# Deep Learning-Based Channel Extrapolation for Dual-Band Massive MIMO Systems

arXiv: https://arxiv.org/abs/2601.06858 · HTML: https://mlsanity.com/paper/2601.06858/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 20 references that are in the map.
- First posted on arXiv 11 Jan 2026.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding](https://mlsanity.com/paper/2006.16668/) (2020)
- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [Pay Less But Get More: A Dual-Attention-based Channel Estimation Network for Massive MIMO Systems with Low-Density Pilots](https://mlsanity.com/paper/2303.00986/) (2023)
- [A Revolution of Personalized Healthcare: Enabling Human Digital Twin with Mobile AIGC](https://mlsanity.com/paper/2307.12115/) (2023)
- [Low-Overhead Channel Estimation via 3D Extrapolation for TDD mmWave Massive MIMO Systems Under High-Mobility Scenarios](https://mlsanity.com/paper/2406.08887/) (2024)
- [Enabling 6G Through Multi-Domain Channel Extrapolation: Opportunities and Challenges of Generative Artificial Intelligence](https://mlsanity.com/paper/2509.01125/) (2025)
- [AI-Driven Channel State Information (CSI) Extrapolation for 6G: Current Situations, Challenges and Future Research](https://mlsanity.com/paper/2601.00159/) (2026)
- [SSNet: Flexible and robust channel extrapolation for fluid antenna systems enabled by an self-supervised learning framework](https://mlsanity.com/paper/2509.17797/) (2025)
- [FusionNet: Enhanced Beam Prediction for mmWave Communications Using Sub-6GHz Channel and A Few Pilots](https://mlsanity.com/paper/2009.02655/) (2020)
- [Channel Estimation for mmWave MIMO using sub-6 GHz Out-of-Band Information](https://mlsanity.com/paper/2311.08996/) (2023)

## Abstract (arXiv)

Future wireless communication systems will increasingly rely on the integration of millimeter wave (mmWave) and sub-6 GHz bands to meet heterogeneous demands on high-speed data transmission and extensive coverage. To fully exploit the benefits of mmWave bands in massive multiple-input multiple-output (MIMO) systems, highly accurate channel state information (CSI) is required. However, directly estimating the mmWave channel demands substantial pilot overhead due to the large CSI dimension and low signal-to-noise ratio (SNR) led by severe path loss and blockage attenuation. In this paper, we propose an efficient Multi-Domain Fusion Channel Extrapolator (MDFCE) to extrapolate sub-6 GHz band CSI to mmWave band CSI, so as to reduce the pilot overhead for mmWave CSI acquisition in dual band massive MIMO systems. Unlike traditional channel extrapolation methods based on mathematical modeling, the proposed MDFCE combines the mixture-of-experts framework and the multi-head self-attention mechanism to fuse multi-domain features of sub-6 GHz CSI, aiming to characterize the mapping from sub-6 GHz CSI to mmWave CSI effectively and efficiently. The simulation results demonstrate that MDFCE can achieve superior performance with less training pilots compared with existing methods across various antenna array scales and signal-to-noise ratio levels while showing a much higher computational efficiency.