Bio-inspired self-adaptive rate control for multi-priority data transmission over IEEE 802.11e EDCA WLANs

The unreasonable allocation of limited network resources in conjunction with burst of traffic load injection in Wireless Local Area Networks (WLANs) may lead to congestion, and cannot guarantee strict Quality of Service (QoS) required by real-time service. Rate control is particularly an important approach for the provision of QoS in the IEEE 802.11e Enhanced Distributed Channel Access (EDCA) WLANs. In this paper, a bio-inspired self-adaptive rate control approach for multi-priority data transmission is proposed on basis of the extended Lotka-Volterra (LV) competitive model, which considers the effects of new arrival traffic on the system stability according to the limited available network resources and competitions with others traffic flows. This approach ensures that the network operates at near-optimal channel utilization and rapidly converge to a global stable equilibrium point (EP). Moreover, all data flows are of peaceful coexistence and QoS differentiation. The proposed approach is applied to IEEE 802.11e EDCA protocol, the allocated bandwidth of each data flow is optimized in an unsaturated case, and the bandwidth utilization is maximized based on Particle Swarm Optimization (PSO) algorithm whilst maintaining the service differentiation of multi-priority flows. Extensive simulations are conducted to illustrate the superior performance of the proposed rate control approach.