Adaptive Threshold of Compressed Sensing for AUD

With the rapid development of wireless communication technology, the number of smart terminals in the network has been steadily increasing. However, the interference and complex environments resulting from multi-user access present significant challenges for user detection and channel estimation. To address these limitations, we propose an adaptive active user detection method based on compressed sensing techniques. This approach utilizes the orthogonal matching pursuit algorithm to adaptively adjust detection thresholds in response to varying noise conditions, thereby optimizing the accuracy of signal reconstruction while maintaining low computational complexity. Simulation results indicate that this method effectively enhances the accuracy of signal reconstruction across different noise environments, significantly improving the environmental adaptability of the compressed sensing algorithm.