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

TL;DR

This paper introduces a deep learning approach using a latent ODE-based network within a VAE framework to accurately extrapolate downlink channels from partial uplink data in hybrid mmWave massive MIMO systems, reducing training overhead.

Contribution

It proposes a novel neural network model combining latent ODEs and VAE for antenna-time domain channel extrapolation in massive MIMO systems, improving efficiency.

Findings

The network accurately infers full downlink channels from partial uplink data.

Simulation results demonstrate significant reduction in channel training overhead.

The method outperforms traditional channel estimation techniques.

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.