A Solution to optimal and fair rate adaptation in wireless mesh networks

Abstract

Current wireless networks still employ techniques originally designed for their xed wired counterparts. These techniques make assumptions (such as a xed topology, a static enviroment and non-mobile nodes) that are no longer valid in the wireless communication environment. Furthermore, the techniques and protocols used in wireless networks should take the number of users of a network into consideration, since the channel is a shared and limited resource. This study deals with nding an optimal solution to resource allocation in wireless mesh networks. These networks require a solution to fair and optimal resource allocation that is decentralised and self-con guring, as users in such networks do not submit to a central authority. The solution presented is comprised of two sections. The rst section nds the optimal rate allocation, by making use of a heuristic. The heuristic was developed by means of a non-linear mixed integer mathematical formulation. This heuristic nds a feasible rate region that conforms to the set of constraints set forth by the wireless communication channel. The second section nds a fair allocation of rates among all the users in the network. This section is based on a game theory framework, used for modelling the interaction observed between the users. The fairness model is de ned in strategic form as a repeated game with an in nite horizon. The rate adaptation heuristic and fairness model employs a novel and e ective information distribution technique. The technique makes use of the optimized link state routing protocol for information distribution, which reduces the overhead induced by utilising multi-point relays. In addition, a novel technique for enforcing cooperation between users in a network is presented. This technique is based on the Folk theorem and ensures cooperation by threat of punishment. The punishment, in turn, is executed in the form of banishment from the network. The study describes the performance of the rate adaptation heuristic and fairness model when subject to xed and randomised topologies. The xed topologies were designed to control the amount of interference that a user would experience. Although these xed topologies might not seem to re ect a real-world scenario, they provide a reasonable framework for comparison. The randomised network topology is introduced to more accurately represent a real-world scenario. Furthermore, the randomised network topologies consist of a signi cant number of users, illustrating the scalability of the solution. Both data and voice tra c have been applied to the rate adaptation heuristic and fairness model. It is shown that the heuristic e ectively reduces the packet loss ratio which drops below 5% after about 15 seconds for all xed topologies. Furthermore, it is shown that the solution is near-optimal in terms of data rate and that a fair allocation of data rates among all nodes is achieved. When considering voice tra c, an increase of 10% in terms of data rate is observed compared to data tra c. The heuristic is successfully applied to large networks, demonstrating the scalability of the implementation.