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

Zhang Liu, Xianbin Wang, Shumin Lian, Lianfen Huang, Liqun Fu, Ying-Jun Angela Zhang

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.

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

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.
Paper Structure (39 sections, 13 equations, 7 figures, 3 tables)

This paper contains 39 sections, 13 equations, 7 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background and Overview
  3. Motivation and Main Challenges
  4. Summary of Contributions
  5. Paper Organization
  6. Related Work
  7. Traffic Allocation in Wi-Fi 7 MLO
  8. Contention Window Optimization in Wi-Fi 7 MLO
  9. Usage of Deep Reinforcement Learning in Wi-Fi
  10. System Model and Problem Formulation
  11. Carrier-Sense Multiple Access with Collision Avoidance
  12. Transmission Mode in Multi-Link Operation
  13. Network Outline
  14. Throughput Analysis via Bianchi Markov Chain
  15. Problem Formulation
  16. ...and 24 more sections

Figures (7)

  • Figure 1: A schematic illustration of the MAC instance in MLO, where each L-MAC in a different color represents a distinct band corresponding to one of the multiple links. In addition, the traffic allocated to each link is highlighted in red, while the channel occupancy is shown in black.
  • Figure 2: Non-Markovian nature of Wi-Fi networks: The throughput of the current traffic flow depends not only on the present state but is also influenced by subsequent flow arrivals.
  • Figure 3: Markov chain model for the backoff stage $q_{n,l}(t)=i$ and backoff counter $b_{n,l}(t)=k$ of STA $n$ on link $l$.
  • Figure 4: Internal structure of an LSTM cell, containing the forget gate, input gate, and output gate.
  • Figure 5: LSTM-based partial historical information state representation layer network.
  • ...and 2 more figures

Theorems & Definitions (1)

  • Remark 1