eduzhai > Applied Sciences > Engineering >

Model-Driven Deep Learning for Massive MU-MIMO with Finite-Alphabet Precoding

  • Save

... pages left unread,continue reading

Document pages: 5 pages

Abstract: Massive multiuser multiple-input multiple-output (MU-MIMO) has been themainstream technology in fifth-generation wireless systems. To reduce highhardware costs and power consumption in massive MU-MIMO, low-resolutiondigital-to-analog converters (DAC) for each antenna and radio frequency (RF)chain in downlink transmission is used, which brings challenges for precodingdesign. To circumvent these obstacles, we develop a model-driven deep learning(DL) network for massive MU-MIMO with finite-alphabet precoding in thisarticle. The architecture of the network is specially designed by unfolding aniterative algorithm. Compared with the traditional state-of-the-art techniques,the proposed DL-based precoder shows significant advantages in performance,complexity, and robustness to channel estimation error under Rayleigh fadingchannel.

Please select stars to rate!

         

0 comments Sign in to leave a comment.

    Data loading, please wait...
×