Throughput Optimization for Multi-AP IEEE P802.11bq Networks Based on Combinatorial Multi-Armed Bandits

2026-06-02

AI Summary

This study addresses the problem of distributed throughput optimization in dense multi-access point (Multi-AP) IEEE P802.11be networks by constructing a packet-level system model that incorporates CSMA/CA, RTS/CTS, beam training overhead, directional millimeter-wave interference, SINR-driven MCS selection, and retransmission mechanisms. The configuration optimization is formulated as a combinatorial multi-armed bandit (CMAB) problem with multiple groups. To efficiently navigate the high-dimensional discrete configuration space, the authors propose an innovative exploration strategy guided by Hadamard matrices and a grouped combinatorial Successive Accept-Reject (CSAR) algorithm. Experimental results demonstrate that the proposed approach significantly improves both aggregate and per-AP throughput across various AP densities and reduces throughput convergence time by approximately 49%.

Abstract

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.

Problem

• throughput optimization
• multi-AP networks
• IEEE P802.11bq
• combinatorial multi-armed bandits
• dense wireless networks

Innovation

• Combinatorial Multi-Armed Bandits
• Throughput Optimization
• mmWave Networks
• Distributed Configuration
• CSAR Algorithm