Applications are invited for a 3.5 year Ph.D. studentship on the theme described below, as part of The University of Sheffield’s Communications Research Group and EPSRC Millimetre Wave Measurement Laboratory. The full studentship is available to UK home students. International students are eligible for a maintenance allowance and for partial funding towards tuition fees.
The concept of Software Defined Radio (SDR) is now relatively mature, having been applied to commercial radio system design and integrated circuit design tasks for several years. The move to an ‘all-digital’ transceiver, where the antenna terminates in an Analogue to Digital Converter (ADC) or Digital to Analogue Converter (DAC), with the whole of the radio function being performed using digital electronics and DSP, is seen as the ultimate technological goal. This is particularly true for radios requiring an extremely high degree of reconfigurability or low manufacturing cost. However, hybrid RF analogue & SDR systems remain vital for very high-performance RF applications, especially for millimetre wave (mmWave) radio systems.
The application of Artificial Intelligence and Machine Learning techniques in wireless communications systems is growing. One exciting area is in the use of such techniques applied directly to the signals at the radio’s hardware or physical layer. This could allow next generation radio systems to identify and ultimately demodulate a signal directly, regardless of channel response or impairment.
The purpose of this research project is to investigate present state-of-the-art aspects of next generation SDR for mmWave and then choose an area for further research that complements group activity. The research topic will be chosen from one or more of (but not limited to) the following themes, as relevant to the ongoing group’s research activity:-
• RF Sampling transceiver architectures
• Application of AI / Machine Learning to physical layer signals
• Efficient techniques for all–digital up-conversion / down-conversion
• Linearisation strategies for full RX / TX chains (with efficient use of DSP)
• DSP algorithms associated with RX signal detection & TX signal generation
• Cognitive Radio hardware architectures and algorithms
• SDR system architectures for future mmWave transceivers (fixed and mobile)
Researchers can expect to become involved in designing novel hardware and related DSP or control software for their proposed system(s) and then trialling and enhancing them for optimal operation in the Lab and in field trials. The project will be part of an ongoing funded activity in 5G & 6G next generation mmWave systems, also involving industry partners. The Communications Research Group is working on multiple areas of wireless communications technology including mmWave, Massive MIMO and IoT systems. The technology emerging from the researcher's chosen project will contribute to these research themes.
The position is available immediately and will be open until it is filled. Applicants should have relevant experience for the position.
For further information and informal enquiries contact Mr Eddie Ball at [Email Address Removed]
To formally apply for this post, please use the graduate application webpage: https://www.sheffield.ac.uk/postgraduate/research/apply