Machine Learning Empowered Spectrum Sensing Under a Sub-Sampling Framework
Han Zhang, Jian Yang, Yue Gao
Abstract
Compressive sensing (CS) is a technique frequently adopted in wireless communications. By utilizing CS, a receiver could sense the state of channels with sub-Nyquist analog to digital converters when signals are sparse. Traditional CS methods struggle with non-sparse signals due to their intrinsic sparsity assumption. Therefore, we propose using deep learning (DL) to solve the vector support recovery problem with channels’ high occupancy. The simulation results show that the proposed CS framework powered by DL can perform better than a traditional CS analytical benchmark, both in high and low channel occupation regions. We also observe that the ML can work under a lower sampling rate than traditional CS methods. To process data sampled with high channel numbers, a divide and conquer tactic is implemented.