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LIDAR Data for Deep Learning-Based mmWave Beam-Selection
Aldebaro Klautau, Nuria González-Prelcic, Robert W. Heath
TL;DR
The paper addresses mmWave beam-selection overhead by using LIDAR to provide vehicle-side information for LOS detection and beam prediction. It proposes a distributed architecture in which the base station broadcasts its position and a deep-learning vehicle selects candidate beams. In V2I simulations, the approach reduced overhead by 12x in LOS and 2x in NLOS without reducing throughput.
Problem
Beam-selection overhead is important in mmWave systems, while prior approaches were limited by connected-vehicle penetration and had not considered LIDAR or decentralized machine-learning architectures.
Method
A distributed architecture has the base station broadcast its position, while the vehicle uses LIDAR and position information with deep learning for LOS/NLOS detection and top-M beam selection.
Results
12x in LOS and 2x in NLOS: beam-selection overhead was reduced without throughput reduction.
Takeaways & Limitations
LIDAR can support LOS detection and reduce mmWave beam-selection overhead in V2I scenarios.
Abstract
from arXiv · showhide
Millimeter wave communication systems can leverage information from sensors to reduce the overhead associated with link configuration. LIDAR (light detection and ranging) is one sensor widely used in autonomous driving for high resolution mapping and positioning. This paper shows how LIDAR data can be used for line-of-sight detection and to reduce the overhead in millimeter wave beam-selection. In the proposed distributed architecture, the base station broadcasts its position. The connected vehicle leverages its LIDAR data to suggest a set of beams selected via a deep convolutional neural network. Co-simulation of communications and LIDAR in a vehicle-to-infrastructure (V2I) scenario confirm that LIDAR can help configuring mmWave V2I links.
I. INTRODUCTION
The paper proposes a distributed, LIDAR-aided architecture that uses vehicle-side sensing and deep learning to reduce mmWave V2I beam-selection overhead. Simulations indicate substantial overhead reductions in both LOS and NLOS settings without reducing throughput.
- Prior work used position information and machine learning for mmWave beam selection, but performance was limited by the penetration rate of connected vehicles.
- The proposed architecture has the base station broadcast its position while the vehicle processes LIDAR, its own position, and the base-station position to estimate M candidate beam pairs.The vehicle reports the recommended pairs to the base station, which trains them and selects the best pair for transmission.
- The system uses machine learning for LOS/NLOS detection and deep-learning top-M classification conditioned on the estimated channel state.All M selected beams are evaluated later, so their local ranking is irrelevant.
- The evaluation combines traffic simulation with paired ray-tracing and LIDAR simulation to model realistic vehicle mobility and channel-sensing data.
- 12x in LOS and 2x in NLOS: beam-selection overhead is reduced without throughput reduction.The paper also considers larger reductions when some throughput reduction is acceptable.
- The distributed design uses only the connecting vehicle's LIDAR, avoiding dependence on the penetration rate of connected vehicles.The approach considers LIDAR as an additional vehicle sensor relative to prior position-based work.
II. SYSTEM MODEL
The system models downlink OFDM mmWave communication with analog beamforming, frequency-selective geometric channels, and transmitter–receiver beam codebooks. Beam selection chooses the beam pair maximizing normalized received signal power.
- The downlink OFDM mmWave system uses analog beamforming with one RF chain and fixed transmitter and receiver beams.
- Ray-tracing outputs provide multipath gains, delays, and departure and arrival angles for the wideband geometric channel model.
- The frequency-domain channel is represented across OFDM subcarriers using the first L channel taps.
- Transmitter and receiver beam codebooks can have arbitrary sizes and need not be DFT codebooks.
- The selected beam pair is the precoder–combiner combination that maximizes normalized signal power.
A. Information exchange protocol
The distributed protocol lets the base station broadcast positioning information while the vehicle combines its own position with LIDAR data for beam alignment. A vehicle-side ML model proposes candidate beam pairs, evaluates them, and feeds back the best pair.
- The proposed V2I architecture uses a vehicle-side ML beam-selection strategy with a LIDAR-equipped connected vehicle.
- The base station broadcasts its absolute position and coverage zone, while the vehicle estimates its own position using GPS or SLAM.
- The coverage zone is a 3D cuboid defined by its height and two planar corner points, enabling a fixed-resolution grid.
- The vehicle-side ML algorithm outputs M precoder–combiner beam pairs from the predefined codebooks.
- The vehicle evaluates the candidate pairs, feeds back the best one to the base station, and can reuse it uplink when beam correspondence holds.
- After link establishment, beam-tracking overhead can potentially use the high data rates of mmWave links.
B. LIDAR-based feature extraction and deep learning
The paper converts vehicle LIDAR point clouds into fixed-grid 3D histograms and applies neural networks to LOS/NLOS classification and top-M beam selection. It also compares deep learning for LOS detection with a geometric baseline.
- ML addresses two tasks: LOS versus NLOS binary classification and selection of the top-M beam pairs for reducing beam-selection overhead.
- The inputs combine the vehicle’s LIDAR point cloud with the base-station coverage zone and vehicle and base-station positions.
- The point cloud is represented as a sparse 3D histogram whose bins count LIDAR points within fixed regions of the coverage zone.
- Outliers and points beyond a maximum vehicle distance are discarded before uniform quantization produces the histogram feature.
- Both tasks use 13-layer neural networks, including seven 2D convolutional layers with decreasing kernel sizes.
- For LOS detection, the deep-learning model is compared with a decision-stump baseline using the minimum distance between LIDAR points and the vehicle–base-station line.
A. Simulation methodology
The study pairs traffic, ray-tracing, communications, and LIDAR simulation to generate V2I datasets, using an urban-canyon scenario with vehicle-mounted sensors and separate LOS/NLOS evaluations.
- Simulation workflow: The simulation workflow integrates SUMO traffic, Wireless InSite ray-tracing, and LIDAR simulation through an orchestration pipeline.SUMO outputs vehicle positions and orientations, which the orchestrator converts for the other simulators; paired LIDAR and ray-tracing results form the dataset.
- Scenario and sensing: The urban-canyon study area covers approximately 337 × 202 m^2, with a base-station antenna height of z = 4 m and LIDARs mounted on connected vehicles.The scenario represents a region of Rosslyn, Virginia, and includes vehicle point clouds and base-station-to-vehicle lines suggesting LOS channels.
- LIDAR processing: The LIDAR model uses a Velodyne HDL-64E2 scanner with 0.1728-degree angular resolution, 10 Hz rotation, and a 25 m maximum sensing distance.Points below z = 0.1 m are removed as ground reflections, and the experiments use bx = by = 6 and bz = 3 bits.
B. Results
The results evaluate LOS detection and top-M beam selection under positioning noise, showing strong LOS performance but substantially greater difficulty for NLOS conditions.
- LIDAR configuration: The accuracy of binary and top-M classifiers improves considerably when the LIDAR elevation angle is adjusted for communications.The experiments did not make this adjustment and instead used the HDL-64E2 default elevation.
- LOS detection: DL reduces the minimum LOS misclassification error from 24% with a geometry-based decision stump to 10% in the noise-free condition.The comparison concerns LOS detection using the binary classification task.
- Noise and propagation condition: The noisy positioning condition does not cause a significant accuracy loss, while NLOS performance remains considerably lower than LOS performance.The evaluation reports both noise-free and noisy conditions for LOS detection and beam selection.
- Top-M evaluation: The beam-selection results report top-M accuracy and corresponding throughput ratio across M = 1, . . . , 120 for 240 beam pairs.The evaluations distinguish LOS and NLOS classification in the noisy condition.
- Beam-selection trade-off: For M = 10, the achieved throughput ratio is RT = 0.97 for LOS and RT = 0.69 for NLOS.The corresponding beam-selection overhead decreases by a factor of 24, while NLOS reaches RT = 0.94 for M = 60.
V. CONCLUSIONS
The paper concludes that LIDAR supports LOS detection and reduced mmWave beam-selection overhead in V2I scenarios. It identifies richer features, sensor fusion, more data, and better ML tuning as future directions.
- Conclusion: LIDAR can be used for LOS detection and to reduce mmWave beam-selection overhead in V2I scenarios.The reported results are promising despite the relatively simple features adopted.
- Future work: Future work includes alternative features, fusion of LIDAR with other sensors, larger datasets, and improved tuning of ML parameters for NLOS.These directions are stated as ways to improve the NLOS results.