面向IEEE 802.11ax的上行OFDMA与MU-MIMO联合优化：深度分层强化学习方法
Joint Optimization on Uplink OFDMA and MU-MIMO for IEEE 802.11ax: Deep Hierarchical Reinforcement Learning Approach
AI总结：
针对IEEE 802.11ax上行OFDMA与MU-MIMO联合优化的组合难题，提出定制化深度分层强化学习方法，在非饱和流量场景下显著提升了吞吐量与收敛速度。
摘要：
本文研究IEEE 802.11ax中融合上行正交频分多址（OFDMA）的联合用户调度与频率资源分配（USRA）、单用户MIMO与多用户（MU）MIMO之间的多输入多输出模式选择（MIMO MS）以及MU-MIMO用户选择问题。具体而言，我们聚焦于用户数据需求波动的非饱和流量场景。在非饱和流量条件下，考虑每个用户的数据包量会引入一个组合优化问题，需要同时沿时-频-空轴优化MU-MIMO用户选择和资源分配。因此，该问题的组合特性（以未知变量基数大为特征）带来了挑战，传统优化方法几乎无法解决。对此，本文提出一种深度分层强化学习（DHRL）方法来求解该联合问题。我们并非简单采用现成的DHRL，而是针对USRA与MS联合问题定制了DHRL，从而显著提升了收敛速度和吞吐量。大量仿真结果表明，在多种非饱和流量场景下，所提算法相比现有方案实现了吞吐量的显著提升。
英文摘要：
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.
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