Postgraduate Research Opportunity

Developing novel AI techniques to mobile network coverage

Details

Title: SH16: Developing novel AI techniques to mobile network coverage

Funding: Full time Home/EU fees and a stipend of £15,009 p.a.

Application deadline: 14 June 2019

Start date: October 2019

Duration: 3 years (full time)

Location: Colchester Campus

Based in: School of Computer Science and Electronic Engineering

This studentship is now closed. See our current opportunities.

Overview 

Mobile phone operators have a need to increase its mobile network coverage in the UK. There are two difficulties with the current network. Firstly, not all areas of the UK are within range of a mobile mast. Secondly, in some areas there is insufficient bandwidth to handle the number of calls being made.

Both of these problems are particularly acute in urban areas, where there is high usage, a high density of tall buildings which obstruct signals, and a lack of suitable mast locations. The choice of cell location depends on a number of factors including elevation (increases coverage):

  • Cost of connection to the existing fibre network
  • Cost of renting space on suitable structures
  • Power
  • Choice of frequency
  • And other factors

Aims and objectives

  • Developing novel AI techniques which can operate in real time to optimise the multiple objectives involved in mobile coverage optimisation 
  • Choosing the best location of cells to maximise coverage while minimising cost is an optimisation problem.
  • Develop real time planning system which can deal with any emergent situation
  • Develop Explainable Artificial Intelligence (XAI) systems which can by understood, analysed and augmented by the lay user
  • Develop adaptive AI models which update the learn models with any changes to the environment or the service operations

Funding

The award consists of a full Home/EU fee waiver or equivalent fee discount for overseas students (further fee details), a doctoral stipend equivalent to the Research Councils UK National Minimum Doctoral Stipend (£15,009 in 2019-20), plus £2,500 training bursary via Proficio funding, which may be used to cover the cost of advanced skills training including conference attendance and travel.

Criteria

At a minimum, the successful applicant will have a good honours BSc degree (1st class or high 2:1, or equivalent) in computer science or related subjects. An MSc with Merit or Distinction is desirable (but not essential for students with a first class degree). Strong analytical and mathematical skills are required, as well as good programming skills.

Essential skills:

  • Qualified to graduate level with experience in computer science, computational intelligence and software engineering. 
  • A creative and innovative approach to solving complex technical problems.

Understanding and/or experience in/of:

  • Computational intelligence (fuzzy systems, neural networks or/and evolutionary computation).
  • Software engineering principles, technologies and frameworks in developing reusable components.
  • An inquiring mind with a preference for working within less defined boundaries, excellent creativity, self-motivation and good written and oral communication skills, and must be passionate about achieving excellent results.

How to apply

You can apply for this postgraduate research opportunity online.

Please include your CV, covering letter, personal statement, and transcripts of UG and Masters degrees in your application.

The University has moved to requiring only one reference for PhD applications and these can be received after a conditional offer has been made so the absence of these will not hold up the recruitment process.

Instruction to applicants

When you apply online you will be prompted to fill out several boxes in the form:

  • For "Course title" please put "PhD Computer Science"
  • For "Proposed research topic or area of research" please put the title of this studentship (SH16: Developing novel AI techniques to mobile network coverage)
  • For "If you have contacted a potential supervisor..." please put the name of the supervisor (Professor Hani Hagras)

If you have any informal queries about this opportunity please email the supervisor, Professor Hani Hagras (hani@essex.ac.uk)