Flexible Queuing Model for Number of Active Users in Cognitive Radio Network Environment

Abstract

This work presents a Soft Queuing Model (SQM) for number of active users present in a cognitive radio network (CRN) at some given instant. Starting with the existing cellular network where the upper limit for the number of channels and active users is well defined. The idea is then extended to the complicated scenario of CRNs where the upper limit is not deterministic for both the number of channels and the active users. Accordingly a probabilistic SQM is proposed under the condition that the number of channels and active cognitive users are both random variables. The proposed model will be useful to offer the level of reliability to the clients connected with CRN and hence to offer secure communication even on a cooperative CRN. The proposed model has been verified theoretically and simulations have been carried out in diversified set ups to evaluate the performance.

A Machine Learning Approach to Enhance the Performance of D2D-Enabled Clustered Networks

“…However, we focus on optimizing the clustering scenario by introducing the segregation concept. Moreover, it has been extensively discussed in the literature that eNBs often have spare capacity [16], [18], [19] and we are proposing to exploit this capacity leading to better performance and more efficient utilization of resources. Several research works have exploited the spare capacity to improve the system's performance [17], [20], [21], but this concept has not been considered for clustering.…”