A Real Time Estimation of QoS for GA and LOA Algorithm in Cognitive Radio Network

Cognitive radio networks sets priority for users to provide improved QoS for primary users. In most cognitive radio networks the primary user is assigned highest priority for communication. This causes the secondary user to wait for communication. Hence, alternative methods employ for secondary user to make transmit data on channel reserved for primary users. The secondary users sense free channels by spectrum sensing mechanism. In this paper, Heuristic Greedy algorithm (GA) and Lion optimization algorithm (LOA) apply for efficient channel utilization to improve QoS. In GA algorithm, the spectrum is shared between primary and secondary users on a time basis. In LOA, the primary and secondary users share spectrum information with each other. The GA and LOA algorithm apply in hardware testbed to evaluate comparative QoS analysis in terms of power consumption, delay and bandwidth. The analysis show LOA algorithm provide better QoS compared to GA algorithm.

A Survey of EE Optimization Methods for Primary and Secondary Users in Cognitive Radio Networks

A large scale of spectrum sensing techniques are proposed to improve the use of spectrum resources. However, the EE (energy efficiency) should be guaranteed for both primary and secondary users, especially under various detection performance constraints. In this regard, the linking between activities of PU (primary user) and dynamic access behaviors of SUs (secondary users) should be considered in an integrated way. This survey has compared different existing scenarios and frameworks on EE optimizations. The principal objective is to enhance the system throughput and to coordinate on the physical layer of both PU and SUs, in order to enable a high-quality spectrum detection and a more efficient spectrum access. In the technical part, several optimization methods are introduced under PU’s constraints, and different methods based on game theory are applied to suitable cooperative sensing scenarios for SUs’ optimal access. Finally, the complexity of algorithms is compared, to further reduce the execution time and deploy real-time adaptation for users with lower delay.