# 5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning

arXiv: https://arxiv.org/abs/2106.05370 · HTML: https://mlsanity.com/paper/2106.05370/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2021.

## Built on by (most cited first, 20 of 20)

- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
- [Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network](https://mlsanity.com/paper/2201.04712/) (2022)
- [OrchestRAN: Network Automation through Orchestrated Intelligence in the Open RAN](https://mlsanity.com/paper/2201.05632/) (2022)
- [WAIR-D: Wireless AI Research Dataset](https://mlsanity.com/paper/2212.02159/) (2022)
- [MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehicles](https://mlsanity.com/paper/2212.03519/) (2022)
- [Multiverse at the Edge: Interacting Real World and Digital Twins for Wireless Beamforming](https://mlsanity.com/paper/2305.10350/) (2023)
- [Resource-Efficient Beam Prediction in mmWave Communications with Multimodal Realistic Simulation Framework](https://mlsanity.com/paper/2504.05187/) (2025)
- [CAVIAR: Co-simulation of 6G Communications, 3D Scenarios and AI for Digital Twins](https://mlsanity.com/paper/2401.03310/) (2024)
- [VOMTC: Vision Objects for Millimeter and Terahertz Communications](https://mlsanity.com/paper/2409.09330/) (2024)
- [Overhead-Free Blockage Detection and Precoding Through Physics-Based Graph Neural Networks: LIDAR Data Meets Ray Tracing](https://mlsanity.com/paper/2209.07350/) (2022)
- [Through the telecom lens: Are all training samples important?](https://mlsanity.com/paper/2511.21668/) (2025)
- [Simulation of machine learning-based 6G systems in virtual worlds](https://mlsanity.com/paper/2204.09518/) (2022)
- [Informed Learning by Wide Neural Networks: Convergence, Generalization and Sampling Complexity](https://mlsanity.com/paper/2207.00751/) (2022)
- [CAR-BRAINet: Sub-6GHz Aided Spatial Adaptive Beam Prediction with Multi Head Attention for Heterogeneous Vehicular Networks](https://mlsanity.com/paper/2509.10508/) (2025)
- [DiffPace: Diffusion-based Plug-and-play Augmented Channel Estimation in mmWave and Terahertz Ultra-Massive MIMO Systems](https://mlsanity.com/paper/2511.01867/) (2025)
- [CKM-Driven Communication-Aware UAV Intelligent Trajectory Optimization for Urban Inspection](https://mlsanity.com/paper/2606.24979/) (2026)
- [Multi-perspective Imbalance-Conscious 6G Beamforming Optimization and Performance](https://mlsanity.com/paper/2608.12929/) (2026)
- [Wireless Foundation Models: State-of-the-Art and Open Challenges](https://mlsanity.com/paper/2609.04707/) (2026)
- [Vision-Wireless Fusion for Multi-User Localization: A Cross-Modal Transformer Approach](https://mlsanity.com/paper/2609.23372/) (2026)
- [A 3GPP-Compliant Benchmark Dataset for RIS-Aided Beyond 5G Networks](https://mlsanity.com/paper/2609.39058/) (2026)

## Abstract (arXiv)

The increasing complexity of configuring cellular networks suggests that machine learning (ML) can effectively improve 5G technologies. Deep learning has proven successful in ML tasks such as speech processing and computational vision, with a performance that scales with the amount of available data. The lack of large datasets inhibits the flourish of deep learning applications in wireless communications. This paper presents a methodology that combines a vehicle traffic simulator with a raytracing simulator, to generate channel realizations representing 5G scenarios with mobility of both transceivers and objects. The paper then describes a specific dataset for investigating beams-election techniques on vehicle-to-infrastructure using millimeter waves. Experiments using deep learning in classification, regression and reinforcement learning problems illustrate the use of datasets generated with the proposed methodology