基于状态增强图神经网络的长周期无线链路调度
Long-Horizon Wireless Link Scheduling with State-Augmented Graph Neural Networks
浏览论文内容
中文总结 AI 辅助
研究大规模无线网络最优链路调度，用拉格朗日对偶性求解约束优化问题，提出迭代算法，通过图神经网络参数化调度决策，采用状态增强技术，经数值模拟验证方法有效性。
中文摘要 AI 辅助
我们研究大规模无线网络中的最优链路调度问题。目标是在一个时间范围内安排传输，以最大化总速率，同时确保每个用户的平均速率达到最低速率要求。为此，我们制定了一个约束优化问题，并使用拉格朗日对偶性来解决它。常见的原始对偶方法会导致时间不变的策略。我们的约束要求所有链路在一定时间内进行传输并避免干扰，这需要时隙间的时变策略。我们提出了一种迭代算法来采样最优调度序列和对偶变量。调度决策通过图神经网络进行参数化。我们采用状态增强技术来学习这种参数化，将对偶变量作为策略的动态输入。这种增强使图神经网络能够随时间调整调度决策，在满足约束和性能最大化之间取得平衡。我们通过广泛的数值模拟验证了我们的方法，与多个基线进行比较，并考虑了不同的约束水平。
英文摘要
We address optimal link scheduling in large-scale wireless networks. The goal is to schedule transmissions over a time horizon so that to maximize sum rate while ensuring that average rates of each customer attain a minimum rate requirement. To this end, we formulate a constrained optimization problem and solve it using Lagrangian duality. Common primal-dual approaches lead to time invariant policies. Our constraint requires all links transmit a fraction of the time while avoiding interference, which calls for time-varying policies across time slots. We propose an iterative algorithm to sample optimal sequences of schedules and dual variables. The scheduling decisions are parameterized using a Graph Neural Network. We incorporate state-augmentation techniques to learn said parameterization, introducing dual variables as dynamic inputs to the policy. This augmentation enables the GNN to adapt scheduling decisions over time, balancing constraint satisfaction with performance maximization. We validate our approach through extensive numerical simulations, benchmarking against several baselines and considering varying constraint levels.
