# Deep Learning Assisted Calibrated Beam Training for Millimeter-Wave Communication Systems

arXiv: https://arxiv.org/abs/2101.05206 · HTML: https://mlsanity.com/paper/2101.05206/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 57 references that are in the map.
- First posted on arXiv 8 Jan 2021.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Wireless Communications with Unmanned Aerial Vehicles: Opportunities and Challenges](https://mlsanity.com/paper/1602.03602/) (2016)
- [Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channels](https://mlsanity.com/paper/1910.02900/) (2019)
- [A Tutorial on Beam Management for 3GPP NR at mmWave Frequencies](https://mlsanity.com/paper/1804.01908/) (2018)
- [Millimeter Wave Base Stations with Cameras: Vision Aided Beam and Blockage Prediction](https://mlsanity.com/paper/1911.06255/) (2019)
- [ViWi: A Deep Learning Dataset Framework for Vision-Aided Wireless Communications](https://mlsanity.com/paper/1911.06257/) (2019)
- [Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordination](https://mlsanity.com/paper/1907.00123/) (2019)
- [Deep Convolutional Neural Networks for Massive MIMO Fingerprint-Based Positioning](https://mlsanity.com/paper/1708.06235/) (2017)
- [Coverage and Rate Analysis for Millimeter Wave Cellular Networks](https://mlsanity.com/paper/1402.6430/) (2014)
- [A New Wireless Communication Paradigm through Software-controlled Metasurfaces](https://mlsanity.com/paper/1806.01792/) (2018)
- [Active Learning and CSI Acquisition for mmWave Initial Alignment](https://mlsanity.com/paper/1812.07722/) (2018)
- [Deep Reinforcement Learning-Based Beam Tracking for Low-Latency Services in Vehicular Networks](https://mlsanity.com/paper/2002.05564/) (2020)
- [Sparse Channel Estimation and Hybrid Precoding Using Deep Learning for Millimeter Wave Massive MIMO](https://mlsanity.com/paper/2002.06380/) (2020)
- [Mobility and Blockage-aware Communications in Millimeter-Wave Vehicular Networks](https://mlsanity.com/paper/2002.11210/) (2020)
- [Robust Adaptive Beam Tracking for Mobile Millimetre Wave Communications](https://mlsanity.com/paper/2005.00980/) (2020)
- [Time-Sequence Channel Inference for Beam Alignment in Vehicular Networks](https://mlsanity.com/paper/1812.01220/) (2018)
- [Beam Training and Allocation for Multiuser Millimeter Wave Massive MIMO Systems](https://mlsanity.com/paper/1901.01421/) (2019)
- [Beam Allocation for Millimeter-Wave MIMO Tracking Systems](https://mlsanity.com/paper/1907.00538/) (2019)
- [Two-Step Codeword Design for Millimeter Wave Massive MIMO Systems with Quantized Phase Shifters](https://mlsanity.com/paper/2002.06351/) (2020)

## Built on by (most cited first, 6 of 6)

- [Knowledge and Data Dual-Driven Channel Estimation and Feedback for Ultra-Massive MIMO Systems under Hybrid Field Beam Squint Effect](https://mlsanity.com/paper/2403.12813/) (2024)
- [Digital Twin-Assisted Explainable AI for Robust Beam Prediction in mmWave MIMO Systems](https://mlsanity.com/paper/2507.14180/) (2025)
- [Beam Management with Orientation and RSRP using Deep Learning for Beyond 5G Systems](https://mlsanity.com/paper/2202.02247/) (2022)
- [Deep Learning Empowered Type-II Codebook: New Paradigm for Enhancing CSI Feedback](https://mlsanity.com/paper/2305.08081/) (2023)
- [Improving the Performance of R17 Type-II Codebook with Deep Learning](https://mlsanity.com/paper/2310.05962/) (2023)
- [Deep Learning Beam Optimization in Millimeter-Wave Communication Systems](https://mlsanity.com/paper/2107.07846/) (2021)

## Abstract (arXiv)

Huge overhead of beam training imposes a significant challenge in millimeter-wave (mmWave) wireless communications. To address this issue, in this paper, we propose a wide beam based training approach to calibrate the narrow beam direction according to the channel power leakage. To handle the complex nonlinear properties of the channel power leakage, deep learning is utilized to predict the optimal narrow beam directly. Specifically, three deep learning assisted calibrated beam training schemes are proposed. The first scheme adopts convolution neural network to implement the prediction based on the instantaneous received signals of wide beam training. We also perform the additional narrow beam training based on the predicted probabilities for further beam direction calibrations. However, the first scheme only depends on one wide beam training, which lacks the robustness to noise. To tackle this problem, the second scheme adopts long-short term memory (LSTM) network for tracking the movement of users and calibrating the beam direction according to the received signals of prior beam training, in order to enhance the robustness to noise. To further reduce the overhead of wide beam training, our third scheme, an adaptive beam training strategy, selects partial wide beams to be trained based on the prior received signals. Two criteria, namely, optimal neighboring criterion and maximum probability criterion, are designed for the selection. Furthermore, to handle mobile scenarios, auxiliary LSTM is introduced to calibrate the directions of the selected wide beams more precisely. Simulation results demonstrate that our proposed schemes achieve significantly higher beamforming gain with smaller beam training overhead compared with the conventional and existing deep-learning based counterparts.