Low-Complexity Adaptive Spectrum Sensing for mmWave Full Duplex Cognitive Radio Over Time-Varying Channels

Full-duplex Cognitive Radio (FD-CR) technology has the potential to significantly improve the spectral efficiency of next-generation wireless systems. However, residual self-interference (RSI), unavoidable in FD systems, represents a colored noise, particularly at mmWave frequencies. This fact affects eigenvalue-based Spectrum Sensing (SS) algorithms, preventing them from maintaining a constant false alarm rate (CFAR). This results in a power-throughput trade-off, causing significant performance degradation. Detection in colored noise has been addressed in the literature through time-domain whitening aided by offline training. However, this method proves ineffective with time-varying self-interference (SI). Our paper proposes an adaptive filter-based whitening approach to allow an eigenvalue-based SS to retain the CFAR property in mobile scenarios without the need for offline training. Focusing on low-complexity adaptive filtering, we analytically demonstrate that both the Least Mean Squares (LMS) and the Recursive Least Squares Lattice (RLSL) filters enable the sphericity test to achieve the CFAR property in typical FD-CR scenarios. We highlight the advantages of RLSL over LMS, including faster convergence, superior tracking, and modularity, making it suitable for effective implementation, as well as for efficient low-complexity SI cancellation. Numerical results confirm superior performance in high RSI power scenarios compared to offline solutions and LMS.