This paper addresses distributed throughput optimization for dense multi-AP IEEE P802.11bq networks. We develop a packet-level model that jointly captures cross-link carrier-sense multiple access with collision avoidance (CSMA/CA), sub-7GHz RTS/CTS exchange, beam-training overhead, directional mmWave interference, signal-to-interference-plus-noise-ratio (SINR)-based MCS selection, and retransmissions. The resulting configuration problem is formulated as a multi-group combinatorial multi-armed bandit (CMAB), where each AP selects its contention window, clear-channel assessment threshold, beamwidth, and MCS reservation margin from finite candidate sets. Inspired by combinatorial successive accept-reject methods, we propose a group-wise feasible CSAR variant that uses Hadamard-guided feasible exploration to estimate empirical ranking scores and eliminate low-performing candidates within each parameter group. Simulations show that the proposed scheme improves aggregate and per-AP throughput over the considered Thompson-sampling baseline across most AP densities and reduces throughput stabilization time by approximately 49$\%$ under the evaluated settings. The learned configurations reveal that high throughput requires a balance among control-channel aggressiveness, mmWave spatial reuse, beam-training cost, and MCS robustness, rather than simply minimizing collisions or maximizing the PHY rate.

翻译：本文针对密集多AP IEEE P802.11bq网络中的分布式吞吐量优化问题展开研究。我们建立了一个数据包级模型，该模型联合捕捉跨链路载波侦听多路访问/冲突避免（CSMA/CA）、Sub-7GHz RTS/CTS交互、波束训练开销、定向毫米波干扰、基于信干噪比（SINR）的MCS选择以及重传机制。相应的配置问题被形式化为多组组合多臂老虎机（CMAB）问题，其中每个AP从有限候选集中选择其竞争窗口、空闲信道评估阈值、波束宽度和MCS预留余量。受组合式连续接受-拒绝方法启发，我们提出了一种分组可行的CSAR变体，该方法利用Hadamard引导的可达探索来估计经验排名得分，并在每个参数组内淘汰低性能候选方案。仿真结果表明，与所考虑的汤普森采样基线相比，所提方案在大多数AP密度下都能提升总吞吐量和单AP吞吐量，且在评估设置下将吞吐量稳定时间缩短约49%。学习到的配置揭示了高吞吐量需要在控制信道侵占性、毫米波空间复用、波束训练开销与MCS鲁棒性之间取得平衡，而非单纯最小化冲突或最大化物理层速率。