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On-device learning and signal processing for evolvable wireless communications


Department of Electronic Engineering

York United Kingdom Communications Engineering Electrical Engineering

About the Project

Due to the rapidly increasing demand for autonomous devices, mobile autonomous systems are more reliant on communicated data to make their decisions. Future autonomous devices (e.g., self-driving robots) will work often in high risk situations where today’s pre-fixed communications are challenging and demand a 1000-fold of today’s traffic by 2022. The state-of-the-art practices in machine learning (ML) are recently applied to wireless communications that can offer autonomous data recovery but with a focus of centralised computing and expensive training. This central intelligence associated with a large number of users and their signals are too burden and lagging, and today’s ML are inaccessible to low-complexity devices and users who cannot customise a complex structure of deep neural networks.

This PhD programme will open a new paradigm for on-device learning communications in a range of autonomous scenarios, extracting uniquely identifiable multi-dimensional physical layer features. Main objective of this project is to develop new machine (deep) learning and signal processing algorithms to be applied to both modulation and multi-access solutions for future evolvable wireless systems. For example, a convergence between artificial intelligence and physical layer technology will make connectivity to a variety of devices, more resilient to unpredictable wireless environments. With limited (if not zero) prior knowledge on unpredictable surrounding, this project aims to develop opportunity for automatically modulating/demodulating data at edge-devices in close proximity to local data source, which should be programmable at run-time. Simultaneously, the devised solutions are to provide high reliability and resilience in unpredictable time-varying scenarios at reduced signalling burdens and energy consumption.

Entry requirements:

Candidates must 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 related subject.

How to apply:

Applicants must 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. To apply, please select the PhD in Electronic Engineering for April or July 2021 entry. Please specify in your PhD application that you would like to be considered for this studentship.

Applications for this studentship will be considered on a first-come, first-served basis and the position will be filled as soon as a suitable applicant is identified.


Funding Notes

This studentship will cover the tuition fee at the home/EU rate (£4,407 in 2020/21) and a stipend at the standard research council rate for a period of up to 3.5 years (£15,285 in 2020/21). International candidates are also welcome to apply but will be required to pay the difference between the UK and international tuition fee rates (approximately £17,000 per year for the duration of the programme).

Applications for this studentship will be considered on a first-come, first-served basis and the position will be filled as soon as a suitable applicant is identified.

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