High-throughput applications for optical links spurred significant interest in spatially coupled product-like codes. Staircase codes and the OFEC code are recommended for standardization and are widely studied in the literature [1-4].

The task of the student is to review and understand the oFEC code and its window decoding. After successful completion of the seminar, the student will have a comprehensive overview of code and decoder design for high-throughput optical communication systems.

[1] Y. Shen et al., "Iterative Logisitic Weight Based Chase Decoder for Open Forward Error Correction," Optical Fiber Communications Conference and Exhibition (OFC), San Francisco, USA, 2025, pp. 1-3.

(Online:  ieeexplore.ieee.org/document/11047193)

[2] W. Wang et al., "Real-Time FPGA Investigation of Potential FEC Schemes for 800G-ZR/ZR+ Forward Error Correction," in Journal of Lightwave Technology, vol. 41, no. 3, pp. 926-933, Feb. 2023.

(Online: ieeexplore.ieee.org/document/9935118 )

[3] A. Y. Sukmadji, U. Martinez-Penas, and F. R. Kschischang, "Zipper codes," in Journal of Lightwave Technology, vol. 40, no. 19, pp. 6397-6407, Oct. 2022.

(Online: arxiv.org/abs/2203.10120 )

[4] L. Rapp, S. Miao, and L. Schmalen, "Optimized Soft-Aided Decoding of OFEC and Staircase Codes," European Conference on Optical Communication (ECOC), Frankfurt, Germany, 2024, pp. 539-542.

(Online: ieeexplore.ieee.org/abstract/document/10926389 )

Voraussetzungen

This thesis investigates coding-theoretic methods to increase the robustness of binary neural networks (BNNs) implemented on noisy hardware platforms, such as memristor crossbars or radiation-prone memories used in space applications. The objective is to design, model, and evaluate decoding strategies and coding schemes ensuring reliable neural computation when both weights and activations are binary and hardware noise significantly affects performance. The work builds upon recent results [1] showing how channel coding principles can be applied to BNN layers.

[1] E. Dupraz and F. Leduc-Primeau, "Channel Coding for Binary Neural Networks Implemented with Noisy Memristor Crossbars," IEEE ISTC, 2025.

For detailed information, please refer to the attached PDF.

Voraussetzungen

In this research internship we apply SOCS [1] decoding to spatially coupled codes. 

[1] [2504.15204] Soft-Output from Covered Space Decoding of Product Codes

High-throughput applications for optical links spured great interest in low-complexity decoding of product codes. Several novel algorithms rely on passing hard messages and careful use of soft channel information to strike the balance between memory efficiency, computational complexity and error-correcting performance. New ideas for component decoding [2] [3] and attempts to go beyond pure iterative decoding [1][4] are of special interest.

The task of the student is to review and understand algorithms for decoding of product codes. After successful completion of the seminar the student will have a comprehensive overview of research in the field of high-throughput decoder design and analysis.

[1] C. Häger and H. D. Pfister, "Approaching Miscorrection-Free Performance of Product Codes With Anchor Decoding," in IEEE Transactions on Communications, vol. 66, no. 7, pp. 2797-2808, July 2018.

(Online: ieeexplore.ieee.org/abstract/document/8316914 )

[2] A. Sheikh, A. Graell i Amat, G. Liva and A. Alvarado, "Refined Reliability Combining for Binary Message Passing Decoding of Product Codes," J. Lightwave Technol., vol. 39, no. 15, pp. 4958-4973, August 2021.

(Online: arxiv.org/pdf/2006.00070 )

[3] A. Sheikh, A. Graell i Amat and A. Alvarado, "Novel High-Throughput Decoding Algorithms for Product and Staircase Codes based on Error-and-Erasure Decoding," J. Lightwave Technol., vol. 39, no. 15, pp. 4909-4922, August 2021.

(Online: arxiv.org/pdf/2008.02181 )

[4] S. Miao, L. Rapp and L. Schmalen, "Improved Soft-Aided Decoding of Product Codes With Dynamic Reliability Scores," J. Lightwave Technol., vol. 40, no. 22, pp. 7279-7288, November 2022.

(Online: arxiv.org/pdf/2204.00466 )

Voraussetzungen