AI-Driven Power Amplifier Linearization on FPGA for 6G and Industrial Wireless

The next generation of wireless communication systems, including 6G networks and Industrial Wireless technologies, will require highly efficient, intelligent, and energy-aware radio frequency (RF) hardware capable of supporting ultra-high data rates, massive connectivity, and low-latency communication. Power amplifiers (PAs) are among the most critical and power-hungry components in wireless transmitters. However, their nonlinear behaviour can introduce signal distortion, reduce spectral efficiency, and increase energy consumption.

This PhD project aims to investigate Artificial Intelligence (AI)-driven techniques for real-time power amplifier linearization using Field Programmable Gate Array (FPGA) platforms. The research will focus on the development of intelligent Digital Predistortion (DPD) algorithms capable of compensating for nonlinear PA characteristics under dynamic operating conditions. Advanced machine learning and neural network approaches will be explored to improve linearization performance while reducing computational complexity and power consumption.

The project will involve the modelling and characterization of RF power amplifiers, development of AI-based linearization algorithms, FPGA implementation and optimisation, and performance evaluation using modern wireless communication signals relevant to 5G, 6G, and industrial wireless applications. The research may also investigate adaptive and self-learning architectures capable of operating in changing environments and supporting future cognitive radio systems.

This project aligns with global research priorities in next-generation communications, energy-efficient electronics, intelligent RF systems, and industrial digitalisation. The outcomes are expected to contribute to the development of greener wireless networks and advanced industrial communication infrastructures.

The successful candidate will gain expertise in RF engineering, Artificial Intelligence, FPGA design, digital signal processing, wireless communications, and embedded systems. The project is expected to generate multiple high-quality journal and conference publications and provide opportunities for collaboration with academic and industrial research partners.

Applicants should hold a minimum of a Bachelor's degree or a Master's degree in Electronic Engineering, Electrical Engineering, Telecommunications Engineering, Computer Engineering, Embedded Systems, Mechatronics, Physics, or a closely related discipline.

The ideal candidate will have an interest in one or more of the following areas:

Wireless Communication Systems
RF and Microwave Engineering
5G and 6G Technologies
Digital Signal Processing (DSP)
Artificial Intelligence and Machine Learning
FPGA and Hardware Acceleration
Embedded Systems Design
Software Defined Radio (SDR)
Power Amplifier Modelling and Linearization

Experience in one or more of the following areas would be advantageous but is not essential:

MATLAB, Python, C/C++, or VHDL/Verilog programming
FPGA development platforms (e.g., Xilinx, Intel/Altera)
Digital Predistortion (DPD) techniques
RF measurement and testing
Wireless communication system simulation
Machine learning frameworks such as TensorFlow or PyTorch
Signal processing and communication theory

The successful candidate should demonstrate:

Strong mathematical, analytical, and problem-solving skills.
A solid foundation in engineering principles and quantitative analysis.
The ability to work independently and as part of a multidisciplinary research team.
Good written and verbal communication skills in English.
A strong motivation to conduct innovative research and publish in leading international journals and conferences.

This project is particularly suitable for candidates seeking careers in wireless communications, RF and microwave engineering, semiconductor technologies, FPGA and embedded systems design, Artificial Intelligence for communications, and next-generation 6G network development. The interdisciplinary nature of the project provides an excellent opportunity to develop expertise at the intersection of AI, digital signal processing, hardware acceleration, and advanced wireless technologies.

Candidates with backgrounds in either Engineering, Computing, Physics, or Applied Mathematics are encouraged to apply.

Self Funded (Scholarship not available. Fees & Materials to be paid by the student.)

If you are interested in submitting an application for this project, please complete an Expression of Interest.

 https://forms.office.com/e/0hCcrv2Gkp