Throughput Optimization for Multi-AP IEEE P802.11bq Networks Based on Combinatorial Multi-Armed Bandits
For network engineers and researchers, this work provides a practical method for optimizing throughput in dense multi-AP mmWave networks, though the approach is incremental.
This paper tackles distributed throughput optimization in dense multi-AP IEEE P802.11bq networks by formulating it as a combinatorial multi-armed bandit problem and proposing a group-wise feasible CSAR algorithm. The scheme improves aggregate and per-AP throughput over a Thompson-sampling baseline and reduces stabilization time by ~49%.
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.