Adaptive hybrid spectrum sensing for OTFS: a high-efficiency solution for low-SNR cognitive radio networks

Efficient detection and optimal utilization of spectrum holes are essential requirements of wireless technology due to increased data processing. Employing cognitive radio (CR) technology to sense the spectrum for secondary users (SUs) allows for intelligent decisions to be made in the digital plane, allowing for opportunistic transmission only when the spectrum is not occupied. Conventional sensing techniques, namely the energy detector (ED) and cyclostationary feature detection (CFD) face limitations when used as standalone detectors. The proposed model utilizes an ED and CFD based hybrid detector (HD) framework, optimized for orthogonal time frequency space (OTFS) modulation in low SNR environments of −20dB at Rayleigh fading. The model is evaluated under both non-cooperative spectrum sensing (NCSS) and cooperative spectrum sensing (CSS) scenarios. Simulation results for NCSS at SNR −20 dB established that the HD outperforms ED by 52% and CFD by 110%. Similarly, for CSS, the results are established for an SNR range from −20 to 0 dB , incorporating different fusion rules showing that HD achieves near-perfect detection (P d ≈ 1 at P f = 0.1) for the adaptive threshold weighted voting rule, while achieving an overall throughput improvement of 22% and an energy efficiency gain of 35% compared to the conventional techniques. The results demonstrate that the proposed hybrid OTFS-based spectrum sensing offers a reliable and energy-efficient solution for spectrum access in next-generation CR networks.