Deep Learning-based Limited Feedback Designs for MIMO Systems
This is an incremental improvement for wireless communication systems, specifically in MIMO feedback design.
The paper tackles the problem of limited feedback in multi-antenna systems by introducing deep neural networks to replace conventional procedures, resulting in a 1 dB symbol error rate gain and reduced computational complexity.
We study a deep learning (DL) based limited feedback methods for multi-antenna systems. Deep neural networks (DNNs) are introduced to replace an end-to-end limited feedback procedure including pilot-aided channel training process, channel codebook design, and beamforming vector selection. The DNNs are trained to yield binary feedback information as well as an efficient beamforming vector which maximizes the effective channel gain. Compared to conventional limited feedback schemes, the proposed DL method shows an 1 dB symbol error rate (SER) gain with reduced computational complexity.