Abstract
In millimeter-wave (mmWave) MIMO systems, when the number of radio frequency (RF) chains are limited, estimation of the beamspace channel can become compelling. Also, as the number of RF chains decreases, pilot overhead increases to make channel estimation reliable, eventually reducing the spectral efficiency. In this paper, we propose a channel estimation method which combines compressive sensing (CS) method of GM-LAMP that assumes beamspace channel elements follows the Gaussian mixture distribution a priori, with a novel denoising network based on sparse feature attention for the estimation. According to performance analysis and simulation results, the GM-LAMP combined with feature attention based denoising neural network outperforms state-of-the-art compressed sensing-based algorithms. Furthermore, the proposed method also outperforms previous LAMP-based neural networks with comparable processing time, albeit using less pilot transmission.
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.
