mlsanityData to 7 Oct 2026

Federated mmWave Beam Selection Utilizing LIDAR Data

arXiv 2102.02802 ↗First posted 4 Feb 2021cs.IT eess.SP math.ITcited by 12

Not on the map. Why?

Abstract (arXiv)

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 information from LIDAR sensors mounted on the vehicles has been leveraged to reduce the beam search overhead. In this letter, we propose a federated LIDAR aided beam selection method for V2I mmWave communication systems. 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 a reduced-complexity convolutional NN (CNN) classifier architecture and LIDAR preprocessing, which significantly outperforms previous works in terms of both the performance and the complexity.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 20 of its 32 references that are in the map. First posted on arXiv 4 Feb 2021.

Builds on

20 of 32 references.

References · 20

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2016Neural Architecture Search with Reinforcement Learningcited by 1,489
  4. 2018Federated Learning with Non-IID Datacited by 1,099
  5. 2017Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Trainingcited by 471
  6. 2017Gradient Sparsification for Communication-Efficient Distributed Optimizationcited by 199
  7. 2016QSGD: Communication-Efficient SGD via Gradient Quantization and Encodingcited by 147
  8. 2019Overcoming Forgetting in Federated Learning on Non-IID Datacited by 120
  9. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 108
  10. 2019A Survey on Neural Architecture Searchcited by 71
  11. 2020Federated Learning With Quantized Global Model Updatescited by 54
  12. 2019Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channelscited by 41
8 more
  1. 2019Millimeter Wave Base Stations with Cameras: Vision Aided Beam and Blockage Predictioncited by 29
  2. 2015Towards Dropout Training for Convolutional Neural Networkscited by 17
  3. 2019LIDAR Data for Deep Learning-Based mmWave Beam-Selectioncited by 14
  4. 20193D Scene Based Beam Selection for mmWave Communicationscited by 6
  5. 2019Inertial Sensor Aided mmWave Beam Tracking to Support Cooperative Autonomous Drivingcited by 4
  6. 2020Vision-Aided Radio: User Identity Match in Radio and Video Domains Using Machine Learningcited by 4
  7. 2017Beam Alignment for Millimetre Wave Links with Motion Prediction of Autonomous Vehiclescited by 2
  8. 2016Context Information Based Initial Cell Search for Millimeter Wave 5G Cellular Networkscited by 1

Built on by

Cited by year

Citing papers per year: 2021: 4; 2022: 5; 2023: 0; 2024: 2; 2025: 1.

12 papers.

  1. 2022Overview of Deep Learning-based CSI Feedback in Massive MIMO Systemscited by 35
  2. 2022MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehiclescited by 8
  3. 2021LIDAR and Position-Aided mmWave Beam Selection with Non-local CNNs and Curriculum Trainingcited by 6
  4. 2022MEET: Mobility-Enhanced Edge inTelligence for Smart and Green 6G Networkscited by 5
  5. 2024Vulnerable Road User Detection and Safety Enhancement: A Comprehensive Surveycited by 5
  6. 2021Denoising Noisy Neural Networks: A Bayesian Approach with Compensationcited by 4
  7. 2024Private Collaborative Edge Inference via Over-the-Air Computationcited by 4
  8. 2025Multi-Modality Sensing in mmWave Beamforming for Connected Vehicles Using Deep Learningcited by 3
  9. 2021Federated learning and next generation wireless communications: A survey on bidirectional relationshipcited by 2
  10. 2022Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANscited by 2
  11. 2022Overhead-Free Blockage Detection and Precoding Through Physics-Based Graph Neural Networks: LIDAR Data Meets Ray Tracingcited by 1
  12. 2021AirNet: Neural Network Transmission over the Aircited by 0