All connected services across emergent edge-computing devices will run directly or indirectly from the future 6G wireless ecosystems (e.g., unmanned automotive/ship, digital twins, extended reality, etc.) In 2025, the 5G will cover more than 50% of total wireless media revenue and this grows up to about 80% in 2028. The future radio access network is facing with a challenge of supporting the flood of new data from the emergent edge devices. Such devices will run in unforeseeable channel states arisen from highly diverse 6G wireless ecosystems. Moreover, the ever-increasing devices in different conditions run in an extensively complex domain (e.g., terrestrial and non-terrestrial networks), disallowing sophisticated central coordination policy among devices and central unit in real-time under limited communication and computing resources.
This PhD project will open a new paradigm for AI communications to extract uniquely identifiable features from time-varying patterns of PHY and/or MAC layer signals. Main objective of this project is to develop new machine learning and signal processing algorithms on how each AI agent would better find and manage its own multi-access rules at real time in future autonomy. Simultaneously, the devised solutions are to provide high reliability and resilience in unpredictable time-varying autonomy at reduced signalling burdens and energy consumption.
Entry requirements:
Candidates should have (or expect to obtain) a minimum of a UK upper second class honours degree (2.1) or equivalent in Electronic and Electrical Engineering, Physics, Computer Science, Mathematics or a closely related subject.
How to apply:
Applicants should apply via the University’s online application system at https://www.york.ac.uk/study/postgraduate-research/apply/. Please read the application guidance first so that you understand the various steps in the application process.
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