Leuven | More than two weeks ago
Compared with 5G, the coming 6G wireless communications will have a wider frequency band, higher transmission rate and spectrum efficiency, greater connection capacity, shorter delay, broader coverage, and more robust anti-interference capability to satisfy various network requirements. The millimeter-wave and sub-Terahertz bands are envisioned as candidate bands to provide large bandwidths supporting 10s to 100s Gbps data rate. Non-linearity in hardware components such as mixers and power amplifiers (PA) is a critical issue. To avoid nonlinear distortions, the PA is intentionally backed off to a linear region with lower transmission power. However, this significantly degrades the power efficiency and the communications range. Alternatively, pre-compensating the distortion would allow operation in a more power-efficient region of the PA. However, state-of-the-art pre-distortion algorithms can be very complex, which makes them inapplicable to systems with a very wide bandwidth: the PA distortion problem is much more acute at higher frequencies and wider bandwidths.
In this research, the student will study state-of-the-art PA non-linearity compensation at digital and system level, including digital pre-distortion (DPD) at the transmitter side and digital post correction (DPC) at the receiver side. If time allows and as an optional direction, he will study machine learning based DPC algorithms.
The student will implement the selected compensation algorithms in the IMEC 140GHz communications simulation chain. This will enable to assess and optimize the performances of the different algorithms. Benefits in back-off reduction, efficiency improvement, cost and complexity need to be considered.
The successful candidate will have the following skills:
knowledge of wireless communications or power amplifier (ideally both)
proficiency with Matlab
knowledge of machine learning is a plus
- Master Thesis internship (6 months)
- Preceded by optional summer internship (3 months)
Responsible scientist(s): Meng Li (Meng.firstname.lastname@example.org )
Type of project: Combination of internship and thesis
Duration: 6 months
Required degree: Master of Engineering Technology, Master of Engineering Science, Master of Science
Required background: Electrotechnics/Electrical Engineering
Supervising scientist(s): For further information or for application, please contact: Meng Li (Meng.Li@imec.be)