Abstract
To address the challenge of mitigating asynchronous and non-stationary interference under single-channel conditions, we proposes a sparse component analysis interference mitigation method based on the recurrent neural network. This method aims to recover the desired signal from the received time-frequency over-lapped co-channel signal. Unlike previous interference mitigation methods, our proposed method achieves ”end-to-end” signal recovery in the time domain without any priori requirements on the received signals, making it more universal than existing methods. Numerical results validate the effectiveness of our proposed method and demonstrate its significantly superior mitigation performance compared to existing schemes under different environmental noises, intensities of interfering signals, and generalization test conditions.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
