Signalling requirements for smart pricing in mobile telecommunications systems.

Abstract

Smart Pricing can be classified as Dynamic Pricing and bears resemblances to Congestion Pricing. It is a pricing scheme that varies prices according to the current users' responses to rising load. Smart Pricing is a solution to the problem of underutilised network resources or to accommodate growing demand within existing network resources. All three pricing schemes necessitates signalling, however, little is known about the signalling requirements. This thesis makes original contributions in this very area whereby it: • analyses the current 3G mobile telecommunications systems network architecture and shows how Smart Pricing can be implemented; • proposes two models for implementing Smart Pricing in 3G mobile telecommunications systems. In these models, a new network element so-called Dynamic Pricing Engine is proposed to be added; • calculates and reports required signalling requirements for Smart Pricing; and • extends the models to more advanced telecommunications systems. The first model proposed is the Monte Carlo Simulation model in which operation of Smart Pricing is simulated and the required signalling is calculated. Both small and large Smart Pricing systems¹ are investigated and eighteen simulation scenarios are conducted. Highlights² of our findings are as follows. When there are more users in the system, the bidding signalling percentage on the uplink increases but decreases on the downlink and on links between network elements. It is not how the level of congestion is defined, it is the user behaviours that dictate the signalling requirements for Smart Pricing. The second model is the State Space Analysis model, in which the Markov Chain technique is employed. Highlights³ of our findings are as follows. In the steady-state condition, the maximum average signalling loads for the uplink, downlink and links between network elements can be accommodated with existing signalling system capacity. With respect to simulation time, this model is significantly faster than the Monte Carlo Simulation model. It is recommended that the Dynamic Pricing Engine be collocated with the Billing System. Applicability of the proposed models to more advanced cellular telecommunications systems, such as HSDPA, HSPA+ and LTE is also demonstrated. Then, estimated average signalling loads⁴ are reported. Finally, the models are shown to be able to be applied in other resource-constrained and non-cellular telecommunications systems, particularly Cognitive Radio⁵. ¹A Smart Pricing system is defined as a WCDMA UMTS system which adopts Smart Pricing. ²A complete set of the findings can be found in Section 3.9. ³A complete set of the findings can be found in Section 4.12. ⁴Summary of details can be found in Section 5.6. ⁵Summary of details can be found in Section 6.6.