多通道多址接入中自动链路选择
Automatic Link Selection in Multi-Channel Multiple Access with Link Failures
AI总结：
本文提出两种自适应算法，用于多通道多址接入中的链路选择问题，分别具有不同的收敛速度和实现效率。
摘要：
本文聚焦于利用带隙反馈进行多通道多址接入中的自动链路选择问题。具体而言，控制器在时间分组系统中将多个用户分配到多个通道，其中每个时间槽内最多只能有一个用户被分配到一个给定的通道，且每个用户最多只能被分配到一个通道。给定用户i被分配到通道j时，传输失败的概率为固定但未知的1-q_{i,j}。这些分配是动态进行的，基于成功/失败的反馈。目标是最大化时间平均效用，其中考虑的是任意（可能非光滑）凹、逐元素非递减的效用函数。所提出的第一个算法具有快速的O(√(log(T)/T))收敛性。然而，该算法需要在每个迭代中求解一个凸优化问题，这可能计算成本较高。第二个算法具有较慢的O(√[3](log(T)/T))收敛性，同时避免了昂贵的内部优化。这两种算法都是自适应的。特别是，收敛保证适用于任何连续的T个时间槽期间，只要成功概率不发生变化。我们进一步研究了几种特殊情形。在单通道情况下，我们获得了快速O(√(log(T)/T))收敛性和高效的实现方法，通过更简单的自适应机制。我们还考虑了基于UCB的非自适应算法，采用最大权型决策。仿真结果突显了引人注目的性能权衡，并展示了所提出自适应方案的快速适应性。
英文摘要：
This paper focuses on the problem of automatic link selection in multi-channel multiple access control using bandit feedback. In particular, a controller assigns multiple users to multiple channels in a time-slotted system, where in each time slot, at most one user can be assigned to a given channel, and at most one channel can be assigned to a given user. Given that user $i$ is assigned to channel $j$, the transmission fails with a fixed unknown probability $1-q_{i,j}$. The assignments are made dynamically using success/failure feedback. The goal is to maximize the time-average utility, where we consider an arbitrary (possibly nonsmooth) concave, entrywise nondecreasing utility function. The first proposed algorithm has fast $\mathcal{O}(\sqrt{\log(T)/T})$ convergence. However, this algorithm requires solving a convex optimization problem within each iteration, which can be computationally expensive. The second algorithm has slower $\mathcal{O}(\sqrt[3]{\log(T)/T})$ convergence, while avoiding the costly inner optimization. Both of these algorithms are adaptive. In particular, the convergence guarantee holds for any interval of $T$ consecutive slots during which the success probabilities do not change. We further study several special cases. In the single-channel setting, we obtain both fast $\mathcal{O}(\sqrt{\log(T)/T})$ convergence and efficient implementation via a simpler adaptive mechanism. We also consider a UCB-based non-adaptive algorithm with max-weight-type decisions. Simulations highlight intriguing performance trade-offs and demonstrate rapid adaptation of the proposed adaptive schemes.
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。
