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Machine learning tools are finding interesting applications in millimeterwave (mmWave) and massive MIMO systems. This is mainly thanks to their powerfulcapabilities in learning unknown models and tackling hard optimizationproblems. To advance the machine learning research in mmWave/massive MIMO,however, there is a need for a common dataset. This dataset can be used toevaluate the developed algorithms, reproduce the results, set benchmarks, andcompare the different solutions. In this work, we introduce the DeepMIMOdataset, which is a generic dataset for mmWave/massive MIMO channels. TheDeepMIMO dataset generation framework has two important features. First, theDeepMIMO channels are constructed based on accurate ray-tracing data obtainedfrom Remcom Wireless InSite. The DeepMIMO channels, therefore, capture thedependence on the environment geometry/materials and transmitter/receiverlocations, which is essential for several machine learning applications.Second, the DeepMIMO dataset is generic/parameterized as the researcher canadjust a set of system and channel parameters to tailor the generated DeepMIMOdataset for the target machine learning application. The DeepMIMO dataset canthen be completely defined by the (i) the adopted ray-tracing scenario and (ii)the set of parameters, which enables the accurate definition and reproductionof the dataset. In this paper, an example DeepMIMO dataset is described basedon an outdoor ray-tracing scenario of 18 base stations and more than onemillion users. The paper also shows how this dataset can be used in an exampledeep learning application of mmWave beam prediction.

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