用深度学习优化加权速率，兼顾用户公平与性能
Precoder Learning for Weighted Sum Rate Maximization
- 设计具备联合酉对称性的神经网络，提升学习效率
- 在多种信道条件下，性能优于传统学习方法
- 适合通信系统中需平衡多用户速率的场景
加权和速率最大化（WSRM）的预编码优化能有效平衡用户间的性能与公平性。近期研究显示深度学习在和速率最大化预编码优化中具有潜力。然而，WSRM问题需要重新设计神经网络架构以融入用户权重。本文提出一种新型深度神经网络（DNN）用于学习WSRM的预编码器。相比现有DNN，所提DNN利用最优预编码策略中固有的联合酉对称性和排列等变性，显著提升学习性能并降低训练复杂度。仿真结果表明，该方法在学习和泛化性能上均显著优于基线学习方法，同时保持低训练与推理复杂度。
原文摘要 · Abstract (English)
Weighted sum rate maximization (WSRM) for precoder optimization effectively balances performance and fairness among users. Recent studies have demonstrated the potential of deep learning in precoder optimization for sum rate maximization. However, the WSRM problem necessitates a redesign of neural network architectures to incorporate user weights into the input. In this paper, we propose a novel deep neural network (DNN) to learn the precoder for WSRM. Compared to existing DNNs, the proposed DNN leverage the joint unitary and permutation equivariant property inherent in the optimal precoding policy, effectively enhancing learning performance while reducing training complexity. Simulation results demonstrate that the proposed method significantly outperforms baseline learning methods in terms of both learning and generalization performance while maintaining low training and inference complexity.
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