As part of radio resource management (RRM), policing mechanisms that influence wireless device behavior and thereby drive systems to better operating points have been addressed amply in the RRM literature. These mechanisms essentially are borne out of expected utility theory (EUT) based microeconomics approaches, and implemented via engineered system design, i.e., embedding these strategies in the link layer and network layer protocols that are executed by wireless devices. When a SP controls access to end-users via differentiated and hierarchical monetary pricing, then the performance of the network is directly subject to end-user decision-making that has shown to deviate from EUT. Prospect Theory, a Nobel prize winning theory that explains real-life decision-making and its deviations from EUT behavior is used to design "prospect pricing" for wireless networks. Specifically, dynamic pricing algorithms for wireless data are designed to enable Heterogeneous Networks (HetNets) to manage the ever increasing demand for data, especially when both spectrum and infrastructure resources are constrained. Using a mix of theory, algorithm development and experimentation, the research agenda proposed by a team comprised of a wireless networking/systems researcher and a cognitive psychologist includes:
- Development of a Framework for Prospect Pricing in Wireless Networks
- Psychophysics Experiments to understand End-User Perceptions and Preferences to Service Offers and Wireless Network Performance
- Evaluation of the Performance of Prospect Pricing in Heterogeneous Networks for Load- Balancing and Resource Management
The research agenda seeks to design and study wireless network pricing from a cognitive psychology perspective, thereby presenting a novel framework to understand how wireless network performance can be influenced by end-user behavior and vice-versa. The successful completion of this research will serve up useful pointers to how prospect pricing can be used by the service providers (SPs) to influence end-user actions and in turn drive solutions that can manage the ever increasing demand for data.