Attention-enhanced DQN scheduling for multi-link devices in synchronous N-STR Wi-Fi 7 networks
Loading...
Date
2025-12-30
Authors
Abdelreheem, Ahmed
Sreenan, Cormac
Zahran, Ahmed H.
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Published Version
Abstract
WiFi 7 IEEE (802.11be) introduced Multi-Link Operation (MLO) that enables its devices to communicate over multiple links to support evolving latency-sensitive and high-throughput applications. However, MLO requires advanced scheduling algorithms to optimize the operation. This paper models WiFi 7 scheduling as a constrained Markov Decision Process that optimizes throughput, delay, latency and fairness while capturing access constraints and traffic dynamics. We also develop an attention-enhanced Rainbow Deep Q-Network (DQN) scheduling framework that combines multi-head attention, distributional Q-learning, and prioritized experience replay. Simulation results show up to 2.3× throughput improvement, 6× delay reduction, and marked gains in packet drop rate and spectral efficiency over baseline Round Robin scheduling.
Description
Keywords
DQN , DRL , MLO , N-STR , Wi-Fi 7 , [ComputerScience]
Citation
Abdelreheem, A, Sreenan, C & Zahran, A H 2025, Attention-enhanced DQN scheduling for multi-link devices in synchronous N-STR Wi-Fi 7 networks. in 2025 International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM). MSWiM 2025 - 27th International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, Institute of Electrical and Electronics Engineers Inc., pp. 659-666, 27th International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, MSWiM 2025, Barcelona, Spain, 27/10/25. https://doi.org/10.1109/MSWiM67937.2025.11309124
Link to publisher’s version
Copyright
© 2025, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.