Jul 2026· International Mediterranean Conference on Communications and Networking· pp. 1-6· 0 citations· 11 references
Computer Science
Abstract
The increasing demand for higher throughput and lower latency in modern applications has driven the evolution of IEEE 802.11 with the introduction of Wi-Fi 7. The new Extremely High Throughput (EHT) amendment enhances performance with Multi-Link Operation (MLO), allowing concurrent transmissions over multiple frequency links. While MLO improves channel access, reduces latency, and boosts reliability, uneven traffic loads may still cause link congestion, starvation, or collisions, which can severely impact TCP flows. This work investigates the impact of MLO on TCP best effort and video traffic flows, and proposes a Reinforcement Learning (RL) Transmission Opportunity (TXOP) Random Discard strategy for adaptive load balancing. Simulation results demonstrate that the proposed AI-driven approach enhances TCP throughput and latency in Wi-Fi 7 multi-link networks.
A path selection model that combines bottleneck link usage and reinforcement learning that achieves superior state awareness and adaptive routing performance in multi-source heterogeneous networks and hence can be used effectively for intelligent routing in next-generation power communication networks.
Ying Zeng, Xing-Nan Li, Yubeng Bao et al.· EAI Endorsed Transactions on...· 0 citations
Reliable low-latency communication is a critical requirement in enterprise wireless networks such as hospitals, offices, and campuses. This paper proposes an earliest deadline first (EDF)-Lyapunov-Robbins-Monro (ELR), a stochastic scheduling algorithm for IEEE 802.11bn (Wi-Fi 8) Multi-Access Point Coordination Coordina...
Hiya Shah· International Conference on...· 0 citations
This work demonstrates the viability of RL for distributed resource management and provides a reproducible simulation toolkit to support further research in AI-driven wireless communication systems.
Mugerwa Joseph, Ajaegbu Chigozirim· International Journal Of Eng...· 0 citations
A hybrid reinforcement learning (RL) framework that jointly controls queue management and bandwidth allocation in bursty multi-service networks and demonstrates the effectiveness of coordinated learning-based control for stable and QoS-aware operation in bursty networked systems.
T. Khan, Babar Shah, Taimur Karamat et al.· Computing· 0 citations
The rapid growth of multimedia streaming poses critical challenges, including bursty traffic and congestion, leading to playback delays. The existing separate prediction and control mechanisms for multimedia traffic scheduling, which are based on software-defined networks (SDN), are unable to proactively manage bursty...
This research is among the first to employ MARL to this extent, and it offers an end-to-end solution that combines cellular, Wi-Fi, and device-to-device (D2D) communications and considers practical network environments like user mobility and channel conditions.
Nabeel Abdolrazagh Yaseen Alrashedi, Rasool Sadeghi, Wael Hussein Zayer Al-Lamy et al.· Journal of universal compute...· 0 citations
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