Research ThemesAutonomous Sensing PlatformsSensor Signal ProcessingAimTo develop ambient backscatter communication systems enhanced with AI-driven signal processing for secure, low-power data exchange in defence environments, enabling covert sensing and communication among autonomous platforms.ObjectivesDesign and optimise ambient backscatter transceivers for secure, energy-autonomous data exchange in contested RF environments.Develop machine learning algorithms for adaptive signal detection, classification, sensing and interference mitigation.Integrate secure communication protocols suitable for emissions-controlled and covert operational scenarios.Build and evaluate a prototype system demonstrating real-time performance and resilience in representative defence settings.DescriptionThis PhD project builds on previous pioneering work (LORAB) in ambient FM backscatter to enable secure, battery-less wireless communications in defence scenarios. These systems reuse existing RF signals (e.g. FM, cellular) for data modulation, producing signals that are several dB below the carrier and nearly undetectable, making them ideal for stealth operations. Their ultra-low power operation allows them to run on supercapacitors and energy harvested from the environment. The project will enhance CSS-based modulation and machine learning for improved range, security, and adaptability. With the current interest in integrated sensing and communications (ISAC)[JT1] , this work will also explore possible sensing functions of such signals. The goal is a portable, autonomous sensor tag capable of silent communication over hundreds of metres, using simple SDR-based receivers. This silent and thermally low-signature technology enables covert sensing of the environment or personnel without revealing the location or activity of the node. Its resilience will be tested under jamming and interception conditions, providing a low-profile solution for secure battlefield and intelligence applications. Further information Relevant references:Daskalakis, S. N., et al., “Ambient FM Backscatter: Towards Long-Range, Zero-Power IoT Communication,” Sensors, MDPI, 2022. https://daskalakispiros.com/files/daskalakispis_mdpi_2022.pdfKimionis, J., Daskalakis, S. N., et al., “Backscatter Communications for Wireless Connectivity in IoT,” Nature Electronics, 2021. https://daskalakispiros.com/files/kimionis_nature_2021.pdfDaskalakis, S. N., et al., “Dual-Polarized Backscatter Sensor for RF and Wireless Applications,” IEEE Transactions on Microwave Theory and Techniques, 2018. https://daskalakispiros.com/files/daskalakis_mtt_2018.pdfD. Galappaththige, et al., "Integrated Sensing and Backscatter Communication," in IEEE Wireless Communications Letters, vol. 12, no. 12, pp. 2043-2047, Dec. 2023. Closing date: Sat, 31/01/2026 - 12:00 Apply now Principal Supervisor Dr Spyridon Daskalakis Assistant Supervisor Prof John Thompson Eligibility Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants. Funding Home rate fees and stipend are available for this position.
