An Optimized and Trained Model of Cooperative Sensing for Cognitive Radio Networks

Abstract

Sensing based spectrum allocation is one of the solutions to bridge the gap between spectrum scarcity and underutilization of allocated spectrum. In this context, cognitive radio technology has become the prominent solution for future wireless communication problems. To accurately detect the spectrum availability, CRN uses cooperative spectrum sensing where N number of selected nodes will be involved in making a decision on spectrum occupation. Various sensing parameters such as sensing duration (τ), decision threshold (λ), number of nodes (N) and decision rule (K) have huge impact on the performance of cooperative spectrum sensing. In addition, there are constraints on energy consumption and protection of licensed user’s needs to be considered. Our work focuses on optimization of sensing parameters to maximize the throughput of the cognitive radio network maintaining the energy efficiency and protecting the licensed users from the interference caused by the secondary users. The proposed work uses convex optimization to optimize sensing duration and two-dimensional search algorithm to find the values N and K. Further optimization is done by comparing local decision with cooperative decision.

An optimized power allocation algorithm for cognitive radio NOMA communication

“…Figure 4 shows the time frame where slot1 indicates the PU busy state and slot3 can be used for secondary communication. Spectrum whitespaces are identified using cooperative sensing [16], [17] and fusion center maintains the database on available spectrum bands. Same fusion center used for cooperative sensing can be used to transmit the superposition coded signal of secondary networks.…”