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

By Ahmed Alkhateeb. arXiv.org, 2019

815 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/d1d8980d04d411c314910d1926f3bbaac46c2197

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Citations in the corpus

DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset (2022, background): “For example, in [12], the authors developed the DeepMIMO dataset, which is a parametric data generation framework based on ray-tracing simulations targeted mainly for MIMO research.”
Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6 GHz Channels (2019, methodology): “The proposed deep learning based mmWave beam and blockage prediction solutions were evaluated using the publicly-available dataset DeepMIMO [20].”
Enabling Large Intelligent Surfaces With Compressive Sensing and Deep Learning (2019, methodology): “We implemented this setup using the outdoor ray-tracing scenario ’O1’ of the DeepMIMO dataset that is publicly available at [26].”