HYBRID APPROACH FOR SECURELY MAXIMIZING SPECTRUM UTILIZATION IN COGNITIVE RADIO NETWORKS: MATCHED FILTER AND SALP SWARM ALGORITHM-OPTIMIZED ENERGY DETECTION

The research paper proposes a novel approach for signal detection in cognitive radio networks, aiming to improve spectrum utilization and overall performance. The approach combines matched filter-based detection and the Salp Swarm Algorithm (SSA)-optimized energy detection.Matched filtering is a technique used to detect the presence of a known signal. It correlates the received signal with a reference waveform to determine if the signal is present. In the proposed approach, matched filtering is utilized to detect known signals in the cognitive radio network.On the other hand, energy detection is employed to identify unknown signals. Energy detection measures the energy level of the received signal and compares it to a predetermined threshold. If the energy exceeds the threshold, it is considered as a signal. In this approach, energy detection is optimized using the Salp Swarm Algorithm (SSA). SSA is a metaheuristic algorithm inspired by the behavior of salps in nature, and it is used to find an optimal energy threshold for energy detection in order to improve detection accuracy. The proposed approach is evaluated through simulations, and the results demonstrate its superiority over existing methods in terms of probability of detection, probability of false alarm, and receiver operating characteristics. This indicates that the proposed hybrid approach offers better performance in detecting both known and unknown signals, leading to more efficient spectrum utilization.