Wireless optimisation based on economic criteria.

Abstract

The rapid growth in demand due to the emergence of mobile communication services with variable rates, coupled with the resource scarcity of mobile air interface, has encouraged researchers to find technological solutions to increase spectral efficiency in order to support different levels of Quality of Service (QoS). Radio resource management (RRM) plays a major role in QoS provisioning and congestion control for wireless networks. The main problem with the congestion control mechanisms provided by current RRM schemes is that they are mostly reactive, triggered only when congestion occurs. The common, traditional solution to congestion has been for system planners to over-engineer a network by assigning more resources than are necessary. This approach is very costly because busy periods are usually brief, causing the network to be often under-utilised outside of these periods. Current static, usage-based pricing models also fail to assist in traffic shaping to even out loads. Economic modelling offers a new perspective into current RRM schemes and enables efficient utilisation of scarce resources and congestion prevention based on concepts such as utility, price, Pareto optimality and game theory. Dynamic pricing has been proposed as a mechanism to encourage users to adapt their resource consumption level according to network conditions. A good pricing model can provide the necessary positive incentives to increase users’ arrival rate when the network load is relatively low and negative incentives for users to defer their usage when the load is relatively high. In this dissertation, we propose an economic framework for pricing and RRM for 3G and beyond systems. Our aim is two-fold: to calculate an optimal integrated dynamic pricing and RRM policy; and to allocate scarce network resources in a fair and Pareto-optimal manner. The optimal integrated dynamic pricing and RRM policy is computed based on the stochastic distribution of users’ budget, arrivals, handoffs and departures. Our results show that the integrated policy is superior in terms of average reward improvement and congestion prevention to current schemes that use static pricing models. In interferencebased networks such as WCDMA, we suggest users be charged according to their noise rise factor, i.e. an estimate of the amount of interference generated by the call. This interference-based pricing model improves on the conventional load-based model in by delivering higher revenue and lower call blocking and handoff probabilities. Using the axiomatic bargaining concepts from cooperative game theory, we derive a class of fair and Pareto-optimal bargaining solutions that allocate wireless resources based on users’ minimum and maximum rate requirements. We propose two models: symmetric and asymmetric. In the latter, resource is allocated according to the price paid by the users. An important significance of the asymmetric bargaining model is that this solution is still Pareto-optimal and fair according to the users’ bargaining power. Our approach is also a departure from current works using noncooperative game theory that can only achieve an inefficient outcome, i.e. the Nash equilibrium; or cooperative game theory that focus on only one solution on the Pareto-optimal boundary. By analysing a range of bargaining solutions instead of specific ones, operators can proceed to select the best outcome out of these Pareto-optimal solutions based on criteria like revenue.