PhD - Leuven | Just now
With the advent of mmWave 6G communications, it has become crucial to characterize devices and circuits under varying source and load impedances, at conditions that closely mimic these emerging applications. While still useful, it is no longer sufficient to perform such characterizations at single excitation frequencies or across a narrow bandwidth (typically <100 MHz). Instead, characterizations must be performed over realistic signal bandwidths, which can span multiple GHz in next generation mmWave communication systems.
Achieving a predefined impedance characteristic over a wideband frequency range cannot be realized using passive loadpull techniques and instead requires active or hybrid loadpull techniques. Some commercial solutions exist at microwave frequencies but currently do not extend into the mmWave band. The main limitation arises from the necessity of a frequency multiplier (FreqX) in the signal generation path to reach W-band (75–110 GHz) and beyond. However, the use of FreqX introduces significant nonlinear distortion, which disrupts the desired impedance synthesis.
This PhD will focus on developing a mmWave hybrid loadpull system capable of synthesizing predefined wideband impedances, both at the source and load ports, while compensating for nonlinear effects introduced by frequency multipliers. This involves pre-distorting the excitation signals such that, after distortion by the FreqX, the desired impedance profile is restored. A central challenge lies in the real-time coordination and synchronization of the predistortion on both the source and load sides, including continuous monitoring of the actual realized wideband impedances.
Required background: Master’s in electrical engineering, with basics in circuits/signals. Familiarity with Python/MATLAB is a plus. No RF experience needed, guidance will be provided. Strong interest in hands-on lab work and collaborative research is essential.
Type of work: 50% experimental / measurement system development, 30% signal processing & modeling (pre-distortion algorithms, FreqX modeling), 20% literature review and methodology benchmarking
Supervisor: Dominique Schreurs
Co-supervisor: Bertrand Parvais
Daily advisor: Rana ElKashlan
The reference code for this position is 2026-155. Mention this reference code on your application form.