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Cost-effective learning and signal processing for evolvable massive wireless communications


Department of Electronic Engineering

About the Project

Due to the rapidly increasing demand for a huge data, we are in the transition region from 4G (LTE-Advance) systems to 5G and beyond 5G systems. In 2025, the 5G will cover more than 50% of total wireless media revenue and this grows up to about 80% in 2028. Accordingly, the average monthly traffic per 5G subscriber is envisaged to grow from 11 GB in 2019 to 84 GB in 2028. Next generation wireless network is facing with a challenge of supporting the flood of new data with limited resource at edge mobile devices/sensors. Such a deluge of new data in diverse applications is a critical problem to mobile operators, vendors and IT manufacturers because today’s management systems cannot support the increasing diverse data. Moreover, the state-of-the-art practices in deep learning are inaccessible to low-complexity devices and users who cannot customise a big dataset of deep neural networks.

This PhD programme will open a new paradigm for cost-effective 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 January 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 (non-EU) candidates are also welcome to apply but will be required to pay the difference between the UK/EU 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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