To support future diverse applications, multi-link operation (MLO) has been introduced in the Wi-Fi 7 standard (IEEE 802.11be) to enable concurrent communication over multiple frequency bands. This new capability relies on a two-tier medium access control (MAC) architecture, where the upper MAC (U-MAC) allocates traffic across links and the lower MAC (L-MAC) performs independent channel access. However, MLO optimization is challenging due to the inherent coupling between the U-MAC and L-MAC, as well as the dynamic and complex nature of wireless networks. To address these challenges, we propose a cross-layer framework that jointly optimizes traffic allocation at the U-MAC layer and initial contention window (ICW) sizes at the L-MAC layer to maximize network throughput. Specifically, we extend the single-link Bianchi Markov model to develop an analytical framework that captures the relationship among network throughput, traffic allocation, and ICW sizes. Based on this framework, we formulate a nonconvex, nonlinear cross-layer optimization problem. To solve it efficiently, we design a long short-term memory-based soft actor-critic (LSTM-SAC) algorithm that leverages LSTM to handle the partial observability and non-Markovian dynamics inherent in Wi-Fi networks. Finally, using a well-developed event-based Wi-Fi simulator, we demonstrate that the proposed LSTM-SAC substantially outperforms existing benchmark solutions across a wide range of network settings.

翻译：为支持未来多样化应用，Wi-Fi 7标准（IEEE 802.11be）引入了多链路操作（MLO），使其能够在多个频段上实现并发通信。这一新能力依赖于双层介质访问控制（MAC）架构，其中上层MAC（U-MAC）负责跨链路流量分配，下层MAC（L-MAC）执行独立信道接入。然而，由于U-MAC与L-MAC之间存在固有耦合性，加之无线网络的动态性与复杂性，MLO优化面临挑战。针对上述问题，本文提出一种跨层框架，联合优化U-MAC层的流量分配与L-MAC层的初始竞争窗口（ICW）大小，以最大化网络吞吐量。具体而言，我们扩展了单链路Bianchi马尔可夫模型，开发了一个能够刻画网络吞吐量、流量分配与ICW大小之间关系的分析框架。基于该框架，我们形式化了一个非凸、非线性的跨层优化问题。为高效求解该问题，本文设计了一种基于长短期记忆网络的软演员-评论家（LSTM-SAC）算法，利用LSTM处理Wi-Fi网络固有的部分可观测性与非马尔可夫动态特性。最后，通过使用成熟的基于事件的Wi-Fi仿真器，我们验证了所提LSTM-SAC算法在多种网络场景下均显著优于现有基准解决方案。