Accurate Cross-Layer Modelling and Evaluation of IEEE 802.11e using a Differentiated p-Persistent CSMA Protocol

With the extensive deployment of 802.11 wireless local area networks (WLANs) and the need for better quality of service (QoS), the 802.11e MAC with service differentiation was developed. In practical WLAN deployments, the capture effect has been shown to enhence the throughput performance of the network. Analysing the effect of fading and near–far effect on the performance of 802.11 is a fundamental consideration in practical situations since the wireless channels are error-prone. Developing an accurate closed form solution of the throughput/delay is a crucial task for the network planning and design. This thesis develops a physical/medium-access-control (PHY/MAC) cross-layer model to characterise the throughput and delay performance of WLANs in error-prone wireless environments. The developed model incorporates the capture effect and channel errors from the PHY-layer perspective, while from the MAC perspective the approach considers the QoS differentiated p-persistent CSMA protocol. This research develops PHY/MAC models that accurately calculate the saturated and non-saturated throughput/delay of p-persistent CSMA protocol with multiple traffic types which can be used to model 802.11e. The developed model expresses the saturated and non-saturated throughput/delay as a function of the number of terminals, packet error rates and capture threshold. The work shows that the PHY layer effects have a significant impact on the throughput/delay performance of WLANs and their dimensioning. The anomaly effect also has a significant impact on performance of 802.11 WLANs which affects the calculation of the network capacity during the network planning phase. This research develops an adaptive QoS differentiated p-persistent CSMA protocol with multirate capability that can be used to resolve the performance anomaly of 802.11 DCF and 802.11e EDCA. The developed models can be applied to the QoS differentiated systems such as 802.11e EDCA with significantly less complexity than Markovian models. The adaptive protocol improves the network capacity which leads to more efficient network deployments in terms of capacity, spectral efficiency and energy consumption.