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Cellular Network Radio Propagation Modeling with Deep Convolutional Neural Networks

Authors:

Xin ChenAuthors Info & Claims

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 2378 - 2386

https://doi.org/10.1145/3394486.3403287

Published: 20 August 2020 Publication History

Abstract

Radio propagation modeling and prediction is fundamental for modern cellular network planning and optimization. Conventional radio propagation models fall into two categories. Empirical models, based on coarse statistics, are simple and computationally efficient, but are inaccurate due to oversimplification. Deterministic models, such as ray tracing based on physical laws of wave propagation, are more accurate and site specific. But they have higher computational complexity and are inflexible to utilize site information other than traditional global information system (GIS) maps.

In this article we present a novel method to model radio propagation using deep convolutional neural networks and report significantly improved performance compared to conventional models. We also lay down the framework for data-driven modeling of radio propagation and enable future research to utilize rich and unconventional information of the site, e.g. satellite photos, to provide more accurate and flexible models.

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Cited By

Sharif SManzoor SEjaz W(2025)Priority‐based resource optimisation and user association in integrated networksIET Networks10.1049/ntw2.1214014:1Online publication date: 24-Jan-2025
https://doi.org/10.1049/ntw2.12140
Hosseinzadeh SAshawa MOwoh NLarijani HCurtis K(2024)Explainable Machine Learning for LoRaWAN Link Budget Analysis and ModelingSensors10.3390/s2403086024:3(860)Online publication date: 29-Jan-2024
https://doi.org/10.3390/s24030860
Sánchez IVallejo FJátiva PDehghan Firoozabadi A(2024)An Analysis of WiFi Coverage Modeling for a Hotspot in the Parish of Checa Employing Deterministic and Empirical Propagation ModelsApplied Sciences10.3390/app14231112014:23(11120)Online publication date: 28-Nov-2024
https://doi.org/10.3390/app142311120
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Index Terms

Cellular Network Radio Propagation Modeling with Deep Convolutional Neural Networks
1. General and reference
  1. Document types
    1. General conference proceedings
2. Networks
  1. Network components
    1. Wireless access points, base stations and infrastructure
  2. Network types
    1. Mobile networks

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Published In

cover image ACM Conferences

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

August 2020

3664 pages

ISBN:9781450379984

DOI:10.1145/3394486

General Chairs:
Rajesh Gupta
UC San Diego, USA
,
Yan Liu
USC, USA
,
Program Chairs:
Mohak Shah
LG Electronics, USA
,
Suju Rajan
Linkedin, USA
,
Publications Chairs:
Jiliang Tang
Michigan State, USA
,
B. Aditya Prakash
Georgia Tech, USA

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 20 August 2020

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KDD '20

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KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

July 6 - 10, 2020

CA, Virtual Event, USA

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Overall Acceptance Rate 783 of 6,627 submissions, 12%

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The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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Cited By

Sharif SManzoor SEjaz W(2025)Priority‐based resource optimisation and user association in integrated networksIET Networks10.1049/ntw2.1214014:1Online publication date: 24-Jan-2025
https://doi.org/10.1049/ntw2.12140
Hosseinzadeh SAshawa MOwoh NLarijani HCurtis K(2024)Explainable Machine Learning for LoRaWAN Link Budget Analysis and ModelingSensors10.3390/s2403086024:3(860)Online publication date: 29-Jan-2024
https://doi.org/10.3390/s24030860
Sánchez IVallejo FJátiva PDehghan Firoozabadi A(2024)An Analysis of WiFi Coverage Modeling for a Hotspot in the Parish of Checa Employing Deterministic and Empirical Propagation ModelsApplied Sciences10.3390/app14231112014:23(11120)Online publication date: 28-Nov-2024
https://doi.org/10.3390/app142311120
Ghaderibaneh MZhan CGupta H(2024)DeepAlloc: Deep Learning Approach to Spectrum Allocation in Shared Spectrum SystemsIEEE Access10.1109/ACCESS.2024.335203412(8432-8448)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3352034
Wu HDing TQin HZhang X(2024)Efficient high‐fidelity deep convolutional generative adversarial network model for received signal strength reconstruction in indoor environmentsElectronics Letters10.1049/ell2.1326560:13Online publication date: 8-Jul-2024
https://doi.org/10.1049/ell2.13265
Wang XYang J(2023)A Big Data-Driven Deep Transfer Learning Approach for Path Loss Prediction in Mobile CommunicationsProceedings of the 2023 9th International Conference on Computing and Artificial Intelligence10.1145/3594315.3594375(584-588)Online publication date: 17-Mar-2023
https://dl.acm.org/doi/10.1145/3594315.3594375
Krause ASchulz PBurmeister FFettweis G(2023)Improving Radio Environment Maps with Joint Communications and Sensing: An Outlook2023 IEEE 3rd International Symposium on Joint Communications & Sensing (JC&S)10.1109/JCS57290.2023.10107465(1-6)Online publication date: 5-Mar-2023
https://doi.org/10.1109/JCS57290.2023.10107465
Sharif SLiaq MEjaz W(2023)Multi-objective Resource Optimization in Space-Aerial-Ground-Sea Integrated Networks2023 IEEE Symposium on Wireless Technology & Applications (ISWTA)10.1109/ISWTA58588.2023.10249639(109-114)Online publication date: 15-Aug-2023
https://doi.org/10.1109/ISWTA58588.2023.10249639
Qiu KBakirtzis SSong HWassell IZhang J(2023)Deep Learning-Based Path Loss Prediction for Outdoor Wireless Communication SystemsICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP49357.2023.10095501(1-2)Online publication date: 4-Jun-2023
https://doi.org/10.1109/ICASSP49357.2023.10095501
Burmeister FPalaios AGeuer PKrause AJacob RSchulz PFettweis G(2023)Deep Learning-Supported Kriging for Interpolation of High-Resolution Indoor REMs2023 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)10.1109/EuCNC/6GSummit58263.2023.10188255(54-59)Online publication date: 6-Jun-2023
https://doi.org/10.1109/EuCNC/6GSummit58263.2023.10188255
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