Abstract
In frequency division duplex massive Multiple-Input Multiple-Output (MIMO) systems, plenty of Channel State Information (CSI) needs to be fed back. By exploiting the correlation of channel coefficients, the channel matrix can be transformed into a sparse form for compression. In this letter, we propose a model-based sparse recovery network that combines the advantages of compressed sensing reconstruction algorithms and neural networks, to perform CSI compression and reconstruction fast and accurately. Moreover, considering that the CSI is not strictly sparse in the discrete Fourier transform basis, we introduce a sparse autoencoder in our network to learn sparse transformations. Extensive experiments show that our model outperforms traditional compressed sensing algorithms and network-based methods.
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.
