Multi-Resource Allocation: Analysis of a Paid Spectrum Sharing Approach Based on Fluid Models

Nowadays industrial and academic communities are focusing much of their efforts to define the main characteristics of 5G. Cognitive radio (CR), offering the possibility to significantly increase the spectrum efficiency, will play a key role in the whole range of Internet of Things communications (one of the use cases of 5G networks). While CR is one of the most discussed topics in contemporary spectrum management, there are still many issues and challenges to be solved, even more when we think about large networks. In this context, one of the main question that motivates this paper is: how to encourage the spectrum sharing behavior of primary users (PUs)? With this in mind, we study a paid-sharing approach where secondary users (SUs) pay for spectrum utilization. We assume a preemptive system where PUs have strict priority over SUs, and the affected SUs will then be reimbursed, implying some cost for the PUs service provider. This paper bears on the analysis of the behavior of the system, where the number of users is arbitrary large and an admission control policy over SUs is applied. We develop a computationally efficient methodology to find an accurate estimation of the optimal admission control boundary based on fluid limits.