归纳偏置，引入GNN学习预编码策略并对比CNNs，解释性能差异原因，通过仿真验证分析并评估GNN泛化能力。

摘要：

基于学习的预编码已被证实可实时实现，能与信道获取联合优化，且对非理想信道具备鲁棒性。但现有研究极少解释设计选择与学习性能，且现有方法要么训练复杂度高，要么依赖特定问题的模型。本文通过分析预编码策略的性质与神经网络的归纳偏置来解决这些问题，研究发现学习性能可分解为近似误差与估计误差，前者与策略的平滑性相关，且两者均取决于神经网络的归纳偏置。为此，我们引入图神经网络（graph neural network, GNN）来学习预编码策略，并分析其与常用卷积神经网络（convolutional neural networks, CNNs）的关联。以和速率最大化预编码策略为例，我们解释了学习到的预编码策略在低信噪比场景、空间非相关信道以及用户数远少于天线数时表现优异的原因，同时阐明了GNN比CNNs学习效率更高的原因。大量仿真验证了我们的分析，并评估了GNN的泛化能力。

英文摘要：

Learning-based precoding has been shown able to be implemented in real-time, jointly optimized with channel acquisition, and robust to imperfect channels. Yet previous works rarely explain the design choices and learning performance, and existing methods either suffer from high training complexity or depend on problem-specific models. In this paper, we address these issues by analyzing the properties of precoding policy and inductive biases of neural networks, noticing that the learning performance can be decomposed into approximation and estimation errors where the former is related to the smoothness of the policy and both depend on the inductive biases of neural networks. To this end, we introduce a graph neural network (GNN) to learn precoding policy and analyze its connection with the commonly used convolutional neural networks (CNNs). By taking a sum rate maximization precoding policy as an example, we explain why the learned precoding policy performs well in the low signal-to-noise ratio regime, in spatially uncorrelated channels, and when the number of users is much fewer than the number of antennas, as well as why GNN is with higher learning efficiency than CNNs. Extensive simulations validate our analyses and evaluate the generalization ability of the GNN.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

AI总结：

摘要：

英文摘要：

相关论文