LIDAR Data for Deep Learning-Based mmWave Beam-Selection
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous DrivingDeep LearningPositionSimilar Papers 제목 키워드 기반
Federated mmWave Beam Selection Utilizing LIDAR Data
Efficient link configuration in millimeter wave (mmWave) communication systems is a crucial yet challenging task due to the overhead imposed by beam selection. For vehicle-to-infrastructure (V2I) networks, side informati…
LIDAR and Position-Aided mmWave Beam Selection with Non-local CNNs and Curriculum Training
Efficient millimeter wave (mmWave) beam selection in vehicle-to-infrastructure (V2I) communication is a crucial yet challenging task due to the narrow mmWave beamwidth and high user mobility. To reduce the search overhea…
Knowledge DistillationPosition3D Scene Based Beam Selection for mmWave Communications
In this paper, we present a novel framework of 3D scene based beam selection for mmWave communications that relies only on the environmental data and deep learning techniques. Different from other out-of-band side-inform…
3D Scene ReconstructionPositionLiDAR Aided Future Beam Prediction in Real-World Millimeter Wave V2I Communications
This paper presents the first large-scale real-world evaluation for using LiDAR data to guide the mmWave beam prediction task. A machine learning (ML) model that leverages the LiDAR sensory data to predict the current an…
Beam PredictionVision Aided Environment Semantics Extraction and Its Application in mmWave Beam Selection
In this letter, we propose a novel mmWave beam selection method based on the environment semantics extracted from user-side camera images. Specifically, we first define the environment semantics as the spatial distributi…
Keypoint Detection