QoE-Based Resource Allocation In Cognitive Radio Networks

In recent years, with the growth of the amount of internet data, the shortage of spectrum resources becomes more and more serious and the data on the server is on the increase, which greatly affect network performance. At the same time, the users' demand for the quality of communication services is increasing. Therefore, in the aspect of improvement of the network performance, an efficient and rational resource allocation scheme becomes more and more urgent. As we know, the cognitive radio technology is beneficial to improving spectrum utilization and the quality of experience(QoE) can measure the service performance reliably. Thus, We adopt the cognitive radio technology and QoE to investigate the resource allocation scheme in cognitive radio network(CRN) from two aspects. One is the QoE-based single-service relay selection and channel scheduling algorithm,and the second is the QoE-based multi-service caching cooperative algorithm.In CRN with only one service, we consider the characteristics of dynamic spectrum access and performance indicators of QoE. We establish a model of relay selection and channel allocation under the constraint of channel uniqueness that one channel can not be used by more than one user at the same time, aiming at maximizing the minimum QoE among secondary users. The model adopt the idea of cooperative relay to make secondary users collaborate with each other. In order to simplify the solving process of the model, we relax variable conditions and convert the model into quadratically constrained mixed integer programming solved by CPLEX optimizer. Simulation results show that the proposed resource allocation approach can significantly improve the minimum QoE among secondary users relative to resource allocation scheme with Qo S as target.In CRN with multi services, considering the interference between primary users and secondary users and performance indicators of QoE of different services, we propose a multi-service caching scheme in CRN. The scheme involves the cache layout, information query and scheduling technique, aiming at maximize the average QoE of each service. A heuristic algorithm is used to solve this scheme. Simulation results show that our caching scheme can improve the average QoE and the QoE can be guaranteed even under high network load, compared with no caching scheme.