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

arXiv: https://arxiv.org/abs/2108.03941 · HTML: https://mlsanity.com/paper/2108.03941/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 9 Aug 2021.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Latent ODEs for Irregularly-Sampled Time Series](https://mlsanity.com/paper/1907.03907/) (2019)
- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [Deep Transfer Learning Based Downlink Channel Prediction for FDD Massive MIMO Systems](https://mlsanity.com/paper/1912.12265/) (2019)
- [Deep Learning for TDD and FDD Massive MIMO: Mapping Channels in Space and Frequency](https://mlsanity.com/paper/1905.03761/) (2019)
- [Graph Neural Network based Channel Tracking for Massive MIMO Networks](https://mlsanity.com/paper/2004.08738/) (2020)
- [Deep Learning Optimized Sparse Antenna Activation for Reconfigurable Intelligent Surface Assisted Communication](https://mlsanity.com/paper/2009.01607/) (2020)
- [Energy Efficiency Optimization for Multi-cell Massive MIMO: Centralized and Distributed Power Allocation Algorithms](https://mlsanity.com/paper/2105.15161/) (2021)
- [Spatial Covariance Matrix Reconstruction for DOA Estimation in Hybrid Massive MIMO Systems with Multiple Radio Frequency Chains](https://mlsanity.com/paper/2106.10709/) (2021)

## Abstract (arXiv)

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.