Deep Learning Based Antenna-time Domain Channel Extrapolation for Hybrid mmWave Massive MIMO

• URL: http://arxiv.org/abs/2108.03941v1
• Date: Mon, 9 Aug 2021 11:12:46 GMT
• Title: Deep Learning Based Antenna-time Domain Channel Extrapolation for Hybrid mmWave Massive MIMO
• Authors: Shunbo Zhang, Shun Zhang, Jianpeng Ma, Tian Liu, and Octavia A. Dobre
• Abstract summary: We design a latent ordinary differential equation (ODE)-based network to learn the mapping function from the partial uplink channels to the full downlink ones at the base station. Simulation results show that the designed network can efficiently infer the full downlink channels from the partial uplink ones.
• Score: 30.201881862681972
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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