On Deep Learning Based Feedback and Precoding For Multi-user Millimeter-Wave Enabled VR/AR

Virtual reality (VR)/augmented reality (AR) and its applications have attracted significant and increasing attention recently. However, the stringent quality of service (QoS) requirements and better spectral efficiency have posed the challenges such as higher bandwidth, lower latency and better reliability on the VR/AR communication system. This paper proposes a deep-learning-based (DL-based) precoding and feedback method for mitigating the channel interference of multi-users VR/AR environments. That is, our DL-based method uses the VR/AR channel state information (CSI) to do radio resource allocation for maximizing the millimeter-wave network throughput. Numerical results show that our DL-based design could significantly enhance the throughput.