A cross-layer adaptive algorithm for multimedia QoS fairness in WLAN environments using neural networks

A cross-layer adaptive algorithm for multimedia QoS fairness in WLAN environments using neural networks

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The authors address the problem of providing fair multimedia quality-of-service (QoS) in IEEE 802.11 distributed co-ordination function-based wireless local area networks in the infrastructure mode where mobile hosts experience heterogeneous channel conditions due to mobility and fading effects. It was observed that unequal link qualities can pose significant unfairness of channel sharing, which may thereby lead to the degradation of multimedia QoS performed in adverse conditions. A cross-layer adaptation scheme that provides fair QoS by online adjusting the multidimensional medium access control layer backoff parameters in accordance with the application-layer QoS requirements as well as the physical-layer channel conditions was proposed. The solution is based on an optimisation approach, which utilises neural networks to learn the cross-layer function. Simulation results demonstrate that the proposed adaptation scheme can tackle heterogeneous channel conditions and random joining (or leaving) of hosts to achieve fair QoS in terms of throughput and packet delay.


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