In this paper, we consider the problem of modelling the average delay experienced by a packet in a single cell IEEE 802.11 DCF wireless local area network. The packet arrival process at each node i is assumed to be Poisson with rate parameter . Since the nodes are sharing a single channel, they have to contend with one another for a successful transmission. The mean delay for a packet has been approximated by modelling the system as a 1-limited Random Polling system with zero switchover time. We show that even for non-homogeneous packet arrival processes, the mean delay of packets across the queues are same and depends on the system utilization factor and the aggregate throughput of the MAC. Extensive simulations are conducted to verify the analytical results.
In @cite_2 , the authors propose System Centric and User Centric Queuing Models for IEEE 802.11 based Wireless LANs. @cite_2 assumes the server to allocate its resources to users in a round robin manner. In the System Centric Model, the arrivals are assumed to be Poisson, thus the resource sharing model takes the form of an M G 1 PS system with the mean delay being the same as that in an equivalent M M 1 system. In the User Centric Model, each user queue is modeled as a separate G G 1 queue.
@cite_2
2107403026
We consider the following two views of an IEEE 802.11 based Wireless LAN: (i) as seen by the WLAN medium and (ii) as seen by a user. In the system centric view, we model the WLAN medium as a server that allocates its resources to users in a round Robin manner. This resource sharing model not only provides a simple model for the system, it also enables us to derive the channel service rate and the total delay incurred in transmitting a packet. For Poisson arrivals, the resource sharing model takes the form of an M G 1 PS system with the mean delay being the same as that in an equivalent M M 1 system. We then take a user centric view and model each user's queue as a separate G G 1 queue. We derive the probability distributions for the different delay sources, i.e., random back-off time, random number of collisions and random number of successful transmissions from other users. This user centric model can provide insights into understanding access and queuing delays in 802.11 DCF. Finally, we discuss the utility of these models for functions such as capacity analysis, admission control and QoS enforcement.