高效用户关联与无线调度及更短时间尺度速率自适应
Efficient User Association and Wireless Scheduling with Shorter Time-Scale Rate Adaptation
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中文总结 AI 辅助
针对无线网络中速率自适应时间尺度远短于用户关联调度的问题，提出集成虚拟队列与UCB的MaxWeight型联合算法及低复杂度PC替代方案，实现吞吐量最大化与公平性，理论保证次线性遗憾和零公平违规。
中文摘要 AI 辅助
速率自适应是IEEE 802.11网络和下一代蜂窝系统中的关键机制。由于速率自适应的时间尺度通常远短于用户关联和调度的时间尺度，我们研究了跨不同时间尺度的无线用户关联、调度与速率自适应的联合设计，以在确保用户间期望公平性的同时最大化累积网络吞吐量。我们开发了一种MaxWeight型用户关联和调度算法，该算法在其权重度量中整合了虚拟队列（用于跟踪每个用户的调度债务以维持公平性）和上置信界（UCB）估计。随后，每个被选中的用户采用UCB算法在短时间尺度上进行速率自适应。我们的理论发现表明，所提出的算法实现了累积遗憾，其随时间范围的平方根增长（至多相差一个对数因子），并在一定数量的时间帧后实现零累积公平性违规。此外，由于MaxWeight型算法涉及评估所有可行调度，而由于干扰约束，这些调度可能随用户数量呈指数增长，导致高计算复杂度，我们引入了一种利用所谓的选取并比较（PC）方法的低复杂度替代方案。我们通过基于真实数据轨迹的仿真证明了两种算法的有效性。
英文摘要
Rate adaptation is a crucial mechanism in IEEE 802.11 networks and next-generation cellular systems. Since the time scale for rate adaptation is typically much shorter than that for user association and scheduling, we investigate a joint design of wireless user association and scheduling and rate adaptation across different time scales to maximize cumulative network throughput while ensuring desired fairness among users. We develop a MaxWeight-type user association and scheduling algorithm that integrates virtual queues -- tracking each user's scheduling debt to maintain fairness -- and Upper Confidence Bound (UCB) estimates in its weight measure. Each selected user then employs the UCB algorithm for rate adaptation on a short time scale. Our theoretical findings reveal that the proposed algorithm achieves cumulative regret that grows with the square root of the time horizon up to a logarithmic factor and results in zero cumulative fairness violation after a certain number of time frames. Furthermore, since the MaxWeight-type algorithm involves evaluating all the feasible schedules that can be exponential to the number of users due to the interference constraints, leading to high computational complexity, we introduce a low-complexity alternative utilizing the so-called pick-and-compare (PC) approach. We demonstrate the effectiveness of both algorithms through simulations based on real-world data traces.
