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

TL;DR

This paper introduces a novel cross-layer optimization framework for Wi-Fi 7 MLO that combines traffic allocation and contention window tuning, employing LSTM-SAC to adaptively maximize throughput in complex wireless environments.

Contribution

It develops an analytical model linking throughput, traffic, and contention windows, and proposes an LSTM-SAC algorithm to solve the nonconvex optimization problem effectively.

Findings

LSTM-SAC significantly improves network throughput over benchmarks.

The framework effectively adapts to dynamic wireless conditions.

Analytical model accurately captures the relationship among key network parameters.

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.