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

TL;DR

DeepMIMO provides a versatile, environment-aware dataset generated from ray-tracing data, enabling improved machine learning research in mmWave and massive MIMO systems through reproducible benchmarks and tailored scenarios.

Contribution

It introduces a parameterized, environment-dependent dataset for mmWave/massive MIMO, based on accurate ray-tracing, facilitating reproducible research and benchmarking.

Findings

Dataset captures environment geometry/materials effects

Allows customization of system and channel parameters

Demonstrated in mmWave beam prediction application

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.