Background: Cognitive Radio Networks (CRNs) provide an innovative solution to spectrum scarcity by enabling secondary users to access licensed spectrum dynamically. However, ensuring reliable communication while minimizing interference remains a significant challenge. Traditional relaying techniques, such as Amplify-and-Forward (AF) and Decode-and-Forward (DF), exhibit limitations, including noise amplification and processing delays. To overcome these challenges, this research proposes a hybrid relaying approach that dynamically switches between Direct Transmission (DR), AF, and DF based on real-time channel conditions, thereby optimizing system performance.

Objectives: The objective of this study is to develop an adaptive communication system that improves transmission efficiency and reliability in underlay CRNs. Specifically, the research aims to design a framework that dynamically selects the most suitable relaying scheme based on channel conditions. Additionally, the study evaluates the impact of the proposed hybrid approach on key performance metrics such as Outage Probability (OP), Symbol Error Rate (SER), and channel capacity. By leveraging a combination of theoretical modeling and MATLAB-based simulations, the effectiveness of the proposed system is analyzed under realistic channel conditions modeled using Nakagami-m fading.

Methods: To achieve these objectives, this research employs analytical modeling and Monte Carlo simulations to compare the performance of the hybrid relaying strategy with conventional single-mode AF and DF schemes. Theoretical formulations are derived to assess how the hybrid system adapts to varying interference and fading environments. Key performance indicators, including OP, SER, and spectral efficiency, are analyzed under different network conditions to validate the advantages of the hybrid approach.

Results: Simulation results demonstrate that the proposed hybrid switching mechanism significantly improves transmission reliability and efficiency in underlay CRNs. The dynamic selection of relaying schemes based on channel conditions reduces OP and SER while enhancing spectral efficiency. Compared to traditional AF and DF relaying strategies, the hybrid model offers superior adaptability, particularly in interference-prone environments with variable fading conditions.

Conclusions: In conclusion, this research presents a novel hybrid relaying framework that dynamically adapts to channel conditions, improving overall system performance in underlay CRNs. The findings contribute to the development of efficient spectrum-sharing strategies that enhance communication reliability while minimizing interference. Future work could explore the integration of machine learning algorithms to further optimize relay selection and transmission efficiency in dynamic wireless environments.