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

Abstract

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.

to appear in Proc. of Information Theory and Applications Workshop (ITA), Feb., 2019

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• Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective
• Deep Learning for Channel Sensing and Hybrid Precoding in TDD Massive MIMO OFDM Systems
• Decentralized Beamforming for Cell-Free Massive MIMO with Unsupervised Learning
• Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment
• Coverage Hole Detection for mmWave Networks: An Unsupervised Learning Approach
• A Digital Twin for Reconfigurable Intelligent Surface Assisted Wireless Communication
• Adversarial Machine Learning in Wireless Communications using RF Data: A Review
• Machine Learning-aided Design of Thinned Antenna Arrays for Optimized Network Level Performance
• Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networks
• Learning Reflection Beamforming Codebooks for Arbitrary RIS and Non-Stationary Channels
• Deep Transfer Learning Based Downlink Channel Prediction for FDD Massive MIMO Systems
• MAP-CSI: Single-site Map-Assisted Localization Using Massive MIMO CSI
• Deep Learning Optimized Sparse Antenna Activation for Reconfigurable Intelligent Surface Assisted Communication
• RSSI-Based Hybrid Beamforming Design with Deep Learning
• 3-D Positioning and Environment Mapping for mmWave Communication Systems
• Deep Learning for Massive MIMO with 1-Bit ADCs: When More Antennas Need Fewer Pilots
• Reinforcement Learning of Beam Codebooks in Millimeter Wave and Terahertz MIMO Systems
• Simulation of machine learning-based 6G systems in virtual worlds
• Neural Calibration for Scalable Beamforming in FDD Massive MIMO with Implicit Channel Estimation
• Learning to Beamform in Heterogeneous Massive MIMO Networks
• Learning Beam Codebooks with Neural Networks: Towards Environment-Aware mmWave MIMO
• Deep Multimodal Learning: Merging Sensory Data for Massive MIMO Channel Prediction
• Deep Learning-based Physical-Layer Secret Key Generation for FDD Systems
• Exploiting Multi-Path for Safeguarding mmWave Communications Against Randomly Located Eavesdroppers
• Ordinary Differential Equation-based CNN for Channel Extrapolation over RIS-assisted Communication
• Reproducible Evaluation of Neural Network Based Channel Estimators And Predictors Using A Generic Dataset
• Machine Learning-Enabled Joint Antenna Selection and Precoding Design: From Offline Complexity to Online Performance
• Geometric Machine Learning for Channel Covariance Estimation in Vehicular Networks
• Deep Learning Based RIS Channel Extrapolation with Element-grouping
• Deep Learning-based Time-varying Channel Estimation for RIS Assisted Communication
• Model-based Learning Network for 3-D Localization in mmWave Communications
• Deep Learning Based Antenna Selection for Channel Extrapolation in FDD Massive MIMO
• FusionNet: Enhanced Beam Prediction for mmWave Communications Using Sub-6GHz Channel and A Few Pilots
• MmWave Communication With Active Ambient Perception
• Reinforcement Learning for Beam Pattern Design in Millimeter Wave and Massive MIMO Systems
• Efficient channel charting via phase-insensitive distance computation
• Distributional Reinforcement Learning for mmWave Communications with Intelligent Reflectors on a UAV
• Tensor Learning-based Precoder Codebooks for FD-MIMO Systems
• Semi-Supervised Learning for Channel Charting-Aided IoT Localization in Millimeter Wave Networks