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Environment mapping using wireless channel state information and deep learning

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posted on 28.03.2022, 15:46 by Adrian Donarski
Situational awareness is becoming ubiquitous with modern communication technologies. Traditionally, user requirements for location based services have evolved from being driven only by government legislation for improved emergency response, to that of social and market based pressures including social-networking, geo-marketing and augmented reality. Not only do these applications necessitate improved localisation, they must also include awareness of the surrounding environment. Furthermore, recent years have seen a marked explosion in Artificial Intelligence (AI), machine learning and deep learning. Such tools enable inference to be drawn from complex data when traditional mathematical techniques cannot. This thesis explores the use of AI and wireless Channel State Information (CSI) for obtaining situational awareness through environment mapping. Several deep learning algorithms are developed that permit estimation of interior room dimensions using wireless Channel Impulse Responses (CIRs). Moreover the physical effects that bandwidth and received signal power incur on a CIR, and hence on room dimension prediction, is investigated yielding results with greater than 90% accuracy. This demonstrates that existing consumer wireless systems with similar physical constraints, should be capable of accurately estimating room dimensions given multiple CIR measurements from multiple receiver locations -- abstract.


Table of Contents

Chapter 1. Introduction -- Chapter 2. Background -- Chapter 3. Literature review -- Chapter 4. Room dimension estimation using feed-forward neural networks -- Chapter 5. Room dimension estimation using recurrent neural networks -- Chapter 6. Conclusion -- Appendices


Bibliography: pages 51-56 Theoretical thesis.

Awarding Institution

Macquarie University

Degree Type

Thesis MRes


MRes, Macquarie University, Faculty of Science and Engineering, School of Engineering

Department, Centre or School

School of Engineering

Year of Award


Principal Supervisor

Iain Collings

Additional Supervisor 1

Stephen Hanly


Copyright Adrian Donarski 2020




1 online resource (xiv, 66 pages)

Former Identifiers

mq:72122 http://hdl.handle.net/1959.14/1281605