用深度学习提升无线网络用户速率可靠性，防止信号衰落导致的速率暴跌。
Ultra-Reliable Risk-Aggregated Sum Rate Maximization via Model-Aided Deep Learning
- 基于风险控制的新型优化方法，用条件风险价值（CVaR）保障弱信号下的速率稳定。
- 训练出的αRGNN模型能完全消除用户在深衰落时的速率暴跌问题。
- 适合追求高可靠通信的5G/6G系统设计，尤其对实时性要求高的场景
在多输入单输出（MISO）下行无线网络中，我们关注加权和速率最大化问题，并特别强调用户速率的可靠性。提出一种新颖的风险聚合公式，利用条件风险价值（CVaR）作为函数，在信道衰落不确定性下强制实现速率（超）可靠性。建立所提预编码问题与加权风险规避均方误差（MSE）问题之间的类WMMSE等价关系，从而设计出定制化的展开图神经网络（GNN）策略函数近似（PFA），命名为α-鲁棒图神经网络（αRGNN）。该模型通过训练以最大化不利无线信道实现下的低尾部（CVaR）速率（如深衰落、衰减情况）。实证表明，训练后的αRGNN可完全消除用户级深速率衰落，显著且最优地降低统计用户速率波动，同时保持足够的遍历性能。
原文摘要 · Abstract (English)
We consider the problem of maximizing weighted sum rate in a multiple-input single-output (MISO) downlink wireless network with emphasis on user rate reliability. We introduce a novel risk-aggregated formulation of the complex WSR maximization problem, which utilizes the Conditional Value-at-Risk (CVaR) as a functional for enforcing rate (ultra)-reliability over channel fading uncertainty/risk. We establish a WMMSE-like equivalence between the proposed precoding problem and a weighted risk-averse MSE problem, enabling us to design a tailored unfolded graph neural network (GNN) policy function approximation (PFA), named α-Robust Graph Neural Network (αRGNN), trained to maximize lower-tail (CVaR) rates resulting from adverse wireless channel realizations (e.g., deep fading, attenuation). We empirically demonstrate that a trained αRGNN fully eliminates per user deep rate fades, and substantially and optimally reduces statistical user rate variability while retaining adequate ergodic performance.
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