PhD Studentship – Machine Learning Enabling Non-linear Equalization in High Capacity Fibre Systems
Aston University – Engineering and Applied Science
|Funding for:||UK Students, EU Students, International Students|
|Placed on:||22nd September 2017|
|Closes:||4th October 2017|
Contract Type: Fixed Term (for 3 years)
Closing Date: 23.59 hours BST on Wednesday 04 October 2017
Interview Date: To be confirmed
Applications are invited for a three year Postgraduate studentship, supported by the School of Engineering and Applied Science, to be undertaken within the Aston Institute of Photonic Technologies (AIPT) at Aston University. The successful applicant will join an established experimental and theoretical group working on the area of optical communications.
The position is available to start in October 2017 or Jan 2018 (subject to negotiation)
This studentship includes a fee bursary to cover the home/EU fees rate, plus a maintenance allowance of £14,553 in 2017/18.
Applicants from outside the EU may apply for this studentship but will need to pay the difference between the ‘Home/EU’ and the ‘Overseas’ tuition fees, currently this is £12,005 in 2017/18. Confirmation that this funding support is in place will be required as part of the application process.
Background of the Project
Fibre non-linearity is a major degradation factor in optical fibre communication systems. Based on the recent advances in high-speed digital signal processing (DSP) and the global adoption of coherent detection, a number of techniques have been proposed in the electronic domain for mitigating non-linear impairments. However, most of those approaches are highly complex, since they require multiple computational steps along the link, and despite various simplifications that have been proposed their commercial deployment is still far from reality.
The field of machine learning offers powerful statistical signal processing tools for the development of equalizers capable of dealing with non-linear transmission effects. Contrary to conventional digital back propagation assisted methods, in machine learning the signal equalization and demodulation processes are treated jointly as a classification problem by mapping the baseband signal onto a space determined by the direct interpretation of a known training sequence. This can bring a significant reduction in the required number of computational steps, as well as, a substantial improvement in the accuracy of the equalization process.
Although machine learning based equalization has been quite extensively investigated in wireless systems, their application in optical transmission is completely unknown. The project envisions to expand this application range in the optical communication area and make machine learning a key enabler of next generation’s high capacity transmission systems.
The successful applicant should have a first class or upper second class honours degree or equivalent qualification in Electronic and/or Telecommunication Engineering, Mathematics, Physics or Computer Science. Preferred skill requirements include knowledge/experience of digital signal processing, machine learning, communication networks and optical fibre transmission.
We would particularly like to encourage applications from women seeking to progress their academic careers. Aston University is committed to the principles of the Athena SWAN Charter, recognised recently by a prestigious Silver Award to EAS, and we pride ourselves on our vibrant, friendly and supportive working environment and family atmosphere.
For informal enquiries about this project and other opportunities within the Optical Communications Research Group of AIPT, contact Dr. Stylianos Sygletos by email at firstname.lastname@example.org
The online application form, reference forms and details of entry requirements, including English language are available here.
Applications must also be accompanied by a research proposal giving an overview of the main themes of the research, and explaining how your knowledge and experience will benefit the project
Further particulars and application forms are available in alternative formats on request i.e. large print, Braille, tape or CD Rom.
If you have any questions, please do not hesitate to contact HR via email@example.com
If you are interested in this vacancy please apply via our website at: http://www.aston.ac.uk/
Deadline: October 04, 2017