The central goal of this PhD thesis is to investigate thoroughly how Machine Learning (ML) can help improving current knowledge and practice in the design and the decoding of short forward-error correction (FEC) codes. The focus will be placed on LDPC codes, a family of powerful error-correcting codes that has found widespread use in storage and wireless communication systems, e.g. in Wi-Fi or in 5G. Three key and inter-related problems will be addressed:
- How to design short LDPC codes that perform well under iterative message-passing decoding
- How to decode short LDPC codes close to their best possible performance (maximum-likelihood decoding or near)
- How to design robust LDPC decoders that automatically adapt to unknow channel parameters or mismatches in the assumed channel model, from the received data.
Not only are these three problems regarded as ideal playgrounds for assessing the potential benefits of ML for channel coding, but any advances on these questions is also expected to find quick outcomes in emerging communication systems. In addressing these three problems, the PhD candidate will be exposed to a variety of learning approaches, including, but not limited to, Supervised Learning (SL), Reinforcement Learning (RL), and Meta-Learning.
Expected start date: September 2022, or sooner
Please consult the following document for further information about the research project and PhD application procedure