Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Hot-Spot Congestion Relief in Public-Area Wireless Networks
WMCSA '02 Proceedings of the Fourth IEEE Workshop on Mobile Computing Systems and Applications
Fairness and load balancing in wireless LANs using association control
Proceedings of the 10th annual international conference on Mobile computing and networking
Self-management in chaotic wireless deployments
Proceedings of the 11th annual international conference on Mobile computing and networking
Available bandwidth-based association in IEEE 802.11 Wireless LANs
Proceedings of the 11th international symposium on Modeling, analysis and simulation of wireless and mobile systems
| Hi-index | 0.00 |
We propose a distributed scheme by which nodes select an appropriate access point to associate with using each individual device's channel utilization. Specifically, we define a new metric, channel utilization, which is defined as the ratio of required bandwidth to available bandwidth estimation. By incorporating channel utilization into the access point selection protocol, we effectively reduce unnecessary reassociations and improve upper layer performance in terms of throughput, packet delivery delay, etc. We further enhance our protocol by using reinforcement learning to adapt the scheduling of probing neighboring access points (APs), ultimately reducing probing overhead by learning from past experience whether the current operational scenario would suffer from undesirable overhead. When channel utilization is combined with adaptive probing, we observed a significant performance improvement compared to traditional association approaches.