*Corresponding Author: rashmitamoon@gmail.com 326 DOI: https://doi.org/10.52756/ijerr.2024.v46.026 Int. J. Exp. Res. Rev., Vol. 46: 326-341 (2024) Proximal Policy Optimization for Efficient Channel Allocation with Quality of Service (QoS) in Cognitive Radio Networks Kalyana Chakravarthy Chilukuri 1 , N Chaitanya Kumar 2 , T. Vidhyavathi 3 , Regidi Suneetha 4 , V Sita Rama Prasad 5 , Badugu Samatha 6 and Mahanty Rashmita 7 * 1 Department of Computer Science and Engineering, MVGR College of Engineering(A), Vizianagaram, Andhra Pradesh, India; 2 Department of Information Technology, Anil Neerukonda Institute of Technology and Sciences(A), Visakhapatnam, Andhra Pradesh, India; 3 Department of Electronics and Communication Engineering, Anil Neerukonda Institute of Technology and Sciences(A), Visakhapatnam, Andhra Pradesh, India; 4 Department of Electronics and Communication Engineering, Sanketika Vidya Parishad Engineering College, Visakhapatnam, Andhra Pradesh, India; 5 Department of Computer science and Engineering, Vignan's Institute of Engineering for Women, Visakhapatnam, Andhra Pradesh, India; 6 Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Andhra Pradesh, India; 7 Department of Basic Sciences and Humanities, Vignan’s Institute of Engineering for Women, Visakhapatnam, Andhra Pradesh, India E-mail/Orcid Id: KCC, kch.chilukuri@gmail.com, https://orcid.org/0000-0002-8137-7074; NCK, chaitanya.india9@gmail.com, https://orcid.org/0009-0006-0535- 7174; TV, vidyavathi.ece@anits.edu.in, https://orcid.org/0000-0003-3609-729X; RS, suneetha.ece@svpec.edu.in, https://orcid.org/0000-0003-2204- 5480;VSRP, vsrprasad45@gmail.com, https://orcid.org/0009-0002-4924-8712; BS, bsamatha@kluniversity.in, https://orcid.org/0000-0003-1353- 2797; MR, rashmitamoon@gmail.com, https://orcid.org/0000-0001-9247-8295 Introduction Although the application of deep learning techniques for spectrum allocation in in CRNs has been widely studied, the problem of channel allocation, which is essential for routing, has not been dealt with extensively. Most of the existing algorithms are focused on efficient spectrum allocation. These techniques focus strictly on a few particular aspects, such as effective queuing of the secondary users or classifying the secondary users based on their priority levels. Some of these techniques focus Article History: Received: 18 th Jul., 2024 Accepted: 25 th Dec., 2024 Published: 30 th Dec., 2024 Abstract: A multi-variable relationship exists in Cognitive Radio Networks (CRNs) where factors such as Energy efficiency, Throughput, Delay and Signal Noise Ratio (SINR) are related. The SINR shows the quality of the signal and is defined as the total power of a specific signal over the total power of an inter signal plus noise. This work proposes an effective energy and delay-efficient channel allocation strategy for CRNs (Cognitive Radio Networks) using Q-Learning and actor-criticism algorithms that maximize rewards. We also propose a Proximal Policy Optimization (PPO) algorithm that uses clipping of surrogate objectives to prevent large policy changes and ensure that the other parameters remain stable over time. We study the tradeoff between rewards, energy efficiency and other parameters and compare the algorithms with respect to the same. Results show that the proposed PPO method, while using optimally increased energy consumption, significantly reduces the delay, improves the thought and reduces the packet loss ratio for efficient channel allocation. This is positive with our findings shown in the results section and by comparing the proposed method with other algorithms to identify improved throughput and channel utilization. As the simulation results indicate that the PPO algorithm has very high throughput and significantly minimizes the delay and packet loss, it is suitable for application in all sorts of services such as video, imaging or M2M. The results are also compared with two of the existing channel allocation schemes and they confirm that the proposed algorithm performs better in terms of throughput discussed in one scheme and channel efficiency in the other. Keywords: Reinforcement learning, Channel allocation, QoS, Q- Learning, Actor-critic, PPO How to cite this Article: Kalyana Chakravarthy Chilukuri, N Chaitanya Kumar, T. Vidhyavathi, Regidi Suneetha, V Sita Rama Prasad, Badugu Samatha and Mahanty Rashmita (2024). Proximal Policy Optimization for Efficient Channel Allocation with Quality of Service (QoS) in Cognitive Radio Networks. International Journal of Experimental Research and Review, 46, 326-341. DOI: https://doi.org/10.52756/ijerr.2024.v46.026