Cross-Layer Traffic Allocation and Contention Window Optimization for Wi-Fi 7 MLO: When DRL Meets LSTM

2026-03-19

AI Summary

This work addresses the challenge of throughput maximization in Wi-Fi 7 Multi-Link Operation (MLO), where upper-layer MAC traffic allocation and lower-layer MAC contention window configuration are tightly coupled and significantly influenced by dynamic wireless conditions. To tackle this, the paper proposes a cross-layer optimization framework that jointly optimizes traffic distribution across links and the initial contention window size. Leveraging an extended Bianchi Markov model, the authors formulate the problem as a non-convex nonlinear optimization based on an analytical throughput relationship. To handle partial observability and non-Markovian dynamics inherent in real-world environments, they innovatively integrate an LSTM-enhanced Soft Actor-Critic (LSTM-SAC) deep reinforcement learning algorithm. Experimental results demonstrate that the proposed approach consistently outperforms existing benchmarks across diverse network scenarios, achieving substantial improvements in Wi-Fi 7 MLO throughput.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Learning to Search
• Machine Learning: Mixture of Experts (MoE)

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Multi-Link Operation
• Cross-Layer Optimization
• Contention Window
• Traffic Allocation
• Wi-Fi 7

Innovation

• Cross-layer optimization
• Multi-link operation (MLO)
• Contention window adaptation
• LSTM-SAC
• Wi-Fi 7