University of Birmingham Featured PhD Programmes
University of Kent Featured PhD Programmes
University of Liverpool Featured PhD Programmes
Catalysis Hub Featured PhD Programmes
Brunel University London Featured PhD Programmes

Learning-based Anomaly Detection and Prediction in 5G Mobile Networks. Computer Science PhD.

  • Full or part time
  • Application Deadline
    Monday, May 13, 2019
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

The University of Exeter EPSRC DTP (Engineering and Physical Sciences Research Council Doctoral Training Partnership) is offering up to 4 fully funded doctoral studentships for 2019/20 entry. Students will be given sector-leading training and development with outstanding facilities and resources. Studentships will be awarded to outstanding applicants, the distribution will be overseen by the University’s EPSRC Strategy Group in partnership with the Doctoral College.

Supervisors:
Dr Yulei Wu, Department of Computer Science, College of Engineering, Mathematics and Physical Sciences
Prof Geyong Min, Department of Computer Science, College of Engineering, Mathematics and Physical Sciences

Project description:
With the rapid development of 5G and the high flexibility of accommodating diversified applications with different requirements, network and service robustness becomes increasingly important. Anomaly detection and prediction plays key roles in ensuring its robustness and is a hot research topic. Existing anomaly detection and prediction methods highly rely on threshold setting for good detection performance under specific scenarios, making the solutions not effectively applicable to 5G mobile networks with time-varying network and service requirements.

This EPSRC funded PhD studentship project aims to research on learning-based methods that could achieve self-improvement on the accuracy of anomaly detection and prediction, and further fosters cutting-edge research and development of 5G mobile networks. Therefore, the student will be using up-to-date machine learning technologies such as Recurrent Neural Network and Reinforcement Learning are the primary methodologies to solve the problem and achieve the aims of this project. Candidates who have background on or who are interested in machine learning, data mining, artificial intelligence, and 5G networks are suitable for this research.

Funding Notes

For successful eligible applicants the studentship comprises:

An index-linked stipend for up to 3.5 years full time (currently £14,777 per annum for 2018/19), pro-rata for part-time students.
Payment of University tuition fees (UK/EU)
Research Training Support Grant (RTSG) of £5,000 over 3.5 years, or pro-rata for part-time students

Related Subjects

Email Now

Insert previous message below for editing? 
You haven’t included a message. Providing a specific message means universities will take your enquiry more seriously and helps them provide the information you need.
Why not add a message here
* required field
Send a copy to me for my own records.

Your enquiry has been emailed successfully





FindAPhD. Copyright 2005-2019
All rights reserved.