Impact of Packet Size on the Performance Evaluation of Routing Protocols in Cognitive Radio Networks

Talekar, Sopan; Terdal, Sujatha P.

doi:10.5120/ijca2020920506

Cited by 2 publications

(1 citation statement)

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“…In this, the optimal policy is determined by the actor-critic model to perform the channel selection in CRN [17]. In [18], impact of variable packet size on routing performance in cognitive radio networks is evaluated. MCSUI [19] performs joint channel selection and routing which minimizes channel switching and user interferences in cognitive radio networks.…”

Section: Related Workmentioning

confidence: 99%

Untitled

2023

IJCNC

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Cognitive Radio Networks are an emerging technology in for wireless communication. With increasing number of wireless devices in wireless communication, there is a shortage of spectrum. Also, due to the static allocation of channels in wireless networks, there is a scarcity of spectrum underutilization. For efficient spectrum utilization, secondary users dynamically select the free channel of primary users for the transmission of packets. In this work, the performance of routing in a cognitive radio network is improved by the decision of optimal channel selection. The aim of this work is to maximize the throughput and reduce the end-to-end delay. Therefore, an Improved Q-Reinforcement learning algorithm is proposed for the optimal channel selection during the packet routing between source and destination. The performance of this work is compared with the existing routing protocols. It is simulated in network simulator-2 (NS2) with Cognitive Radio Cognitive Network (CRCN) simulation. After performance evaluation, it is observed that the proposed work performs better than existing work with respect to packet delivery ratio, throughput, delay, jitter, control overhead, call blocking probability, packet dropping ratio, good put and normalized routing overhead.

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Section: Related Workmentioning

confidence: 99%