Abstract
The underwater acoustic (UWA) channel in the delay-Doppler domain is more sparse and slower time-varying than in the time-delay domain. Hence, the channel dynamics can be tracked more efficiently in the delay-Doppler spreading function (DDSF) representation. In this paper, we propose a data detection scheme with sparse channel prediction in the DDSF representation. Moreover, the sparse channel prediction is further improved by the reinforcement learning in the DDSF representation. Experimental result shows that proposed schemes can obtain a 3. 6-7.5dB bit error rate (BER) performance improvement compared with the traditional channel prediction.
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.
