Compensation of PA Distortion for 6G Communication Systems

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. The nonlinearity generates spectral re-growth, which leads to adjacent channel interference and violations of the out-of-band emissions standards mandated by regulatory bodies. It also causes in-band distortion, which degrades the bit-error rate (BER) and data throughput of the communication system. 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.  Digital pre-distortion (DPD) is the technique to increase linearity or compensate for non-linearity in power amplifiers. However, as the bandwidth increases, more complex DPD models and algorithms are required and must operate at higher data rate, which significantly increases the power consumption of the DPD system.  The need of low-cost and good performance DPD algorithms in 5G system is ever increasing.

In this research, the student will study state-of-the-art digital pre-distortion (DPD) algorithms. The student will optimize the DPD algorithms to decrease the implementation complexity in IMEC’s ultra-high-speed communications simulation chain. The benefits of DPD in back-off reduction, efficiency improvement, cost and complexity need to be considered.  With the knowledge of performance and complexity of DPD algorithms, the student will further explore DPD schemes for MIMO system in 5G.  

The successful candidate will have the following skills:

- knowledge of wireless communications and signal processing

- proficiency with Matlab

- knowledge of machine learning is a plus

Type:

- Master Thesis internship (6 months)

- Preceded by optional summer internship (3 months)