Cooperative Multi-Layer Overlapping Spectrum Signal Recognition Based Monitoring Node Selection

With the surge in internet of things (IoT) devices, the radio frequency spectrum is increasingly congested. It leads to overlapping spectrum signals (OSS) and challenging radio signal monitoring. This paper proposes a multi-layer overlapping spectrum signal recognition method, integrating OSS multi-parameter recognition tasks into one single framework. By applying the Hodges-Lehmann (HL) test, an optimal monitoring node selection is designed to enhance the performance of integrated OSS recognition and reduce resource consumption. In low signal-to-noise ratio (SNR) conditions, our experiments demonstrate that using more than three monitoring stations will reduce accuracy, while apply node selection based the HL reduces positioning and power estimation errors, compared to random selection. The method provides a performance improvement for OSS recognition in dense IoT environments.