A Novel Look at LIDAR-aided Data-driven mmWave Beam Selection
- URL: http://arxiv.org/abs/2104.14579v2
- Date: Mon, 3 May 2021 12:02:06 GMT
- Title: A Novel Look at LIDAR-aided Data-driven mmWave Beam Selection
- Authors: Matteo Zecchin, Mahdi Boloursaz Mashhadi, Mikolaj Jankowski, Deniz
Gunduz, Marios Kountouris, David Gesbert
- Abstract summary: We propose a lightweight neural network (NN) architecture along with the corresponding LIDAR preprocessing.
Our NN-based beam selection scheme can achieve 79.9% throughput without any beam search overhead and 95% by searching among as few as 6 beams.
- Score: 24.711393214172148
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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
overhead of iterative beam discovery procedures, contextual information from
light detection and ranging (LIDAR) sensors mounted on vehicles has been
leveraged by data-driven methods to produce useful side information. In this
paper, we propose a lightweight neural network (NN) architecture along with the
corresponding LIDAR preprocessing, which significantly outperforms previous
works. Our solution comprises multiple novelties that improve both the
convergence speed and the final accuracy of the model. In particular, we define
a novel loss function inspired by the knowledge distillation idea, introduce a
curriculum training approach exploiting line-of-sight (LOS)/non-line-of-sight
(NLOS) information, and we propose a non-local attention module to improve the
performance for the more challenging NLOS cases. Simulation results on
benchmark datasets show that, utilizing solely LIDAR data and the receiver
position, our NN-based beam selection scheme can achieve 79.9% throughput of an
exhaustive beam sweeping approach without any beam search overhead and 95% by
searching among as few as 6 beams.
Related papers
- Online waveform selection for cognitive radar [8.187445866881637]
We propose adaptive algorithms that select waveform parameters in an online fashion.
We propose three reinforcement learning algorithms: bandwidth scaling, Q-learning, and Q-learning lookahead.
Our proposed algorithms achieve the dual objectives of minimizing range error and maintaining continuous tracking without losing the target.
arXiv Detail & Related papers (2024-10-14T15:01:41Z) - Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Driving [58.16024314532443]
We introduce LaserMix++, a framework that integrates laser beam manipulations from disparate LiDAR scans and incorporates LiDAR-camera correspondences to assist data-efficient learning.
Results demonstrate that LaserMix++ outperforms fully supervised alternatives, achieving comparable accuracy with five times fewer annotations.
This substantial advancement underscores the potential of semi-supervised approaches in reducing the reliance on extensive labeled data in LiDAR-based 3D scene understanding systems.
arXiv Detail & Related papers (2024-05-08T17:59:53Z) - Unsupervised Domain Adaptation for Self-Driving from Past Traversal
Features [69.47588461101925]
We propose a method to adapt 3D object detectors to new driving environments.
Our approach enhances LiDAR-based detection models using spatial quantized historical features.
Experiments on real-world datasets demonstrate significant improvements.
arXiv Detail & Related papers (2023-09-21T15:00:31Z) - Deep Learning and Image Super-Resolution-Guided Beam and Power
Allocation for mmWave Networks [80.37827344656048]
We develop a deep learning (DL)-guided hybrid beam and power allocation approach for millimeter-wave (mmWave) networks.
We exploit the synergy of supervised learning and super-resolution technology to enable low-overhead beam- and power allocation.
arXiv Detail & Related papers (2023-05-08T05:40:54Z) - Boosting 3D Object Detection by Simulating Multimodality on Point Clouds [51.87740119160152]
This paper presents a new approach to boost a single-modality (LiDAR) 3D object detector by teaching it to simulate features and responses that follow a multi-modality (LiDAR-image) detector.
The approach needs LiDAR-image data only when training the single-modality detector, and once well-trained, it only needs LiDAR data at inference.
Experimental results on the nuScenes dataset show that our approach outperforms all SOTA LiDAR-only 3D detectors.
arXiv Detail & Related papers (2022-06-30T01:44:30Z) - Deep Learning on Multimodal Sensor Data at the Wireless Edge for
Vehicular Network [8.458980329342799]
We propose a novel expediting beam selection by leveraging multimodal data collected from sensors like LiDAR, camera images, and GPS.
We propose individual and distributed fusion-based deep learning (F-DL) architectures that can execute locally as well as at a mobile edge computing center.
Results from extensive evaluations conducted on publicly available synthetic and home-grown real-world datasets reveal 95% and 96% improvement in beam selection speed over classical RF-only beam sweeping.
arXiv Detail & Related papers (2022-01-12T21:55:34Z) - End-To-End Optimization of LiDAR Beam Configuration for 3D Object
Detection and Localization [87.56144220508587]
We take a new route to learn to optimize the LiDAR beam configuration for a given application.
We propose a reinforcement learning-based learning-to-optimize framework to automatically optimize the beam configuration.
Our method is especially useful when a low-resolution (low-cost) LiDAR is needed.
arXiv Detail & Related papers (2022-01-11T09:46:31Z) - Efficient and Robust LiDAR-Based End-to-End Navigation [132.52661670308606]
We present an efficient and robust LiDAR-based end-to-end navigation framework.
We propose Fast-LiDARNet that is based on sparse convolution kernel optimization and hardware-aware model design.
We then propose Hybrid Evidential Fusion that directly estimates the uncertainty of the prediction from only a single forward pass.
arXiv Detail & Related papers (2021-05-20T17:52:37Z) - Improving Perception via Sensor Placement: Designing Multi-LiDAR Systems
for Autonomous Vehicles [16.45799795374353]
We propose an easy-to-compute information-theoretic surrogate cost metric based on Probabilistic Occupancy Grids (POG) to optimize LiDAR placement for maximal sensing.
Our results confirm that sensor placement is an important factor in 3D point cloud-based object detection and could lead to a variation of performance by 10% 20% on the state-of-the-art perception algorithms.
arXiv Detail & Related papers (2021-05-02T01:52:18Z) - Federated mmWave Beam Selection Utilizing LIDAR Data [3.7352534957395522]
We propose distributed LIDAR aided beam selection for V2I mmWave communication systems utilizing federated training.
In the proposed scheme, connected vehicles collaborate to train a shared neural network (NN) on their locally available LIDAR data during normal operation of the system.
We also propose an alternative reduced-complexity convolutional NN (CNN) architecture and LIDAR preprocessing, which significantly outperforms previous works in terms of both the performance and the complexity.
arXiv Detail & Related papers (2021-02-04T18:49:20Z) - Experimental Investigation of Deep Learning for Digital Signal
Processing in Short Reach Optical Fiber Communications [2.9801732851402556]
We investigate methods for experimental performance enhancement of auto-encoders based on a recurrent neural network (RNN)
In particular, our focus is on the recently proposed sliding window bidirectional RNN (SBRNN) optical fiber autoencoder.
We show that adjusting the processing window in the sequence estimation algorithm at the receiver improves the reach of simple systems trained on a channel model.
arXiv Detail & Related papers (2020-05-18T15:09:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.