One-Bit Compressed Wideband Spectrum Sensing with Adaptive Sparsity

There has been a growing interest in compressed wideband spectrum sensing. One significant challenge in this field is how to effectively overcome the sparsity dependence. To solve this, two adaptive sparsity methods based on one-bit compressed sensing are explored for wideband spectrum sensing. One method (ASBIHT) is proposed by introducing adaptive sparsity into the binary iterative hard threshold method (BIHT). Using the magnitude of energy, it can accurately reconstruct the signals with unknown sparsity by learning the signal and noise. The other method is constructed by combining the binary iterative hard threshold algorithm with the pinball loss function (PIHT), and adaptive sparsity (ASPIHT). Utilizing the fixed step, the hard threshold parameter is adjusted to approach the signal sparsity gradually. Simulation results verify the efficacies of two proposed methods in the context of wideband spectrum sensing.