Transmission Power Control Based on Cross Layer Routing Optimization Technique in Cognitive Radio Network

Authors

  • S. Aravindkumaran Department of Electronics and communication engineering, Puducherry technological university, Puducherry-605014,India
  • D. Saraswady Department of Electronics and communication engineering, Puducherry technological university, Puducherry-605014 India.
  • S. Sedhumadhavan Department of Electronics and communication engineering ,Rajiv Gandhi College of engineering and technology,Puducherry-605014

Keywords:

cross layer model, power transmission control, routing optimization, machine learning, cognitive radio networks

Abstract

Cognitive radio networks have been proposed as a feasible option for the fifth generation (5G) wireless system to address the different demands. These networks utilise intelligence to access a principal user's underutilised channel. Cognitive radio networks have developed as a potential solution to the problem that permits unlicensed users to get dynamic spectrum while licenced users remain inactive. One of the many critical cognitive radio processes is channel assignment to the unlicensed user. Due to the variability of channel propagation characteristics, sporadic availability of licenced channel, frequent hand-offs, and demand for critical user security, finding a viable route is more challenging. Additionally, the capability of spectrum management at all network levels is required by the inclusion of opportunistic spectrum access in a cognitive radio network. If there are too many levels, management costs increase. As additional layers are added, performance becomes slower.In cognitive radio networks, this study proposes a unique method for cross-layer model-based power transmission management with routing optimisation. Here, the Levenshtein cross layer model is used to manage power transmission using a software-defined spectrum. Cross layer-enabled transceiver takes place in two separate lower layers; physical (PHY) and data link layer (DLL).Then, reinforced multilayer Q-graph colony optimisation is used to do the routing optimisation. Throughput, lifespan, jamming prediction, energy efficiency, routing delay, and packet delivery ratio are all included in the simulation study. Proposed technique attained throughput of 96%, lifetime of 73%, jamming prediction of 82%, energy efficiency of 65%, routing latency of 55%, packet delivery ratio of 88%; existing LEACH attained throughput of 92%, lifetime of 68%, jamming prediction of 77%, energy efficiency of 59%, routing latency of 52%, PDR of 85%, CWSN attained throughput of 95%, lifetime of 72%, jamming prediction of 79%, energy efficiency of 63%, routing latency of 53%, packet delivery ratio of 86%.The performance of proposed CLM-CRN-MLT model increases the efficiency of the network and attains power consumption.

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Published

12.07.2023

How to Cite

Aravindkumaran, S. ., Saraswady, D. ., & Sedhumadhavan, S. . (2023). Transmission Power Control Based on Cross Layer Routing Optimization Technique in Cognitive Radio Network. International Journal of Intelligent Systems and Applications in Engineering, 11(9s), 511–527. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3188

Issue

Vol. 11 No. 9s (2023)

Section

Research Article

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