Applications of Artificial Intelligence to Electromagnetism and Indoor Wireless Networks

Fifth-generation (5G) and beyond (B5G) mobile networks are anticipated to revolutionise the structure of the wireless ecosystem and support a set of highly heterogeneous applications, handing a substantially larger volume of mobile traffic with diverse requirements in terms of bandwidth, throughput, latency, quality of service and reliability. Remarkably, although the preponderance of this traffic is generated in indoor environments, the largest part of the legacy communication systems radio access network is deployed outdoors. As a result, next-generation networks are anticipated to align with the emerging need to serve indoor users, and therefore, major vendors and mobile network operators expect indoor cellular networks (ICNs) to be extensively deployed in 5G/B5G systems. Consequently, the installation of new components within the heart of the radio access network, in conjunction with the increasing network complexity and user requirements, calls for expedient electromagnetic wave propagation modelling tools and optimisation frameworks that can assist the design of ICNs, ensuring the orchestrated and prosperous operation of the wireless ecosystem. Furthermore, to enable their optimal functionality it is necessary to comprehend the characteristics of the mobile service demands generated by ICNs. Aiming at tackling these open problems, this thesis probes how artificial intelligence (AI) can be leveraged in the context of computational electromagnetics and indoor wireless networking. To this end, first, an innovative solution to Maxwell’s equations through graph neural network (GNN) message passing is introduced by establishing a causal connection between GNNs and the finite-difference time-domain method. Consequently, an expedient and credible data-driven radio propagation modelling tool, named EM DeepRay, is developed by coupling a high-performance ray-tracer with a convolutional encoder-decoder. Then, the computational efficiency and robustness of EM DeepRay are exploited to develop an AI-assisted indoor wireless network planning framework, and to assess the uncertainty in network’s performance. Finally, a characterisation of the mobile service usage across a nationwide ICN deployment is provided, indicating that ICNs inherently manifest a limited set of mobile application utilisation profiles, which are not present in conventional outdoor macro base stations.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2024

Author dc:creator

• Bakirtzis, Stefanos Sotirios

Advisors dc:contributor.advisor

• Wassell, Ian
• Zhang, Jie

• Artificial Intellgience
• Deep Learning
• Electromagnetism
• Radio Propagation
• Traffic Analysis
• Wireless Communications

Licence

• https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/b9da415c-6448-4a40-8eca-301822777325/download
• https://creativecommons.org/licenses/by-nc-nd/4.0/

Language dc:language

eng

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.113346

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/375825