Efficient Decoding of Tailbiting Convolutional Codes
Description
The aim of this thesis is to study decoding of tailbiting convolutional codes, a class of codes suited for short blocklengths. We address their efficient decoding as well as applications to communication systems with feedback.
The aim of this thesis is to enhance decoding algorithms for precoded polar product codes. We make use of successive cancellation list (SCL) decoding to generate reliability information and scale it based on the maximization of generalized mutual information.
Supervisor:
Andreas Straßhofer
Publications
2024
Straßhofer, A.: Dispersion of the Rayleigh Fading Channel with CSIR and CSIT. Munich Workshop on Shannon Coding Techniques (MSCT), 2024 more…
Straßhofer, A.: Dispersion of the Rayleigh Fading Channel with CSIR and CSIT. Munich Workshop on Coding and Cryptography (MWCC), 2024 more…
Straßhofer, A.: Achievable Rates for Channels with Feedback. European School of Information Theory (ESIT), 2024 more…
Straßhofer, A.; Kramer, G.: Feedback Coding for Wireless Communication. Workshop on Algorithms for Short Transmission Reach Optics (ASTRO), 2024 more…
2023
Straßhofer, A.; Lentner, D.; Liva, G.; Graell i Amat, A.: Soft-Information Post-Processing for Chase-Pyndiah Decoding Based on Generalized Mutual Information. International Symposium on Topics in Coding, 2023 more…
Straßhofer, Andreas: Density Evolution of Chase-Pyndiah Decoding. 20th Joint Workshop on Communications and Coding, 2023 more…
