Directional-antenna-based spatial and energy-efficient semi-distributed spectrum sensing in cognitive internet-of-things networks

Recently, mmWave-based massive antenna transmission and cognitive networks have emerged as promising approaches for achieving high performances in dense, complicated 6G networks. However, accurate and efficient spectrum sensing that can provide the primary users (PUs) and secondary users (SUs) with satisfactory performance levels remains to be realized. Therefore, this paper proposes a novel semi-distributed and cooperative spectrum sensing approach for directional-antenna-based cognitive radio networks, which are considered key technologies for the next internet-of-things systems. The proposed approach finds the optimal directional sensing beams and spectrum detection energy thresholds to maximizes the system utilization, in terms of the spectrum sensing probability and spatial efficiency. To determine the optimal controls in a semi-distributed manner, a modified elimination method and low-complex coordination algorithm for localized user groups are developed. Simulation results indicate that the proposed control strategy enables enhanced spectrum sensing in terms of the sensing accuracy, spatial efficiency, and energy consumption compared with those achievable using the legacy omnidirectional antenna-based sensing method and directional-antenna-based semi-distributed and fully distributed sensing methods. Notably, the performance gain increases with increase in the number of SUs. Moreover, the proposed scheme achieves near-optimal performance within 1% of that of a directional-antenna-based centralized sensing approach.