Application of Neural Networks for Achieving 802.11 QoS in Heterogeneous Channels

In error-prone IEEE 802.11 WLAN (Wireless Local Area Network) environments, heterogeneous link qualities can significantly affect channel utilizations of mobile stations and consequently the user-perceived QoS (Quality of Services) of multimedia applications. This paper proposes a novel optimization framework which provides QoS by adjusting IWSs (Initial Window Size) according to current channel states and QoS requirements. It is a table-driven approach which offline pre-establishes the table of the best IWSs based on a cost-reward function. Neural networks are utilized to learn the mapping correlation and then to generalize that to other situations of interest.