Cognitive Radio and Spectrum Sensing Techniques for Sustainable Marine Ecosystem Monitoring

Arun KUMAR¹Aziz Nanthaamornphong^2*Mohammed H. Alsharif³Mehedi Masud^4*Hossam Meshref⁴

• ¹Sikkim Manipal University, Gangtok, India
• ²Prince of Songkla University Phuket Campus, Phuket, Thailand
• ³Sejong University, Gwangjin-gu, Republic of Korea
• ⁴Taif University, Taif, Saudi Arabia

The smart monitoring of marine ecosystems requires reliable and efficient wireless communication technologies to support large-scale sensor networks deployed across oceans and coastal regions. Spectrum sensing (SS) plays a vital role in enabling efficient spectrum utilization in the cognitive radio-based Internet of Underwater Things (IoUT) for such applications, particularly when integrated with non-orthogonal multiple access (NOMA) systems. This work explores two prominent spectrum sensing techniques: a Double Match Filter (DMF) with a probability of false alarm (Pfa) of 0.5, and Energy Spectrum Sensing (ESS) with Pfa < 0.5—under Rayleigh and Rician fading channels that commonly represent dynamic marine environments. DMF enhances detection accuracy through optimized filtering, whereas ESS provides a straightforward method to assess the spectrum availability for sensor-to-sensor and sensor-to-satellite communication. Integrating these methods ensures robust connectivity for transmitting real-time data regarding water quality, biodiversity, and pollution indicators. Key performance parameters, including the probability of detection (Pd), Pfa, and bit error rate (BER), were simulated and compared with those of the conventional SS methods. The results demonstrate that the proposed approach outperforms traditional ESS and Match Filter techniques, highlighting its potential to improve the reliability, scalability, and energy efficiency of smart technologies designed for real-time monitoring and conservation of marine ecosystems.