# Deep Learning for TDD and FDD Massive MIMO: Mapping Channels in Space and Frequency

arXiv: https://arxiv.org/abs/1905.03761 · HTML: https://mlsanity.com/paper/1905.03761/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2019.

## Built on by (most cited first, 17 of 17)

- [Vision-Aided 6G Wireless Communications: Blockage Prediction and Proactive Handoff](https://mlsanity.com/paper/2102.09527/) (2021)
- [Efficient Deep Unfolding for SISO-OFDM Channel Estimation](https://mlsanity.com/paper/2210.06588/) (2022)
- [Deep learning for location based beamforming with NLOS channels](https://mlsanity.com/paper/2201.01386/) (2021)
- [Camera Based mmWave Beam Prediction: Towards Multi-Candidate Real-World Scenarios](https://mlsanity.com/paper/2308.06868/) (2023)
- [Neural Calibration for Scalable Beamforming in FDD Massive MIMO with Implicit Channel Estimation](https://mlsanity.com/paper/2108.01529/) (2021)
- [DeepTx: Deep Learning Beamforming with Channel Prediction](https://mlsanity.com/paper/2202.07998/) (2022)
- [Channel charting based beamforming](https://mlsanity.com/paper/2212.12340/) (2022)
- [Learn to Communicate with Neural Calibration: Scalability and Generalization](https://mlsanity.com/paper/2110.00272/) (2021)
- [Generative Diffusion Receivers: Achieving Pilot-Efficient MIMO-OFDM Communications](https://mlsanity.com/paper/2506.18419/) (2025)
- [Enabling Deep Learning-based Physical-layer Secret Key Generation for FDD-OFDM Systems in Multi-Environments](https://mlsanity.com/paper/2211.03065/) (2022)
- [A Variational Auto-Encoder Enabled Multi-Band Channel Prediction Scheme for Indoor Localization](https://mlsanity.com/paper/2309.12200/) (2023)
- [Multimodal Mixture-of-Experts for ISAC in Low-Altitude Wireless Networks](https://mlsanity.com/paper/2512.01750/) (2025)
- [Deep Generative Models for Downlink Channel Estimation in FDD Massive MIMO Systems](https://mlsanity.com/paper/2203.04935/) (2022)
- [Outage Performance and Novel Loss Function for an ML-Assisted Resource Allocation: An Exact Analytical Framework](https://mlsanity.com/paper/2305.09739/) (2023)
- [Deep Learning Based Antenna-time Domain Channel Extrapolation for Hybrid mmWave Massive MIMO](https://mlsanity.com/paper/2108.03941/) (2021)
- [Deep Learning for THz Channel Estimation and Beamforming Prediction via Sub-6GHz Channel](https://mlsanity.com/paper/2411.15589/) (2024)
- [Deep Reinforcement Learning Approach to QoSAware Load Balancing in 5G Cellular Networks under User Mobility and Observation Uncertainty](https://mlsanity.com/paper/2510.24869/) (2025)

## Abstract (arXiv)

Can we map the channels at one set of antennas and one frequency band to the channels at another set of antennas---possibly at a different location and a different frequency band? If this channel-to-channel mapping is possible, we can expect dramatic gains for massive MIMO systems. For example, in FDD massive MIMO, the uplink channels can be mapped to the downlink channels or the downlink channels at one subset of antennas can be mapped to the downlink channels at all the other antennas. This can significantly reduce (or even eliminate) the downlink training/feedback overhead. In the context of cell-free/distributed massive MIMO systems, this channel mapping can be leveraged to reduce the fronthaul signaling overhead as only the channels at a subset of the distributed terminals need to be fed to the central unit which can map them to the channels at all the other terminals. This mapping can also find interesting applications in mmWave beam prediction, MIMO radar, and massive MIMO based positioning. In this paper, we introduce the new concept of channel mapping in space and frequency, where the channels at one set of antennas and one frequency band are mapped to the channels at another set of antennas and frequency band. First, we prove that this channel-to-channel mapping function exists under the condition that the mapping from the candidate user positions to the channels at the first set of antennas is bijective; a condition that can be achieved with high probability in several practical MIMO communication scenarios. Then, we note that the channel-to-channel mapping function, even if it exists, is typically unknown and very hard to characterize analytically as it heavily depends on the various elements of the surrounding environment. With this motivation, we propose to leverage the powerful learning capabilities of deep neural networks ....