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Funded PhD Opportunity

Low-complexity Detection Algorithms for Faster-than-Nyquist Signaling

Subject: Engineering


There is an increasing demand to improve the spectral efficiency (SE) to support gigabit experience to mobile users in future 5G and beyond communications or to support up to 1 Tbps data rates in long-haul optical communications. To achieve such goal, there is a need to explore novel and innovative technologies that can be implemented for single or multicarrier optical or wireless communication systems to improve the SE. The SE measured in bits/sec/Hz, is defined as the number of information bits carried per a given time and bandwidth and it can be improved by either changing the transmission time or bandwidth.

However, the transmission time and bandwidth are related in the sense that one can be traded for the other. Additionally, they are limited and costly resources that in most cases we cannot afford to change them. Another possible way to increase the SE is to adopt higher order modulation, i.e., M-ary modulation; however, to increase the SE by 1, this will be at the expense of almost doubling the signal-to-noise ratio (SNR)—to maintain the same error probability—at higher values of M. Conventional digital communication systems use orthogonal pulses (with respect to shifts by integer number of symbol duration) for transmission in time-domain to avoid having intersymbol interference (ISI).

The roots of such design principles stem from the Nyquist theorem and the optimal detection process is simple and can be achieved on a symbol-by-symbol basis. Faster-than-Nyquist (FTN) signaling is a novel transmission technique that intentionally violates the Nyquist limit and transmits pulses at a rate beyond the Nyquist limit, and hence, ISI is unavoidable.The FTN signaling concept has been extended to the frequency-domain as well to improve the SE of multi-carrier systems.

Our objective in this project is to design low-complexity detection algorithms for FTN signalling in both time-domain single carrier communications and frequency-domain multicarrier communication systems. In general, and as a binary/non-binary sequence estimation problem in the presence of interference, maximum likelihood (ML) or maximum a posteriori probability (MAP) estimations can be used to find the optimal transmit sequence; however, their prohibitive computational complexity prevents practical implementations.

Our approach to tackle such problem is based on a key observation that the interference at the receivers of FTN signalling is different from its counterpart resulting from the propagation through dispersive channel as it has a special trellis structure that is known at the transmitter. Such a structure can be exploited to design precoding techniques at the transmitter and/or reduce the complexity of ML/MAP estimation or their approximations at the receivers.

Please note the student working on this project is expected to have a communication theory and signal processing background and very good experience in one of the programming languages. The student will work in an office environment and use programming language, e.g., Matlab, on a daily basis to test the developed theory.

Essential criteria

  • Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC)


    Vice Chancellors Research Scholarships (VCRS)

    The scholarships will cover tuition fees and a maintenance award of £14,777 per annum for three years (subject to satisfactory academic performance). Applications are invited from UK, European Union and overseas students.


    The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £ 14,777 per annum for three years. EU applicants will only be eligible for the fees component of the studentship (no maintenance award is provided).  For Non EU nationals the candidate must be "settled" in the UK.

Other information

The Doctoral College at Ulster University

Key dates

Submission deadline
Monday 18 February 2019

Interview Date
March 2019


Jordanstown campus

Jordanstown campus
The largest of Ulster's campuses

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Other supervisors


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