Joint Optimization on Uplink OFDMA and MU-MIMO for IEEE 802.11ax: Deep Hierarchical Reinforcement Learning Approach

This letter tackles a joint user scheduling, frequency resource allocation (USRA), multi-input-multi-output mode selection (MIMO MS) between single-user MIMO and multi-user (MU) MIMO, and MU-MIMO user selection problem, integrating uplink orthogonal frequency division multiple access (OFDMA) in IEEE 802.11ax. Specifically, we focus on \textit{unsaturated traffic conditions} where users' data demands fluctuate. In unsaturated traffic conditions, considering packet volumes per user introduces a combinatorial problem, requiring the simultaneous optimization of MU-MIMO user selection and RA along the time-frequency-space axis. Consequently, dealing with the combinatorial nature of this problem, characterized by a large cardinality of unknown variables, poses a challenge that conventional optimization methods find nearly impossible to address. In response, this letter proposes an approach with deep hierarchical reinforcement learning (DHRL) to solve the joint problem. Rather than simply adopting off-the-shelf DHRL, we \textit{tailor} the DHRL to the joint USRA and MS problem, thereby significantly improving the convergence speed and throughput. Extensive simulation results show that the proposed algorithm achieves significantly improved throughput compared to the existing schemes under various unsaturated traffic conditions.