Index Modulation using Reversed Source Coding Techniques

Index Modulation using Reversed Source Coding Techniques

Eligibility: UK/EU/International graduates with the required entry requirements

Duration: Full-Time – between three and three and a half years fixed term

Application deadline: This opportunity will only remain open until a suitable candidate is identified and appointed. Early application is therefore recommended

Interview date: Will be confirmed to shortlisted candidates

Start date: September 2021

For further details contact: Dr Ye Liu

Introduction

The Research Centre for Data Science (CDS) is based in the Faculty of Engineering, Environment and Computing (EEC) of Coventry University.  It provides a hub to develop cutting edge research in the areas of Artificial Intelligence, Data Science and Future Computing Technologies. The centre has a vocation to push the boundaries of both fundamental science and practical applications. 

About the project

Dating back to the 1960s, index modulation for orthogonal frequency division multiplexing (OFDM) has gained significant interest in the last decade due to its potential benefits in energy efficiency and error performance for communications. The idea is that information can be communicated when one turns the communication channel (subcarriers of OFDM) on and off. In other words, silence carries information!

The simple heuristic method that maps the on-off pattern of one subcarrier to one bit of information marks the beginning of recent index modulation research. To improve the amount of information that can be carried by the silence patterns, researchers in the last two years began to realize that Huffman coding can be applied but in a reversed manner. However, Huffman coding introduces error propagation, and the fundamental limit of spectral efficiency of index modulation using reversed source coding techniques has not been reached. Further research on the application of arithmetic-coding based index modulation schemes appears to be a promising direction for addressing the aforementioned issues. Moreover, it is worth mentioning that the same idea can be applied to utilize other degrees-of-freedom in communication systems, e.g., antennas (known as spatial modulation) and orbital angular momentum.

The project is to be co-supervised with Dr Taufiq Asyhari from Birmingham City University.

Funding 

This project is offered on a self-funded or externally funded basis. We particular welcome applications from candidates with funding from their home governments or sponsorship from companies.

For the academic year 2020/21:  English, Welsh and EU students can apply to borrow up to £26,445 to help cover the cost of their PhD.  Find out more about PHD Loans.

Benefits

The successful candidate will receive comprehensive research training including technical, personal and professional skills.  All researchers at Coventry University (from PhD to Professor) are part of the Doctoral College which provides support with career development activities.

The candidate for this project will work in the Centre for Data Science amongst other researchers employing similar data science techniques to a wide variety of problems and applications.  With access to cutting edge techniques and high performance computing resources, the Centre for Data Science is an ideal place to pursue a PhD in Data Science, Machine Learning, or Artificial Intelligence.

Entry requirements

  • A minimum of a 2:1 first degree in a relevant discipline/subject area with a minimum 60% mark in the project element or equivalent with a minimum 60% overall module average. 
    PLUS 
    the potential to engage in innovative research and to complete the PhD within a 3.5 years
  • a minimum of English language proficiency (IELTS overall minimum score of 7.0 with a minimum of 6.5 in each component)

In addition

  • Applicants should have a background in digital communication theory and signal processing.
  • Knowledge in information theory and proficiency in engineering mathematics is also beneficial.

How to apply

All applications require full supporting documentation, a covering letter showing how the applicant’s expertise and interests are relevant to the project. 

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