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5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning

Aldebaro Klautau, Pedro Batista, Nuria Gonzalez-Prelcic, Yuyang Wang, Robert W. Heath

arXiv:2106.05370v1eess.SP

TL;DR

The paper tackles the scarcity and cost of 5G mmWave MIMO data by combining traffic and ray-tracing simulators to generate time-evolving channel datasets. Experiments across learning tasks illustrate the methodology’s use for vehicle-to-infrastructure beam selection, including performance close to dynamic programming in reinforcement learning.

  • Problem

    5G mmWave MIMO research lacks freely available large datasets because measurements require expensive equipment and elaborate outdoor campaigns.

  • Method

    The paper combines traffic simulation and ray tracing, repeatedly generating spatially consistent channel scenes with mobility and time evolution for machine-learning applications.

  • Results

    Deep learning experiments illustrate classification, regression, and reinforcement-learning uses of the generated datasets, with DRL achieving an average reward of 0.874 versus 0.879 for dynamic programming.

  • Takeaways & Limitations

    The methodology provides a flexible way to investigate 5G PHY learning problems involving mobility, channel dynamics, and constraints from MAC and upper layers.

Abstract

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The increasing complexity of configuring cellular networks suggests that machine learning (ML) can effectively improve 5G technologies. Deep learning has proven successful in ML tasks such as speech processing and computational vision, with a performance that scales with the amount of available data. The lack of large datasets inhibits the flourish of deep learning applications in wireless communications. This paper presents a methodology that combines a vehicle traffic simulator with a raytracing simulator, to generate channel realizations representing 5G scenarios with mobility of both transceivers and objects. The paper then describes a specific dataset for investigating beams-election techniques on vehicle-to-infrastructure using millimeter waves. Experiments using deep learning in classification, regression and reinforcement learning problems illustrate the use of datasets generated with the proposed methodology

I. INTRODUCTION

The paper addresses scarce, costly 5G mmWave MIMO data by proposing simulator-based channel-data generation and illustrating its use in beam-selection learning tasks.

  • I. INTRODUCTION: 5G mmWave MIMO research lacks freely available data because measurements require expensive equipment and elaborate outdoor campaigns.
  • I. INTRODUCTION: The methodology repeatedly invokes traffic and ray-tracing simulators to generate channel data for complicated mobility scenarios.It targets PHY studies and complements, rather than substitutes for, measurements.
  • I. INTRODUCTION: The generated datasets are used to illustrate deep learning for beam selection in vehicle-to-infrastructure mmWave communications.The paper explicitly focuses on demonstrating methodology flexibility rather than comparing specific deep-learning architectures.
  • I. INTRODUCTION: Ray tracing provides accurate, spatially consistent propagation data but requires detailed environments and can become computationally expensive.The simulator models buildings, vehicles, materials, reflections, diffractions, and scattering.
  • I. INTRODUCTION: Because ray-tracing simulators commonly do not model moving objects, mobility is represented by repeatedly simulating scenes with updated specifications.The paper uses Wireless InSite for this process.

B. Spatial consistency and time evolution requirements

The methodology preserves spatial consistency and channel evolution over time, enabling datasets for dynamic and multiuser 5G MIMO learning problems.

  • B. Spatial consistency and time evolution requirements: Ray tracing is adopted because it can generate simulated channels with spatial consistency and time-evolution history.These are identified as key requirements for machine-learning channel datasets.
  • B. Spatial consistency and time evolution requirements: Each stored snapshot S(t) can contain multiple transmitters and receivers plus positions, dimensions, angles of arrival, and gains.Post-processing uses scene information to model MIMO channels H(t).
  • B. Spatial consistency and time evolution requirements: Observation windows extract sequences of scenes at intervals Tsam over episodes of duration Tepi.This design improves scene diversity while retaining temporal channel structure.
  • B. Spatial consistency and time evolution requirements: Preserving channel variation over time supports algorithms that account for channel dynamics and interactions with higher protocol layers.The paper specifically connects temporal evolution to beam tracking and PHY/MAC studies.

C. Integration of traffic and ray-tracing simulators

The methodology integrates traffic and ray-tracing simulators to model mobility and generate reproducible 5G mmWave MIMO channel data across time-varying scenes.

  • SUMO and Wireless InSite are integrated to study mobility effects in mmWave physical-layer applications.The integration supports V2I scenarios involving moving vehicles, pedestrians, transmitters, receivers, and environmental objects.
  • Traffic simulation separates mobility specification from ray-tracing, enabling scenarios with distinct vehicle characteristics and interactions rather than only constant-speed motion.This separation simplifies experiments involving changing transceiver and scatterer positions.
  • A Python orchestrator repeatedly invokes the traffic simulator, converts object positions, runs ray tracing, and organizes the resulting outputs.The workflow creates episodes by sampling scenes over time and post-processing ray-tracing results.
  • Simulation snapshots are stored at t = nTsam, allowing each scene to include multiple transmitters and receivers for later MIMO channel modeling.Scene data can support customized extraction, visualization, and reproducibility of ray-tracing simulations.
  • The configuration stage defines the ray-tracing environment, mobility lanes, three-dimensional objects, traffic distributions, routes, and motion statistics.Coordinates are converted between the traffic and ray-tracing software, and mobile objects are associated with transmitters or receivers.
  • 5GMdata stores episode metadata and per-scene ray information, including powers, delays, angles, complex gains, and LOS/NLOS interaction paths.The dataset records simulator configurations, object mappings, geometry, and the number L of rays per transmitter-receiver pair.

III. MACHINE LEARNING FOR BEAM-SELECTION IN V2I

The paper illustrates its framework by generating data for machine-learning prediction of the best mmWave beam pairs in vehicle-to-infrastructure cellular systems.

  • The experiments generate data for applying machine learning to predict the best beam pairs in mmWave vehicle-to-infrastructure cellular systems.

A. Brief literature review of beam-selection

The beam-selection literature addresses the challenge of pointing narrow beams at both transmitter and receiver in mmWave vehicular communication.

  • Narrow-beam pointing at both transmitter and receiver is a central mmWave challenge for vehicular sensor-data exchange.
  • Out-of-band measurements and vehicle positions can reduce the time required to find the best beam pair.
  • Beam training is included in standards such as IEEE 802.11ad and 5G and has been extensively studied.

B. Dataset for machine learning in V2I

The V2I dataset uses ray-traced scenes of a modeled urban area with moving vehicles, while representing each scene’s vehicle layout as a structured input matrix for machine learning.

  • Dataset configuration: The dataset uses a three-dimensional Wireless InSite example scenario representing part of Rosslyn, Virginia, with a 337 × 202 m^2 ray-tracing area.
  • Dataset configuration: 116 episodes contain 50 scenes each, with receivers placed on top of 10 vehicles and one transmitter located at the roadside unit.
  • Machine-learning inputs: Beam-selection inputs use vehicle positions and sizes, assuming the roadside unit receives error-free position and identity information for every scene.
  • Machine-learning inputs: The V2I study area is a 23 × 250 m^2 subarea represented on a 1 × 1 m^2 grid, producing a 23 × 250 scene matrix Qs.
  • Scope boundary: The modeled lanes follow the three-dimensional geometry but do not physically exist in the real environment.
  • Machine-learning inputs: The machine-learning input encodes receiver locations with positive vehicle indices, other vehicles with negative height values, and unoccupied positions with zero.

D. Post-processing ray-tracing outputs

The paper converts ray-tracing outputs into channel and beam representations that support supervised beam-selection as regression or classification. Finite-array codebooks then map candidate beam pairs to unique labels and select the pair with maximum received signal.

  • Ray-tracing outputs are combined with channel and beam models to define target beam-selection outcomes.Optimal beam-pair indices serve as supervised-learning labels, while strongest-ray departure and arrival angles support regression targets.
  • In the massive-MIMO limit, increasing antenna count narrows beamwidth and makes the strongest ray’s departure and arrival directions target optimal angles.These angles can be used directly in regression problems.
  • For classification, departure and arrival angles are quantized into four indices and mapped to labels for beam-pair prediction.Only beam vectors observed in the dataset are retained, reducing the label range to M unique vectors rather than all Cartesian products.
  • With finite antenna arrays, the MIMO channel combines ray-tracing outputs with a geometric channel model that accounts for nonzero projected-beam width.The model uses path gains and transmitter- and receiver-side steering vectors for each propagation path.
  • DFT codebooks define transmitter and receiver beams, and each beam pair is converted into a unique index before selecting the pair with the largest received signal.The number of unique indices M can be smaller than |C_t||C_r|.

IV. EXAMPLES OF EXPERIMENTS WITH 5GMDATA

The experiments use the 5GMdata dataset to illustrate classification, regression, and reinforcement-learning formulations for beam selection. Evaluation emphasizes episode-level splitting, while classification results show deep learning advantages on NLOS data alongside overfitting concerns.

  • The experimental section presents three machine-learning examples, with only reinforcement learning using time evolution.The other examples are drop-based and depend on data from a given scene.
  • The dataset and associated code are intended to support reproducibility and are scheduled for public availability.
  • A. Conventional drop-based classification: Classification predicts the best beam-pair index from receiver-specific scene features using 116 episodes, 4 × 4 arrays, and 61 observed classes.The dataset contains 41,023 examples, including LOS and NLOS cases, while 256 beam-pair combinations are possible.
  • A. Conventional drop-based classification: Approximately 60% of the all-data classification examples are LOS, comparable to the 63% maximum accuracy reported for that column.Restricting evaluation to NLOS examples indicates a clear deep-learning advantage over the other tested methods.
  • A. Conventional drop-based classification: Some classifiers achieve zero training errors while retaining relatively large test errors, indicating overfitting in the small-data regime.The authors state that more data is needed to avoid evaluating deep-learning algorithms solely under these conditions.

B. Conventional drop-based multivariate regression

The paper formulates beam-direction estimation as a drop-based multivariate regression problem using the previously defined input matrix. Deep networks outperform shallow networks, although angle-estimation deviations remain relatively large.

  • B. Conventional drop-based multivariate regression: Beam-selection can be formulated as multivariate regression to estimate departure and arrival angles from the previously used input matrix.
  • B. Conventional drop-based multivariate regression: Deep neural architecture outperforms a shallow network with one hidden layer in angle estimation.The comparison is reported using root mean-squared error in degrees.
  • Regression can also estimate each beam’s output power when the objective is overall quality of service rather than the optimum beam direction.This formulation is used within a reinforcement-learning setup.

C. Deep reinforcement learning

The paper casts multi-user V2I beam selection as deep reinforcement learning, combining beam-pair selection with receiver scheduling across time slots. A two-network cascade learns from simulated channel histories and achieves rewards close to dynamic programming.

  • DRL formulation: The environment state includes channel evolution over time, enabling the example to account for interactions between mmWave PHY decisions and MAC or upper-layer constraints.The authors present this experiment as an illustration of how 5GMdata can support DRL, rather than as a benchmark against prior methods.
  • DRL formulation: The DRL action schedules one receiver per time slot and selects its analog beam pair, while the state represents all receivers in the scene.The reward is based on normalized received-power values and penalizes receiver outages.
  • Network architecture: A cascade of convolutional network N1 and network N2 estimates beam gains, then selects receivers for time-slot allocation using recent allocation history.N1 outputs an array of estimated beam-pair gains for each receiver; N2 uses these estimates and binary indicators of allocations over the previous Nout scenes.
  • Network architecture: Shared layers across receiver-specific inputs reduce the computational cost, while supervised regression trains N1 to estimate beam-gain outputs before DRL scheduling.The receiver-specific matrices distinguish the target receiver from other vehicles in the scene.
  • Results: 67.5% accuracy is obtained when N1’s strongest estimated beam pair is used as the classifier output.This is reported for the embedded supervised regression component of the DRL system.
  • Results: 0.874 average reward is achieved by DRL versus 0.879 for dynamic programming using N1 estimates, while dynamic programming reaches 0.891 with actual beam-gain values.The results indicate that DRL learns simultaneous receiver-to-time-slot allocation and beam-pair selection.

V. CONCLUSION

The paper concludes that its methodology generates time-evolving 5G mmWave MIMO channel data by decoupling mobility modeling from channel modeling. It supports ML studies spanning PHY decisions and higher-layer constraints, while broader validation remains future work.

  • Conclusion: The methodology decouples mobility modeling from channel modeling to generate 5G propagation data with channel evolution over time.The focus is mmWave MIMO, but the authors state that the methodology can be used in other scenarios.
  • Conclusion: The generated data can support ML problems involving 5G PHY aspects together with constraints from MAC and upper layers.The conclusion motivates simulation because freely available large datasets for benchmarking 5G deep-learning algorithms remain limited.
  • Limitations and future work: Broad conclusions across distinct sites remain uncertain because ray-tracing scenarios are site specific and their required level of detail is unclear.The authors identify measurement-based validation and methodology tuning as future work.
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