Path Loss Prediction Using Machine Learning with Extended Features

• URL: http://arxiv.org/abs/2501.08306v1
• Date: Tue, 14 Jan 2025 18:44:35 GMT
• Title: Path Loss Prediction Using Machine Learning with Extended Features
• Authors: Jonathan Ethier, Mathieu Chateauvert, Ryan G. Dempsey, Alexis Bose,
• Abstract summary: Wireless communications rely on path loss modeling, which is most effective when it includes the physical details of the propagation environment. Access to such details enables propagation models to more accurately predict coverage and minimize interference in wireless deployments. We introduce an extended set of features that improves prediction accuracy while, most importantly, maintaining model generalization across a broad range of environments.
• Score: 0.0
• License:
• Abstract: Wireless communications rely on path loss modeling, which is most effective when it includes the physical details of the propagation environment. Acquiring this data has historically been challenging, but geographic information system data is becoming increasingly available with higher resolution and accuracy. Access to such details enables propagation models to more accurately predict coverage and minimize interference in wireless deployments. Machine learning-based modeling can significantly support this effort, with feature-based approaches allowing for accurate, efficient, and scalable propagation modeling. Building on previous work, we introduce an extended set of features that improves prediction accuracy while, most importantly, maintaining model generalization across a broad range of environments.

Related papers

• What Really Matters for Learning-based LiDAR-Camera Calibration [50.2608502974106]
  This paper revisits the development of learning-based LiDAR-Camera calibration. We identify the critical limitations of regression-based methods with the widely used data generation pipeline. We also investigate how the input data format and preprocessing operations impact network performance.
  arXiv  Detail & Related papers  (2025-01-28T14:12:32Z)
• Neural Conformal Control for Time Series Forecasting [54.96087475179419]
  We introduce a neural network conformal prediction method for time series that enhances adaptivity in non-stationary environments. Our approach acts as a neural controller designed to achieve desired target coverage, leveraging auxiliary multi-view data with neural network encoders. We empirically demonstrate significant improvements in coverage and probabilistic accuracy, and find that our method is the only one that combines good calibration with consistency in prediction intervals.
  arXiv  Detail & Related papers  (2024-12-24T03:56:25Z)
• Radio Map Prediction from Aerial Images and Application to Coverage Optimization [46.870065000932016]
  We focus on predicting path loss radio maps using convolutional neural networks. We show that state-of-the-art models developed for existing radio map datasets can be effectively adapted to this task. We introduce a new model that slightly exceeds the performance of the present state-of-the-art with reduced complexity.
  arXiv  Detail & Related papers  (2024-10-07T09:19:20Z)
• TimeSieve: Extracting Temporal Dynamics through Information Bottlenecks [31.10683149519954]
  We propose an innovative time series forecasting model TimeSieve. Our approach employs wavelet transforms to preprocess time series data, effectively capturing multi-scale features. Our results validate the effectiveness of our approach in addressing the key challenges in time series forecasting.
  arXiv  Detail & Related papers  (2024-06-07T15:58:12Z)
• Machine Learning-Based Path Loss Modeling with Simplified Features [0.0]
  Obstacle depth offers a streamlined, yet surprisingly accurate, method for predicting wireless signal propagation. We propose a novel approach that uses environmental information for predictions.
  arXiv  Detail & Related papers  (2024-05-16T11:46:39Z)
• Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagation [80.227864832092]
  Graph neural networks (GNNs) have exhibited exceptional efficacy in a diverse array of applications. The sheer size of large-scale graphs presents a significant challenge to real-time inference with GNNs. We propose an online propagation framework and two novel node-adaptive propagation methods.
  arXiv  Detail & Related papers  (2023-10-17T05:03:00Z)
• Layout Sequence Prediction From Noisy Mobile Modality [53.49649231056857]
  Trajectory prediction plays a vital role in understanding pedestrian movement for applications such as autonomous driving and robotics. Current trajectory prediction models depend on long, complete, and accurately observed sequences from visual modalities. We propose LTrajDiff, a novel approach that treats objects obstructed or out of sight as equally important as those with fully visible trajectories.
  arXiv  Detail & Related papers  (2023-10-09T20:32:49Z)
• Efficient Graph Neural Network Inference at Large Scale [54.89457550773165]
  Graph neural networks (GNNs) have demonstrated excellent performance in a wide range of applications. Existing scalable GNNs leverage linear propagation to preprocess the features and accelerate the training and inference procedure. We propose a novel adaptive propagation order approach that generates the personalized propagation order for each node based on its topological information.
  arXiv  Detail & Related papers  (2022-11-01T14:38:18Z)
• Goal-driven Self-Attentive Recurrent Networks for Trajectory Prediction [31.02081143697431]
  Human trajectory forecasting is a key component of autonomous vehicles, social-aware robots and video-surveillance applications. We propose a lightweight attention-based recurrent backbone that acts solely on past observed positions. We employ a common goal module, based on a U-Net architecture, which additionally extracts semantic information to predict scene-compliant destinations.
  arXiv  Detail & Related papers  (2022-04-25T11:12:37Z)
• Interpretable AI-based Large-scale 3D Pathloss Prediction Model for enabling Emerging Self-Driving Networks [3.710841042000923]
  We propose a Machine Learning-based model that leverages novel key predictors for estimating pathloss. By quantitatively evaluating the ability of various ML algorithms in terms of predictive, generalization and computational performance, our results show that Light Gradient Boosting Machine (LightGBM) algorithm overall outperforms others.
  arXiv  Detail & Related papers  (2022-01-30T19:50:16Z)

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.