Enhancing 5G Fixed Wireless Access in Rural Settings - Machine Learning-Driven Resource Optimization

Aug 5, 2026

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Providing broadband access to rural communities remains a critical societal challenge that helps bridge the digital divide. While 5G wireless networks can support rural broadband, base-station placement becomes difficult, especially when using millimeter-wave frequencies that require unobstructed line of sight. Conventional ray-tracing for varied terrain is computationally costly and impractical for large regions. This thesis applies machine learning with DTED and MATLAB to identify optimal base-station locations using exhaustive benchmarks, differential evolution optimization, and graph neural networks.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages59

Reading Time2 h 29 min

AccessFree to download

Abstract

Problem background: rural 5G fixed wireless access and line-of-sight constraints

Proposed approach: DTED, MATLAB, and machine learning methods

Optimization workflow: sampling, exhaustive search benchmark, differential evolution

Extension: graph neural networks for further optimization

Graduate Theses, Dissertations, and Problem Reports 2024 Enhancing 5G Fixed Wireless Access in Rural Settings via Machine Learning-Driven Resource Optimization Maryam Amini Follow this and additional works at: [https://researchrepository.wvu.edu/etd](https://researchrepository.wvu.edu/etd) Part of the Electrical and Electronics Commons, and the Systems and Communications Commons Recommended Citation Amini, Maryam, "Enhancing 5G Fixed Wireless Access in Rural Settings via Machine Learning-Driven Resource Optimization" (2024) . Graduate Theses, Dissertations, and Problem Reports. 12402. [https://researchrepository.wvu.edu/etd/12402](https://researchrepository.wvu.edu/etd/12402) This Thesis is protected by copyright and/or related rights. It has been brought to you by the The Research Repository @ WVU with permission from the rights-holder(s) . You are free to use this Thesis in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you must obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself. This Thesis has been accepted for inclusion in WVU Graduate Theses, Dissertations, and Problem Reports collection by an authorized administrator of The Research Repository @ WVU. For more information, please contact [researchrepository@mail.wvu.edu](researchrepository@mail.wvu.edu). Enhancing 5G Fixed Wireless Access in Rural Settings via Machine Learning-Driven Resource Optimization Maryam Amini Thesis submitted to the College of Engineering and Mineral Resources at West Virginia University in partial fulfillment of the requirements for the degree of Master of Science in Electrical Engineering Nasser M. Nasrabadi, Ph.D. Brian Woerner, Ph.D. Matthew C. Valenti, Ph.D. , Chair Lane Department of Computer Science and Electrical Engineering Morgantown, West Virginia 2024 Keywords: 5G Fixed Wireless Access, Rural Internet Copyright 2024 Maryam Amini Abstract Enhancing 5G Fixed Wireless Access in Rural Settings via Machine Learning-Driven Resource Optimization by Maryam Amini Master of Science in Electrical Engineering West Virginia University Matthew C. Valenti, Ph.D. , Chair Providing broadband access to rural communities continues to be an important societal problem whose solution would help to break down the digital divide. While 5G wireless networks may be used for rural broadband, a key challenge is the placement of base stations, which is exacerbated by the use of high frequencies in the millimeter-wave band. Such technology requires an unobstructed line of sight, demanding meticulous planning of the number, height, and location of base stations for optimal coverage. Conventional methods, such as ray-tracing to simulate signal propagation across varied terrain, are computational costly and not feasible for vast coverage areas. These constraints pose significant hurdles for extensive network deployment, especially in rural U.S. areas with complex topographies like mountainous regions. In this thesis, we investigate the fusion of machine learning algorithms with Digital Terrain Elevation Data (DTED) and leverage MATLAB’s analytical capabilities to identify optimal locations for base stations in order to enhance line-of-sight (LoS) coverage in rural areas. Preston County, West Virginia, serves as a compelling case study for this research. The choice of this location is based on two main factors: its proximity to West Virginia University (WVU) provides logistical advantages for field studies, and its characteristics of sparse population and rugged mountainous terrain present typical obstacles encountered in rural telecommunications. The methodology begins by spatially sampling the region, considering 100 potential sites for base stations. An exhaustive search technique is then deployed to identify a

FAQs

What problem does the thesis address?

The thesis addresses how to provide effective broadband coverage in rural areas using 5G fixed wireless access, where line-of-sight requirements make base-station placement challenging.

Why are conventional propagation simulations difficult for this use case?

Conventional methods like ray-tracing are computationally expensive and become impractical for wide coverage areas with complex terrain.

What machine learning or optimization methods are used to improve base-station placement?

The study uses an exhaustive search to form a benchmark, then applies the Differential Evolution algorithm, and finally explores Graph Neural Networks for further optimization.

Aug 5, 2026

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