Map-Based Path Loss Prediction in Multiple Cities Using Convolutional Neural Networks

• URL: http://arxiv.org/abs/2411.17752v3
• Date: Tue, 25 Mar 2025 23:17:14 GMT
• Title: Map-Based Path Loss Prediction in Multiple Cities Using Convolutional Neural Networks
• Authors: Ryan G. Dempsey, Jonathan Ethier, Halim Yanikomeroglu,
• Abstract summary: Obstructions along a communications link are often considered implicitly or through derived metrics such as representative clutter height or total obstruction depth.<n>We propose a path-specific path loss prediction method that uses convolutional neural networks to automatically perform feature extraction from 2-D obstruction height maps.
• Score: 20.62701088477552
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Radio deployments and spectrum planning benefit from path loss predictions. Obstructions along a communications link are often considered implicitly or through derived metrics such as representative clutter height or total obstruction depth. In this paper, we propose a path-specific path loss prediction method that uses convolutional neural networks to automatically perform feature extraction from 2-D obstruction height maps. Our methods result in low prediction error in a variety of environments without requiring derived metrics.

Related papers

• Reciprocity-Aware Convolutional Neural Networks for Map-Based Path Loss Prediction [20.62701088477552]
  Path loss modeling is a widely used technique for estimating point-to-point losses along a communications link from transmitter (Tx) to receiver (Rx) Modern path loss modeling often leverages data-driven approaches, using machine learning to train models on drive test measurement datasets. In this paper, we demonstrate that data augmentation can be used to train a path loss model that is generalized to uplink, downlink, and backhaul scenarios.
  arXiv  Detail & Related papers  (2025-04-04T17:44:14Z)
• Investigating Map-Based Path Loss Models: A Study of Feature Representations in Convolutional Neural Networks [20.62701088477552]
  We investigate different methods of representing scalar features in convolutional neural networks. We find that representing scalar features as image channels results in the strongest generalization.
  arXiv  Detail & Related papers  (2025-01-13T18:15:01Z)
• TopoSD: Topology-Enhanced Lane Segment Perception with SDMap Prior [70.84644266024571]
  We propose to train a perception model to "see" standard definition maps (SDMaps) We encode SDMap elements into neural spatial map representations and instance tokens, and then incorporate such complementary features as prior information. Based on the lane segment representation framework, the model simultaneously predicts lanes, centrelines and their topology.
  arXiv  Detail & Related papers  (2024-11-22T06:13:42Z)
• Clutter Classification Using Deep Learning in Multiple Stages [0.0]
  This paper explores the application of deep learning to satellite imagery to identify environmental clutter types automatically. Knowing the type of obstruction can improve the prediction accuracy of key propagation metrics such as path loss.
  arXiv  Detail & Related papers  (2024-08-08T12:16:14Z)
• Transformer-Based Neural Surrogate for Link-Level Path Loss Prediction from Variable-Sized Maps [11.327456466796681]
  Estimating path loss for a transmitter-receiver location is key to many use-cases including network planning and handover. We present a transformer-based neural network architecture that enables predicting link-level properties from maps of various dimensions and from sparse measurements.
  arXiv  Detail & Related papers  (2023-10-06T20:17:40Z)
• Enhancing Mapless Trajectory Prediction through Knowledge Distillation [19.626383744807068]
  High-definition maps (HD maps) may suffer from the high cost of annotation or restrictions of law that limits their widespread use. In this paper, we tackle the problem of improving the consistency of multi-modal prediction trajectories and the real road topology. Our solution is generalizable for common trajectory prediction networks and does not bring extra computation burden.
  arXiv  Detail & Related papers  (2023-06-25T09:05:48Z)
• Can Forward Gradient Match Backpropagation? [2.875726839945885]
  Forward Gradients have been shown to be utilizable for neural network training. We propose to strongly bias our gradient guesses in directions that are much more promising, such as feedback obtained from small, local auxiliary networks. We find that using gradients obtained from a local loss as a candidate direction drastically improves on random noise in Forward Gradient methods.
  arXiv  Detail & Related papers  (2023-06-12T08:53:41Z)
• Self-Supervised Training with Autoencoders for Visual Anomaly Detection [61.62861063776813]
  We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold. We adapt a self-supervised learning regime that exploits discriminative information during training but focuses on the submanifold of normal examples. We achieve a new state-of-the-art result on the MVTec AD dataset -- a challenging benchmark for visual anomaly detection in the manufacturing domain.
  arXiv  Detail & Related papers  (2022-06-23T14:16:30Z)
• Robust Depth Completion with Uncertainty-Driven Loss Functions [60.9237639890582]
  We introduce uncertainty-driven loss functions to improve the robustness of depth completion and handle the uncertainty in depth completion. Our method has been tested on KITTI Depth Completion Benchmark and achieved the state-of-the-art robustness performance in terms of MAE, IMAE, and IRMSE metrics.
  arXiv  Detail & Related papers  (2021-12-15T05:22:34Z)
• A Graph Convolutional Network with Signal Phasing Information for Arterial Traffic Prediction [63.470149585093665]
  arterial traffic prediction plays a crucial role in the development of modern intelligent transportation systems. Many existing studies on arterial traffic prediction only consider temporal measurements of flow and occupancy from loop sensors and neglect the rich spatial relationships between upstream and downstream detectors. We fill this gap by enhancing a deep learning approach, Diffusion Convolutional Recurrent Neural Network, with spatial information generated from signal timing plans at targeted intersections.
  arXiv  Detail & Related papers  (2020-12-25T01:40:29Z)
• Trajectory Prediction in Autonomous Driving with a Lane Heading Auxiliary Loss [1.1470070927586014]
  We propose a loss function which enhances trajectory prediction models by enforcing expected driving rules on all predicted modes. Our contribution to trajectory prediction is twofold; we propose a new metric which addresses failure cases of the off-road rate metric. We then use this auxiliary loss to extend the the standard multiple trajectory prediction (MTP) and MultiPath models.
  arXiv  Detail & Related papers  (2020-11-12T22:51:25Z)
• Promoting Connectivity of Network-Like Structures by Enforcing Region Separation [101.10228007363673]
  We propose a connectivity-oriented loss function for training deep convolutional networks to reconstruct network-like structures. The main idea behind our loss is to express the connectivity of roads, or canals, in terms of disconnections that they create between background regions of the image. We show, in experiments on two standard road benchmarks and a new data set of irrigation canals, that convnets trained with our loss function recover road connectivity so well.
  arXiv  Detail & Related papers  (2020-09-15T12:21:35Z)
• Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces [58.780482825156035]
  We will introduce a method for longitudinal prediction of cortical surfaces using a spatial graph convolutional neural network (GCNN) The proposed method is designed to model the cortical growth trajectories and jointly predict inner and outer curved surfaces at multiple time points. We will demonstrate with experimental results that our method is capable of capturing the nonlinearity oftemporal cortical growth patterns.
  arXiv  Detail & Related papers  (2020-09-06T18:46:04Z)
• Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance [95.86944752753564]
  We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
  arXiv  Detail & Related papers  (2020-07-28T07:34:30Z)
• Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks [107.77595511218429]
  In this paper, we investigate the empirical Rademacher complexity related to intermediate layers of deep neural networks. We propose a feature distortion method (Disout) for addressing the aforementioned problem. The superiority of the proposed feature map distortion for producing deep neural network with higher testing performance is analyzed and demonstrated.
  arXiv  Detail & Related papers  (2020-02-23T13:59:13Z)

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.