The CODES research team designs and develops channel and source coding solutions for emerging digital communication and storage systems. The primary challenge and driver at the heart of our research work is to propose codes and algorithms operating close to the fundamental theoretical limits in error-correction and source coding and simultaneously adressing the need of practical applications either in terms of systems specifications (e.g. short packets, distributed network architectures, uncoordinated communications and network access, etc) or hardware constraints (energy consumption, ultra-high-rate transmission, non-faulty computations, etc).
The research investigations are at the crossroads of signal processing, discrete mathematics, information theory, circuits and systems, and more recently machine learning.
Example of applications include next-generation wireless networks (5G and beyond) and access as well as long-haul fiber-optical networks, but also emerging technologies and applications such as DNA storage systems or free-space space-earth satellite communications.
Examples of recent research topics
- Towards Tb/s turbo-decoding (H2020 project EPIC)
- Non-binary LDPC code design (Lab-STICC project home)
- Non-binary modulation and coding for sensors networks and IoT (ANR project QCSP)
- Coding for DNA storage (CominLabs project dnarXiv)
- Coding for fault-tolerant systems (ANR project EF-FECtive)
- Distributed source coding (CominLabs project InterCom)
- Learning on compressed data (CominLabs chair IoTAD-CEO)
- Joint source-channel coding to improve learning and reconstruction of coded date (CominLabs projet CoLearn)
- AI-aided code design and decoding (ANR project AI4CODE)
High-data rate channel coding, energy-efficient channel coding, non-binary coding, coded modulation, low-latency coding, coding for fiber-optical transmission, low-complexity decoding algorithms, distributed source coding, coding for DNA storage, NOMA communications