基于竞争驱动特征提取的WLAN信道分配惩罚分散上下文Bandit学习
Penalized and Decentralized Contextual Bandit Learning for WLAN Channel Allocation with Contention-Driven Feature Extraction
摘要：
在这项研究中，一个基于上下文多臂强盗（CMAB）的分散式信道探索框架解开了信道效用函数（即，奖励）。所提出的框架使得AP能够对竞争AP的观察到的奖励进行组合评估，从而允许对由于相邻AP的变化信道引起的奖励波动的鲁棒性和对甚至未探索的信道的评估。为了实现这一框架，我们提出了竞争驱动的特征提取（CDFE），它提取竞争下的AP之间的邻接关系，并形成了以解纠缠形式表示奖励函数的基础，即与竞争下的相邻AP相关联的参数的线性组合。这允许CMAB与联合线性置信上限（JLinUCB）探索一起利用，并深入研究所提出的框架的有效性。此外，我们解决了不收敛的问题-信道探索周期-提出了一个惩罚JLinUCB（P-JLinUCB）的基础上引入一个折扣参数的奖励之前和之后的学习轮利用不同的信道的关键思想。数值计算结果表明，该方法允许AP评估信道质量鲁棒性对奖励波动的CDFE和实现更好的收敛性能的P-JLinUCB。
英文摘要：
In this study, a contextual multi-armed bandit (CMAB)-based decentralized channel exploration framework disentangling a channel utility function (i.e., reward) with respect to contending neighboring access points (APs) is proposed. The proposed framework enables APs to evaluate observed rewards compositionally for contending APs, allowing both robustness against reward fluctuation due to neighboring APs' varying channels and assessment of even unexplored channels. To realize this framework, we propose contention-driven feature extraction (CDFE), which extracts the adjacency relation among APs under contention and forms the basis for expressing reward functions in the disentangled form, that is, a linear combination of parameters associated with neighboring APs under contention). This allows the CMAB to be leveraged with joint a linear upper confidence bound (JLinUCB) exploration and to delve into the effectiveness of the proposed framework. Moreover, we address the problem of non-convergence -- the channel exploration cycle -- by proposing a penalized JLinUCB (P-JLinUCB) based on the key idea of introducing a discount parameter to the reward for exploiting a different channel before and after the learning round. Numerical evaluations confirm that the proposed method allows APs to assess the channel quality robustly against reward fluctuations by CDFE and achieves better convergence properties by P-JLinUCB.
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。
