Modelling the IEEE 802.11 wireless MAC layer under heterogeneous VoIP traffic to evaluate and dimension QoE

Abstract

As computers become more popular in the home and workplace, sharing resources and Internet access locally is a necessity. The simplest method of choice is by deploying a Wireless Local Area Network; they are inexpensive, easy to configure and require minimal infrastructure. The wireless local area network of choice is the IEEE 802.11 standard; IEEE 802.11, however, is now being implemented on larger scales outside of the original scope of usage. The realistic usage spans from small scale home solutions to commercial ‘hot spots,’ providing access within medium size areas such as cafés, and more recently blanket coverage in metropolitan. Due to increasing Internet availability and faster network access, in both wireless and wired, the concept of using such networks for real-time services such as internet telephony is also becoming popular. IEEE 802.11 wireless access is shared with many clients on a single channel and there are three non-overlapping channels available. As more stations communicate on a single channel there is increased contention resulting in longer delays due to the backoff overhead of the IEEE 802.11 protocol and hence loss and delay variation; not desirable for time critical traffic. Simulation of such networks demands super-computing resource, particularly where there are over a dozen clients on a given. Fortunately, the author has access to the UK’s super computers and therefore a clear motivation to develop a state of the art analytical model with the required resources to validate. The goal was to develop an analytical model to deal with realistic IEEE 802.11 deployments and derive results without the need for super computers. A network analytical model is derived to model the characteristics of the IEEE 802.11 protocol from a given scenario, including the number of clients and the traffic load of each. The model is augmented from an existing published saturated case, where each client is assumed to always have traffic to transmit. The nature of the analytical model is to allow stations to have a variable load, which is achieved by modifying the existing models and then to allow stations to operate with different traffic profiles. The different traffic profiles, for each station, is achieved by using the augmented model state machine per station and distributing the probabilities to each station’s state machine accordingly. To address the gap between the analytical models medium access delay and standard network metrics which include the effects of buffering traffic, a queueing model is identified and augmented which transforms the medium access delay into standard network metrics; delay, loss and jitter. A Quality of Experience framework, for both computational and analytical results, is investigated to allow the results to be represented as user perception scores and the acceptable voice call carrying capacity found. To find the acceptable call carrying capacity, the ITU-T G.107 E-Model is employed which can be used to give each client a perception rating in terms of user satisfaction. PAGE 4 OF 162 QUEEN MARY, UNIVERSITY OF LONDON OLIVER SHEPHERD With the use of a novel framework, benchmarking results show that there is potential to maximise the number of calls carried by the network with an acceptable user perception rating. Dimensioning of the network is undertaken, again compared with simulation from the super computers, to highlight the usefulness of the analytical model and framework and provides recommendations for network configurations, particularly for the latest Wireless Multimedia extensions available in IEEE 802.11. Dimensioning shows an overall increase of acceptable capacity of 43%; from 7 to 10 bidirectional calls per Access Point by using a tuned transmission opportunity to allow each station to send 4 packets per transmission. It is found that, although the accuracy of the results from the analytical model is not precise, the model achieves a 1 in 13,000 speed up compared to simulation. Results show that the point of maximum calls comes close to simulation with the analytical model and framework and can be used as a guide to configure the network. Alternatively, for specific capacity figures, the model can be used to home-in on the optimal region for further experiments and therefore achievable with standard computational resource, i.e. desktop machines.