Physical-layer security enhancement in cognitive radio networks using whale optimization-based sidelobe suppression

OFDM-based cognitive radio networks suffer from high sidelobe emissions, leading to adjacent interference and potential security vulnerabilities. This paper introduces a Whale Optimization Algorithm (WOA)-based cancellation carrier (CC) approach for sidelobe suppression. Unlike conventional fixed-amplitude methods, the proposed framework adaptively optimizes CC amplitudes under power constraints to minimize total sidelobe energy (TSE). Simulation results show significant improvements: Peak Sidelobe Level (PSL) reduces from -41.48 dB to -57.1 dB, TSE drops from -57.60 dB to -80 dB, and Adjacent Channel Leakage Ratio (ACLR) approaches zero, while maintaining stable Bit Error Rate (BER) and improved Peak-to-Average Power Ratio (PAPR). These improvements enhance spectral efficiency and improve physical-layer robustness by reducing spectral leakage, which may limit susceptibility to eavesdropping, jamming, and spoofing.