CS-EE SEMINAR:AttentionCodes: AI-aided iterative error correction codes

Guest: Emre Özfatura, Ericsson Research UK

Title:AttentionCodes: AI-aided iterative error correction codes

Date/Time: 13 March 2025, 13:40

Location: https://sabanciuniv.zoom.us/j/9113486000 

Abstract: Deep neural network (DNN)-assisted channel coding, such as low-complexity neural decoders for existing structured codes, or end-to-end neural-network-based auto-encoder designs have received significant interest in recent years due to their improved performance and flexibility. Such designs are particularly attractive for channels for which high-performing structured codes do not exist, such as the feedback channel or multi-user networks. In the scope of our research, we consider communication with an active feedback link, where the receiver can encode its feedback over the noisy feedback channel, which combines the challenges of feedback and multi-user problems. We propose novel encoding and decoding algorithms for both the transmitter and the receiver, built upon the transformer architecture, self-attention mechanism in particular. In the proposed solution, we combine the self-attention mechanism with an active feedback strategy in order to further improve the block error rate (BLER) performance and to reduce the amount of feedback required to make the overall design more attractive for practical implementations. We further propose a variable length coding strategy, which inspired from semi-supervised learning, and seek a better trade-off between the BLER performance and the communication budget, finally we have also show that such strategy is not limited to point-to-point communication scenario but can be generalized to multi-access scenario as well.

Bio: Received the B.Sc. degree in electronics engineering with mathematics minor and the M.Sc. degree in electronics engineering from Sabanci University, Turkey, in 2012 and 2015, respectively, and the Ph.D. degree from the Department of Electrical and Electronic Engineering, Imperial College London, U.K., in 2021. Following Ph.D., worked as Post-Doctoral Research Associate at the Information Processing and Communications (IPC) Laboratory, Imperial College London in various EU projects and Huawei collaboration. From 2024 working as researcher in Ericsson Research UK. His research interests include wireless AI, joint source-channel coding, semantic communication, AI-aided filter designs, federated learning, robust and secure learning, backdoor attacks, AI-based time series forecasting models.