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DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications

arXiv 1902.06435 ↗First posted 18 Feb 2019cs.IT eess.SP math.ITcited by 121

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Abstract (arXiv)

Machine learning tools are finding interesting applications in millimeter wave (mmWave) and massive MIMO systems. This is mainly thanks to their powerful capabilities in learning unknown models and tackling hard optimization problems. To advance the machine learning research in mmWave/massive MIMO, however, there is a need for a common dataset. This dataset can be used to evaluate the developed algorithms, reproduce the results, set benchmarks, and compare the different solutions. In this work, we introduce the DeepMIMO dataset, which is a generic dataset for mmWave/massive MIMO channels. The DeepMIMO dataset generation framework has two important features. First, the DeepMIMO channels are constructed based on accurate ray-tracing data obtained from Remcom Wireless InSite. The DeepMIMO channels, therefore, capture the dependence on the environment geometry/materials and transmitter/receiver locations, which is essential for several machine learning applications. Second, the DeepMIMO dataset is generic/parameterized as the researcher can adjust a set of system and channel parameters to tailor the generated DeepMIMO dataset for the target machine learning application. The DeepMIMO dataset can then be completely defined by the (i) the adopted ray-tracing scenario and (ii) the set of parameters, which enables the accurate definition and reproduction of the dataset. In this paper, an example DeepMIMO dataset is described based on an outdoor ray-tracing scenario of 18 base stations and more than one million users. The paper also shows how this dataset can be used in an example deep learning application of mmWave beam prediction.

Key facts. Cited by 121 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Feb 2019.

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Citing papers per year: 2021: 17; 2022: 13; 2023: 9; 2024: 12; 2025: 30; 2026: 40.

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  3. 2025WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communicationcited by 39
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  5. 2024Beam Prediction based on Large Language Modelscited by 20
  6. 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
  7. 2025RadioDiff-3D: A 3D×3D Radio Map Dataset and Generative Diffusion Based Benchmark for 6G Environment-Aware Communicationcited by 16
  8. 2025A Multi-Task Foundation Model for Wireless Channel Representation Using Contrastive and Masked Autoencoder Learningcited by 15
  9. 2024Generating High Dimensional User-Specific Wireless Channels using Diffusion Modelscited by 14
  10. 2025Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Artcited by 13
  11. 2021Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspectivecited by 12
  12. 2022Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel chartingcited by 10
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  3. 2021Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networkscited by 8
  4. 2021Efficient channel charting via phase-insensitive distance computationcited by 8
  5. 2022Efficient Deep Unfolding for SISO-OFDM Channel Estimationcited by 8
  6. 2021Deep learning for location based beamforming with NLOS channelscited by 7
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  8. 2022Multi-Environment based Meta-Learning with CSI Fingerprints for Radio Based Positioningcited by 6
  9. 2021Adversarial Machine Learning Security Problems for 6G: mmWave Beam Prediction Use-Casecited by 5
  10. 2021Security Concerns on Machine Learning Solutions for 6G Networks in mmWave Beam Predictioncited by 5
  11. 2021Neural Calibration for Scalable Beamforming in FDD Massive MIMO with Implicit Channel Estimationcited by 5
  12. 2021Semi-Supervised Learning for Channel Charting-Aided IoT Localization in Millimeter Wave Networkscited by 5
  13. 2022Channel charting based beamformingcited by 5
  14. 2024Differentiable and Learnable Wireless Simulation with Geometric Transformerscited by 5
  15. 2025AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challengescited by 5
  16. 2025Resource-Efficient Beam Prediction in mmWave Communications with Multimodal Realistic Simulation Frameworkcited by 5
  17. 2026LWM-Temporal: Sparse Spatio-Temporal Attention for Wireless Channel Representation Learningcited by 5
  18. 2021DyLoc: Dynamic Localization for Massive MIMO Using Predictive Recurrent Neural Networkscited by 4
  19. 2023Model-based Deep Learning for Beam Prediction based on a Channel Chartcited by 4
  20. 2026U6G XL-MIMO Radiomap Prediction: Multi-Config Dataset and Beam Map Approachcited by 4
  21. 2021Learn to Communicate with Neural Calibration: Scalability and Generalizationcited by 3
  22. 2021Network-Aware 5G Edge Computing for Object Detection: Augmenting Wearables to "See" More, Farther and Fastercited by 3
  23. 2022The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retrainingcited by 3
  24. 2025Analogical Learning for Cross-Scenario Generalization: Framework and Application to Intelligent Localizationcited by 3
  25. 2025Generative Diffusion Receivers: Achieving Pilot-Efficient MIMO-OFDM Communicationscited by 3
  26. 2025EMind: A Foundation Model for Multi-task Electromagnetic Signals Understandingcited by 3
  27. 2021Deep Learning Based RIS Channel Extrapolation with Element-groupingcited by 2
  28. 2021Coverage Hole Detection for mmWave Networks: An Unsupervised Learning Approachcited by 2
  29. 2024CAVIAR: Co-simulation of 6G Communications, 3D Scenarios and AI for Digital Twinscited by 2
  30. 2024ViT LoS V2X: Vision Transformers for Environment-aware LoS Blockage Prediction for 6G Vehicular Networkscited by 2
  31. 2025AI2MMUM: AI-AI Oriented Multi-Modal Universal Model Leveraging Telecom Domain Large Modelcited by 2
  32. 2025Great X: A Unified Multi-Modal Simulator Bridging the Sim2Real Gap for 6Gcited by 2
  33. 2025Digital Twin-Assisted Explainable AI for Robust Beam Prediction in mmWave MIMO Systemscited by 2
  34. 2025Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approachescited by 2
  35. 2026Adaptive 3D-RoPE: Physics-Aligned Rotary Positional Encoding for Wireless Foundation Modelscited by 2
  36. 2021Residual-Aided End-to-End Learning of Communication System without Known Channelcited by 1
  37. 2022Deep Generative Models for Downlink Channel Estimation in FDD Massive MIMO Systemscited by 1
  38. 2023Outage Performance and Novel Loss Function for an ML-Assisted Resource Allocation: An Exact Analytical Frameworkcited by 1

50 most cited of 121 All 121 (JSON)