Mapping SFO mitigation/Linearization algorithms, trade-off between memory and computation on GPU
From ISLAB/CAISR
| Title | Mapping SFO mitigation/Linearization algorithms, trade-off between memory and computation on GPU |
|---|---|
| Summary | investigating the parallelisation and mapping of Sampling Frequency Offset algorithms |
| Keywords | |
| TimeFrame | Fall 2024 |
| References | |
| Prerequisites | |
| Author | |
| Supervisor | Hazem Ali (HH), Håkan Johansson (LiU) |
| Level | Master |
| Status | Open |
Mapping and mitigating sampling frequency offset (SFO) or linearizing frequency-related errors are critical in many digital communication systems. Sampling frequency offset occurs when there is a mismatch between the sampling frequencies of the transmitter and receiver, which can cause various issues like inter-symbol interference (ISI), phase distortion, and performance degradation. We investigate Mapping and parallelisation strategies for these algorithms to study the tradeoff between memory and computation on GPUs. This work is a part of the ELLIIT project "Baseband Processing for Beyond 5G Wireless".
