Deep Learning Based Antenna-time Domain Channel Extrapolation for Hybrid mmWave Massive MIMO
This addresses the problem of high overhead in channel training for hybrid mmWave massive MIMO systems, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the challenge of acquiring downlink channel state information in time-varying massive MIMO systems by designing a latent ODE-based network under a VAE framework to extrapolate full downlink channels from partial uplink ones, reducing channel training overhead as shown in simulations.
In a time-varying massive multiple-input multipleoutput (MIMO) system, the acquisition of the downlink channel state information at the base station (BS) is a very challenging task due to the prohibitively high overheads associated with downlink training and uplink feedback. In this paper, we consider the hybrid precoding structure at BS and examine the antennatime domain channel extrapolation. We design a latent ordinary differential equation (ODE)-based network under the variational auto-encoder (VAE) framework to learn the mapping function from the partial uplink channels to the full downlink ones at the BS side. Specifically, the gated recurrent unit is adopted for the encoder and the fully-connected neural network is used for the decoder. The end-to-end learning is utilized to optimize the network parameters. Simulation results show that the designed network can efficiently infer the full downlink channels from the partial uplink ones, which can significantly reduce the channel training overhead.