Intelligent and Dynamic Spectrum Management for Beyond 5G
type: CreativeWork
description: The 5G network introduces virtualisation technology and network slicing, that allows an agile creation and (re)configuration of multiple network slices on the same infrastructure that can be independently managed. This provides the impetus for new business models and third party operators, that support private networks targeting verticals such as manufacturing industry, warehouse and logistics, mining and maritime ports, among others. This requires the optimised management of radio spectrum, where traditional rule-based techniques are facing serious challenges – they can hardly consider all network and slice combinations in 5G in order to fully exploit all the available degrees of freedom. To address this challenge, this thesis proposes an intelligent framework to automate spectrum management and adapt to the dynamically varying network conditions. The proposed concept is engineered to address a spectrum sharing scheme with very high granularity, where we divide the network coverage area in pixels that also maps to network slices, and allocate to every pixel and network slice, the required spectrum to reflect demand in that area for a given time period. This avoids the need to overprovision spectrum to each slice, and in all territory where that slice is offered, according to the busy hour traffic demand. The proposed spectrum sharing scheme is implemented in software to exploit the programmability of 5G networks, and thus avoiding limiting the number of slices that can be offered in each pixel. Therefore, it can support all the spectrum sharing schemes proposed in the literature (TV white spaces, licensed shared access, citizens broadband radio service, etc.), and goes beyond the state-of-art by allowing to implement many other sharing schemes, with different priorities to access and leave the spectrum, and different interference protection levels. To implement the proposed concept, a 3GPP-compliant radio management architecture is proposed, that leverages the 3GPP…
abstract: The 5G network introduces virtualisation technology and network slicing, that allows an agile creation and (re)configuration of multiple network slices on the same infrastructure that can be independently managed. This provides the impetus for new business models and third party operators, that support private networks targeting verticals such as manufacturing industry, warehouse and logistics, mining and maritime ports, among others. This requires the optimised management of radio spectrum, where traditional rule-based techniques are facing serious challenges – they can hardly consider all network and slice combinations in 5G in order to fully exploit all the available degrees of freedom. To address this challenge, this thesis proposes an intelligent framework to automate spectrum management and adapt to the dynamically varying network conditions. The proposed concept is engineered to address a spectrum sharing scheme with very high granularity, where we divide the network coverage area in pixels that also maps to network slices, and allocate to every pixel and network slice, the required spectrum to reflect demand in that area for a given time period. This avoids the need to overprovision spectrum to each slice, and in all territory where that slice is offered, according to the busy hour traffic demand. The proposed spectrum sharing scheme is implemented in software to exploit the programmability of 5G networks, and thus avoiding limiting the number of slices that can be offered in each pixel. Therefore, it can support all the spectrum sharing schemes proposed in the literature (TV white spaces, licensed shared access, citizens broadband radio service, etc.), and goes beyond the state-of-art by allowing to implement many other sharing schemes, with different priorities to access and leave the spectrum, and different interference protection levels. To implement the proposed concept, a 3GPP-compliant radio management architecture is proposed, that leverages the 3GPP…
author: Morgado, António Jorge Da Silva
language: eng
keywords: Integrated Access and Backhaul, Spectrum Management, Radio Systems, Machine Learning, Reinforcement Learning
date published: 2026
date modified: 2026-07-24T04:40:03Z
provider: University of South Wales
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of South Wales