GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave Communication

• URL: http://arxiv.org/abs/2505.17530v2
• Date: Fri, 11 Jul 2025 21:49:26 GMT
• Title: GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave Communication
• Authors: Vendi Ardianto Nugroho, Byung Moo Lee,
• Abstract summary: This research presents a GPS-aided deep learning (DL) model that simultaneously predicts current and future optimal beams for UAV mmWave communications.<n>The model reduces overhead by approximately 93% (requiring the training of 2 3 beams instead of 32 beams) with 95% beam prediction accuracy guarantees, and ensures 94% to 96% of predictions exhibit mean power loss not exceeding 1 dB.
• Score: 6.21540494241516
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Millimeter-wave (mmWave) communication enables high data rates for cellular-connected Unmanned Aerial Vehicles (UAVs). However, a robust beam management remains challenging due to significant path loss and the dynamic mobility of UAVs, which can destabilize the UAV-base station (BS) link. This research presents a GPS-aided deep learning (DL) model that simultaneously predicts current and future optimal beams for UAV mmWave communications, maintaining a Top-1 prediction accuracy exceeding 70% and an average power loss below 0.6 dB across all prediction steps. These outcomes stem from a proposed data set splitting method ensuring balanced label distribution, paired with a GPS preprocessing technique that extracts key positional features, and a DL architecture that maps sequential position data to beam index predictions. The model reduces overhead by approximately 93% (requiring the training of 2 ~ 3 beams instead of 32 beams) with 95% beam prediction accuracy guarantees, and ensures 94% to 96% of predictions exhibit mean power loss not exceeding 1 dB.

Related papers

• RadProPoser: A Framework for Human Pose Estimation with Uncertainty Quantification from Raw Radar Data [1.5318029014836756]
  We introduce RadProPoser, a probabilistic encoder-decoder architecture that processes complex-valued radar tensors.<n>By incorporating variational inference into keypoint regression, RadProPoser jointly predicts 26 three-dimensional joint locations.<n>On our newly released dataset with optical motion-capture ground truth, RadProPoser achieves an overall mean per-joint position error (MPJPE) of 6.425 cm, with 5.678 cm at the 45 degree angle.
  arXiv  Detail & Related papers  (2025-08-05T15:46:05Z)
• Multi-Modal Sensor Fusion for Proactive Blockage Prediction in mmWave Vehicular Networks [10.4860429348784]
  We propose a proactive blockage prediction framework that utilizes multi-modal sensing, including camera, GPS, LiDAR, and radar inputs.<n>This approach uses modality-specific deep learning models to process each sensor stream independently and fuses their outputs.<n>Our results display the effectiveness and efficiency of multi-modal sensing for mmWave blockage prediction and provide a pathway for proactive wireless communication in dynamic environments.
  arXiv  Detail & Related papers  (2025-07-21T16:25:44Z)
• TacoDepth: Towards Efficient Radar-Camera Depth Estimation with One-stage Fusion [54.46664104437454]
  We propose TacoDepth, an efficient and accurate Radar-Camera depth estimation model with one-stage fusion.<n>Specifically, the graph-based Radar structure extractor and the pyramid-based Radar fusion module are designed.<n>Compared with the previous state-of-the-art approach, TacoDepth improves depth accuracy and processing speed by 12.8% and 91.8%.
  arXiv  Detail & Related papers  (2025-04-16T05:25:04Z)
• Multi-Modality Sensing in mmWave Beamforming for Connected Vehicles Using Deep Learning [2.2879063461015425]
  This paper presents a deep learning-based solution for utilizing the multi-modality sensing data for predicting optimal beams having sufficient mmWave received powers.<n>The results show that it can achieve up to 98.19% accuracies while predicting top-13 beams.
  arXiv  Detail & Related papers  (2025-04-08T16:18:00Z)
• Resource-Efficient Beam Prediction in mmWave Communications with Multimodal Realistic Simulation Framework [57.994965436344195]
  Beamforming is a key technology in millimeter-wave (mmWave) communications that improves signal transmission by optimizing directionality and intensity.<n> multimodal sensing-aided beam prediction has gained significant attention, using various sensing data to predict user locations or network conditions.<n>Despite its promising potential, the adoption of multimodal sensing-aided beam prediction is hindered by high computational complexity, high costs, and limited datasets.
  arXiv  Detail & Related papers  (2025-04-07T15:38:25Z)
• Leveraging LSTM for Predictive Modeling of Satellite Clock Bias [0.0]
  We propose an approach utilizing Long Short-Term Memory (LSTM) networks to predict satellite clock bias. Our LSTM model exhibits exceptional accuracy, with a Root Mean Square Error (RMSE) of 2.11 $times$ 10$-11$. This study holds significant potential in enhancing the accuracy and efficiency of low-power receivers used in various devices.
  arXiv  Detail & Related papers  (2024-11-11T14:18:32Z)
• VECTOR: Velocity-Enhanced GRU Neural Network for Real-Time 3D UAV Trajectory Prediction [2.1825723033513165]
  We propose a new trajectory prediction method using Gated Recurrent Units (GRUs) within sequence-based neural networks. We employ both synthetic and real-world 3D UAV trajectory data, capturing a wide range of flight patterns, speeds, and agility. The GRU-based models significantly outperform state-of-the-art RNN approaches, with a mean square error (MSE) as low as 2 x 10-8.
  arXiv  Detail & Related papers  (2024-10-24T07:16:42Z)
• A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques [63.10251271444959]
  Large language models are first pre-trained on trillions of tokens and then instruction-tuned or aligned to specific preferences. We conduct an in-depth investigation of the impact of popular choices for three crucial axes. Our setup spanning over 300 experiments reveals consistent trends and unexpected findings.
  arXiv  Detail & Related papers  (2024-06-07T12:25:51Z)
• Position Aware 60 GHz mmWave Beamforming for V2V Communications Utilizing Deep Learning [2.4993733210446893]
  This paper presents a deep learning-based solution on utilizing the vehicular position information for predicting the optimal beams having sufficient mmWave received powers. The results show that the solution can achieve up to 84.58% of received power of link status on average.
  arXiv  Detail & Related papers  (2024-02-02T09:30:27Z)
• Collaborative Learning with a Drone Orchestrator [79.75113006257872]
  A swarm of intelligent wireless devices train a shared neural network model with the help of a drone. The proposed framework achieves a significant speedup in training, leading to an average 24% and 87% saving in the drone hovering time.
  arXiv  Detail & Related papers  (2023-03-03T23:46:25Z)
• A Meta-Learning Approach to Predicting Performance and Data Requirements [163.4412093478316]
  We propose an approach to estimate the number of samples required for a model to reach a target performance. We find that the power law, the de facto principle to estimate model performance, leads to large error when using a small dataset. We introduce a novel piecewise power law (PPL) that handles the two data differently.
  arXiv  Detail & Related papers  (2023-03-02T21:48:22Z)
• Fast Beam Alignment via Pure Exploration in Multi-armed Bandits [91.11360914335384]
  We develop a bandit-based fast BA algorithm to reduce BA latency for millimeter-wave (mmWave) communications. Our algorithm is named Two-Phase Heteroscedastic Track-and-Stop (2PHT&S)
  arXiv  Detail & Related papers  (2022-10-23T05:57:39Z)
• A Novel Look at LIDAR-aided Data-driven mmWave Beam Selection [24.711393214172148]
  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.
  arXiv  Detail & Related papers  (2021-04-29T18:07:31Z)
• Codebook-Based Beam Tracking for Conformal ArrayEnabled UAV MmWave Networks [33.52271582081627]
  Millimeter wave (mmWave) communications can potentially meet the high data-rate requirements of unmanned aerial vehicle (UAV) networks. As the prerequisite of mmWave communications, the narrow directional beam tracking is very challenging because of the three-dimensional (3D) mobility and attitude variation of UAVs. We propose to integrate the conformal array with the surface of each UAV, which enables the full spatial coverage and the agile beam tracking in highly dynamic UAV mmWave networks.
  arXiv  Detail & Related papers  (2020-05-28T14:57:23Z)

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.