Enhancing 5G Spectrum Sensing: Noncoherent Neyman–Pearson Detector Design and Interference Suppression Method

As the evolution of post-5G and Industrial Internet of Things (IoT) technologies progresses, existing radio frequency (RF) resources become increasingly strained. It is crucial to develop efficient sensing methods for spectrum utilization. Conventional spectrum sensing algorithms suffer from degraded performance when dealing with multicell and multibeam detections within the same frequency, especially in the presence of multipaths. In this article, we propose a noncoherent Neyman–Pearson (NCNP) detector, which is based on the tapped-delay line (TDL) channel model, to appropriately set the detection threshold. Especially, in consideration of the shadowing effects from strong signals on weak signals, we employ a signal reconstruction and interference suppression (SRIS) method. This algorithm detects the signals from stronger cells, treats them as interference, mitigates them, and detects the weaker signals. Furthermore, we use a discrete Fourier transform (DFT)-based channel estimation algorithm to further reduce the noise in the signal demodulation. Finally, the effectiveness and reliability of the proposed algorithm are verified through simulation experiments in different scenarios. The over-the-air (OTA) platform was used to validate the performance of the algorithm in a practical wireless communication environment. Further details regarding the simulation code and experimental results are presented in https://github.com/jiangd33/BS.