### Prof. Dr.-Ing. Antonia Wachter-Zeh

Technische Universität München

Professur für Coding for Communications and Data Storage (Prof. Wachter-Zeh)

###### Postadresse

Postal:

Theresienstr. 90

80333 München

- Tel.: +49 (89) 289 - 23495
- Raum: 0104.03.418
- antonia.wachter-zeh@tum.de

## Biografie

Since October 2016, I am a Rudolf Mößbauer Tenure Track Assistant Professor at the Technical University of Munich (TUM) and a Fellow at the TUM Institute for Advanced Study (TUM IAS). I am heading the "Coding for Communications and Data Storage" (COD) group.

From November 2013 to August 2016, I was a Postdoctoral Researcher at the Computer Science Department of the Technion---Israel Institute of Technology in the group of Tuvi Etzion, Ronny Roth, and Eitan Yaakobi.

From October 2009 to October 2013, I was a PhD student and teaching assistant under the co-supervision of Martin Bossert at the Institute of Communications Engineering, Ulm University, Germany, and Pierre Loidreau at the Institut de Recherche Mathématique de Rennes (IRMAR), Université de Rennes 1, Rennes, France. You can download my PhD thesis "Decoding of Block and Convolutional Codes in Rank Metric" here.

My main research interests are coding theory, efficient algorithms, security (in particular code-based cryptography and physical unclonable functions), and applying error-correcting codes to communications and storage in general, in particular to network coding, non-volatile memories, distributed data storage, and DNA storage.

#### Selected Grants and Awards

- ERC Starting Grant 2018 (ERC Press Release | TUM Press Release)
- DFG Heinz Maier-Leibnitz-Preis 2018, (DFG Press Release | TUM Press Release)
- DFG Emmy Noether Program (2016)
- Marie Skłodowska-Curie Individual Fellowship, Horizon 2020, European Commission (2015--2016)
- Minerva Postdoctoral Fellowship (2013--2015)
- Dissertation Award of "Ulmer Universitätsgesellschaft" (2013)
- 2nd Prix de Thèse, Édition 2013, Fondation Rennes 1 (2013)

#### (Co-)Organized Workshops

## Lehre

#### Lectures

- Channel Coding, Winters 2016/17, 2017/18
- Coding Theory for Storage and Networks, Summers 2017, 2018, 2019
- Security in Communications and Storage, Winter 2017/18

#### Supervision of PhD Students

- Lorenz Welter, since 03/2019
- Hedongliang Liu, since 03/2019
- Georg Maringer, Post-quantum cryptography, since 12/2018
- Haider Al Kim, Coding for storage, since 10/2018
- Julian Renner, Code-based cryptography, since 11/2017
- Lukas Holzbaur, Coding for distributed data storage & Private information retrieval, since 03/2017
- Andreas Lenz, Insertions/deletions & coding for DNA storage, since 12/2016

## Forschung

#### Code-based Cryptography

The realistic threat of a quantum supercomputer has motivated research on post-quantum cryptography. Assuming an attack of a sufficiently large quantum computer, several classical public-key algorithms as RSA become insecure since computationally intensive mathematical problems become easy-to-solve. It is therefore necessary to design techniques which are secure against an attack of a quantum computer. One approach to achieve this security is code-based cryptography, where encryption and decryption is based on encoding and decoding an algebraic code. The COD group investigates and improves post-quantum cryptographic systems.

- Interleaving Loidreau's Rank-Metric Cryptosystem
- A Cryptosystem based on Interleaved Goppa Codes
- A Rank-Metric Based Cryptosystem of Small Key Size
- Twisted Gabidulin Codes in the McEliece System

#### Network Coding and Rank-Metric Codes

The principle of network coding has been attracting growing attention in the recent years as a technique to disseminate information in data networks. The reason for this interest is that network coding achieves higher throughput than routing. In routing, the packets are simply forwarded at each node of the network, whereas in network coding, the nodes perform linear combinations of all packets received so far. Fundamental questions in the area of network coding include assigning appropriate linear combinations to the nodes and choosing a suitable error-correcting code for the case when erroneous packets occur or packets are lost.

- Vector Network Coding Outperforms Scalar Linear Network Coding
- Optimal Ferrers Diagram Rank-Metric Codes
- Convolutional Codes in Rank Metric with Application to Random Network Coding
- Decoding High-Order Interleaved Rank-Metric Codes

#### List Decoding

List decoding is a major technique for increasing the error correcting capability of codes. The basic idea of list decoding is to return not only a unique decoding result (which is only possible up to half the minimum distance of the corresponding code), but to return all codewords in a certain radius around the received word. This enables us to decode more errors at the cost of a usually small list. Practically, list decoding is used in concatenated coding schemes. Apart from explicit list decoding algorithms, a fundamental question is whether list decoding in a certain metric up to a certain radius can be done in polynomial time.

- List Decoding of Locally Repairable Codes
- List Decoding of Insertions and Deletions
- List Decoding of Crisscross Errors
- Some Gabidulin Codes Cannot be List Decoded Efficiently at any Radius
- Bounds on List Decoding of Rank-Metric Codes
- Interpolation-Based Decoding of Interleaved Gabidulin Codes
- Efficient Interpolation-Based Decoding of Interleaved Subspace Codes

#### Coding for Insertions/Deletions and DNA Storage

The Levenshtein distance, also known as the edit distance, is a measure of similarity between two strings evaluated based on the minimum number of insertion/deletion operations required to transform one string into the other. Codes for correcting insertions and deletions were first proposed for communication applications in the presence of synchronization errors. However, insertions, deletions, and duplications have particular importance also for numerous applications in bioinformatics such as DNA storage.

- Coding over Sets for DNA Storage
- Clustering-Correcting Codes
- Duplication-Correcting Codes
- List Decoding of Insertions and Deletions
- Codes Correcting a Burst of Deletions or Insertions

#### Coding for Storage

Data storage media like flash memories (used in USB flash drives or solid state drives) suffer from manufacturing imperfections, wearout, and fluctuating read/write errors. In distributed data storage (necessary for cloud storage systems), the most common problem are failures of servers and the task is to reconstruct the lost data efficiently. It is therefore necessary to design coding solutions tailored to these storage applications.

- List Decoding of Locally Repairable Codes
- Codes for Partially Stuck-at Memory Cells
- Masking Trapped Charge in Flash Memories
- Erasure List Decoding of Locally Repairable Codes

#### Private Information Retrieval

Private information retrieval (PIR) studies the problem when a user wants to retrieve a file from a storage system without revealing the identity of the file in question to the storage servers.

#### Fast Algorithms

Fast or efficient algorithms for performing certain polynomial and matrix operations are important in several applications, including coding theory.

- Fast Operations on Linearized Polynomials
- Sub-quadratic Decoding of Gabidulin Codes
- A Fast Linearized Euclidean Algorithm
- Fast Multi-Sequence Shift-Register Synthesis with the Euclidean Algorithm

#### Physical Unclonable Functions

A Physical Unclonable Function (PUF) is a digital circuit that possesses an intrinsic randomness resulting from process variations. This randomness is exploited to generate random keys for cryptographic applications which can be reproduced on demand. Thus, no embedded physically secure non-volatile memory is needed. However, the regeneration of a key is not perfect due to environmental factors such as temperature variations and aging effects of the digital circuit. These variations can be seen as an erroneous channel and therefore error-correcting codes increase the reliability of key regenerations.

- Timing Attack Resilient Decoding Algorithms for Physical Unclonable Functions
- Variable-Length Bit Mapping and Error-Correcting Codes for Higher-Order Alphabet PUFs

#### Coding for Communications

In communications, error-correcting codes are an indispensable means to cope with errors which happen during the transmission.

#### Reed-Solomon and Cyclic Codes

- Decoding Interleaved Reed-Solomon Codes
- Unambiguous Decoding of Reed-Solomon Codes beyond Half the Minimum Distance
- Generalizing Bounds on the Minimum Distance of Cyclic Codes
- Decoding Cyclic Codes up to a New Bound on the Minimum Distance

#### Convolutional Codes

### Publications

#### Journal Publications

[23] A. Lenz, P. Siegel, A. Wachter-Zeh, and E. Yaakobi, “Coding over Sets for DNA Storage,” (in revision for) IEEE Trans. Inform. Theory, 2018.

[22] R. Tajeddine, A. Wachter-Zeh, and C. Hollanti, “Private Information Retrieval over Networks,” (in revision for) IEEE Trans. on Information Forensics and Security, 2018.

[21] L. Holzbaur, R. Freij-Hollanti, A. Wachter-Zeh, and C. Hollanti, “Private Streaming with Convolutional Codes,” (in revision for) IEEE Trans. Inform. Theory, 2018.

[20] V. Immler, M. Hiller, L. Qinzhi, A. Lenz, and A. Wachter-Zeh, “Variable-Length Bit Mapping and Error- Correcting Codes for Higher-Order Alphabet PUFs,” Journal of Hardware and Systems Security, vol. 3, pp. 78–93, Mar. 2019.

[19] L. Holzbaur, H. Bartz, and A. Wachter-Zeh, “Improved Decoding and Error Floor Analysis of Staircase Codes,” Designs, Codes and Cryptography, vol. 2-3, Jan. 2019.

[18] A. Lenz, A. Wachter-Zeh, and E. Yaakobi, “Duplication-Correcting Codes,” Designs, Codes and Cryptogra- phy, vol. 2-3, Jan. 2019.

[17] H. Bartz and A. Wachter-Zeh, “Efficient List Decoding of Interleaved Subspace and Gabidulin Codes Using Gröbner Bases,” Advances in Mathematics of Communications, vol. 12, no. 4, pp. 773–804, Nov. 2018.

[16] S. Puchinger and A. Wachter-Zeh, “Fast Operations on Linearized Polynomials and their Applications in Coding Theory,” Journal of Symbolic Computation, vol. 89, pp. 194–215, Nov. 2018.

[15] A. Wachter-Zeh, “List Decoding of Insertions and Deletions,” IEEE Trans. Inform. Theory, vol. 64, no. 9, pp. 6297–6304, Sep. 2018.

[14] T. Etzion and A. Wachter-Zeh, “Vector Network Coding based on Subspace Codes Outperforms Scalar Linear Network Coding,” IEEE Trans. Inform. Theory, vol. 64, no. 4, pp. 2460–2473, Apr. 2018.

[13] C. Schoeny, A. Wachter-Zeh, R. Gabrys, and E. Yaakobi, “Codes Correcting a Burst of Deletions or Insertions,” IEEE Trans. Inform. Theory, vol. 63, no. 4, pp. 1971–1985, Apr. 2017.

[12] A. Wachter-Zeh, “List Decoding of Crisscross Errors,” IEEE Trans. Inform. Theory, vol. 63, no. 1, pp. 142–149, Jan. 2017.

[11] N. Raviv and A. Wachter-Zeh, “Some Gabidulin Codes Cannot be List Decoded Efficiently at any Radius,” IEEE Trans. Inform. Theory, vol. 62, no. 4, pp. 1605–1615, Apr. 2016.

[10] T. Etzion, E. Gorla, A. Ravagnani, and A. Wachter-Zeh, “Optimal Ferrers Diagram Rank-Metric Codes,” IEEE Trans. Inform. Theory, vol. 62, no. 4, pp. 1616–1630, Apr. 2016. 1/6

[9] A. Wachter-Zeh and E. Yaakobi, “Codes for Partially Stuck-at Memory Cells,” IEEE Trans. Inform. Theory, vol. 62, no. 2, pp. 639–654, Feb. 2016.

[8] A. Wachter-Zeh, M. Stinner, and V. Sidorenko, “Convolutional Codes in Rank Metric with Application to Random Network Coding,” IEEE Trans. Inform. Theory, vol. 61, no. 6, pp. 3199–3213, Jun. 2015.

[7] A. Wachter-Zeh and A. Zeh, “List and Unique Error-Erasure Decoding of Interleaved Gabidulin Codes with Interpolation Techniques,” Des. Codes Cryptogr., vol. 73, no. 2, pp. 547–570, 2014.

[6] A. Wachter-Zeh, A. Zeh, and M. Bossert, “Decoding Interleaved Reed–Solomon Codes beyond their Joint Error-Correcting Capability,” Des. Codes Cryptogr., vol. 71, no. 2, pp. 261–281, Jul. 2014.

[5] A. Wachter-Zeh, “Bounds on List Decoding of Rank-Metric Codes,” IEEE Trans. Inform. Theory, vol. 59, no. 11, pp. 7268–7277, Nov. 2013.

[4] A. Wachter-Zeh, V. Afanassiev, and V. Sidorenko, “Fast Decoding of Gabidulin Codes,” Des. Codes Cryptogr., vol. 66, no. 1, pp. 57–73, Jan. 2013.

[3] A. Zeh, A. Wachter-Zeh, and S. Bezzateev, “Decoding Cyclic Codes up to a New Bound on the Minimum Distance,” IEEE Trans. Inform. Theory, vol. 58, no. 6, pp. 3951–3960, Jun. 2012.

[2] A. Zeh and A. Wachter, “Fast Multi-Sequence Shift-Register Synthesis with the Euclidean Algorithm,” Adv. Math. Commun., vol. 5, no. 4, pp. 667–680, Nov. 2011.

[1] A. Wachter, V. Sidorenko, M. Bossert, and V. Zyablov, “On (Partial) Unit Memory Codes Based on Gabidulin Codes,” Probl. Inf. Transm., vol. 47, no. 2, pp. 38–51, Jun. 2011.

#### Conference Publications

[44] L. Holzbaur, S. Puchinger, and A. Wachter-Zeh, “On Error Decoding of Locally Repairable and Partial MDS Codes,” in IEEE Information Theory Workshop (ITW), Aug. 2019, Visby, Sweden.

[43] A. Lenz, P. Siegel, A. Wachter-Zeh, and E. Yaakobi, “An Upper Bound on the Capacity of the DNA Storage Channel,” in IEEE Information Theory Workshop (ITW), Aug. 2019, Visby, Sweden.

[42] T. Shinkar, E. Yaakobi, A. Lenz, and A. Wachter-Zeh, “Clustering-Correcting Codes,” in IEEE Int. Symp. Inf. Theory (ISIT), Jul. 2019, Paris, France.

[41] A. Lenz, P. Siegel, A. Wachter-Zeh, and E. Yaakobi, “Anchor-Based Correction of Substitutions in Indexed Sets,” in IEEE Int. Symp. Inf. Theory (ISIT), Jul. 2019, Paris, France.

[40] L. Holzbaur, H. Liu, S. Puchinger, and A. Wachter-Zeh, “On Decoding and Applications of Interleaved Goppa Codes,” in IEEE Int. Symp. Inf. Theory (ISIT), Jul. 2019, Paris, France.

[39] H. Cai, T. Etzion, M. Schwartz, and A. Wachter-Zeh, “Network Coding Solutions for the Combination Network and its Subgraphs,” in IEEE Int. Symp. Inf. Theory (ISIT), Jul. 2019, Paris, France.

[38] A. Lenz, P. Siegel, A. Wachter-Zeh, and E. Yaakobi, “Coding over Sets for DNA Storage,” in Non-Volantile Memories Workshop (NVMW), Feb. 2019, San Diego, USA (**NVMW Memorable Paper Award**).

[37] L. Holzbaur, R. Freij-Hollanti, A. Wachter-Zeh, and C. Hollanti, “Private Streaming with Convolutional Codes,” in IEEE Information Theory Workshop (ITW), Nov. 2018, Guangzhou, China.

[36] S. Puchinger, J. Renner, and A. Wachter-Zeh, “Twisted Gabidulin Codes in the GPT Cryptosystem,” in Sixteenth International Workshop on Algebraic and Combinatorial Coding Theory (ACCT), Sep. 2018, Svetlogorsk, Russia. 2/6

[35] A. Lenz, N. Jünger, and A. Wachter-Zeh, “Bounds and Constructions for Multi-Symbol Duplication Error Correcting Codes,” in Sixteenth International Workshop on Algebraic and Combinatorial Coding Theory (ACCT), Sep. 2018, Svetlogorsk, Russia.

[34] H. Liu, L. Holzbaur, and A. Wachter-Zeh, “Locality in Crisscross Error Correction,” in Sixteenth In- ternational Workshop on Algebraic and Combinatorial Coding Theory (ACCT), Sep. 2018, Svetlogorsk, Russia.

[33] A. Wachter-Zeh, S. Puchinger, and J. Renner, “Repairing the Faure-Loidreau Public-Key Cryptosystem,” in IEEE Int. Symp. Inf. Theory (ISIT), Jun. 2018, Vail, Colorado, USA.

[32] L. Holzbaur and A. Wachter-Zeh, “List Decoding of Locally Repairable Codes,” in IEEE Int. Symp. Inf. Theory (ISIT), Jun. 2018, Vail, Colorado, USA.

[31] A. Lenz, P. Siegel, A. Wachter-Zeh, and E. Yaakobi, “Coding over Sets for DNA Storage,” in IEEE Int. Symp. Inf. Theory (ISIT), Jun. 2018, Vail, Colorado, USA.

[30] V. Immler, M. Hiller, L. Qinzhi, A. Lenz, and A. Wachter-Zeh, “Variable-Length Bit Mapping and Error- Correcting Codes for Higher-Order Alphabet PUFs,” in Seventh International Conference on Security, Privacy, and Applied Cryptography Engineering (SPACE), Dec. 2017, Goa, India.

[29] L. Holzbaur, H. Bartz, and A. Wachter-Zeh, “Improved Decoding and Error Floor Analysis of Staircase Codes,” in International Workshop on Coding and Cryptography (WCC), Sep. 2017, St. Petersburg, Russia.

[28] A. Lenz, A. Wachter-Zeh, and E. Yaakobi, “Bounds on Codes Correcting Tandem and Palindromic Duplications,” in International Workshop on Coding and Cryptography (WCC), Sep. 2017, St. Petersburg, Russia.

[27] A. Wachter-Zeh, “Limits to List Decoding of Insertions and Deletions,” in IEEE Int. Symp. Inf. Theory (ISIT), Jun. 2017, Aachen, Germany.

[26] V. Sidorenko, H. Bartz, and A. Wachter-Zeh, “Interleaved Subspace Codes in Fountain Mode,” in IEEE Int. Symp. Inf. Theory (ISIT), Jun. 2017, Aachen, Germany.

[25] S. Puchinger, S. Müelich, M. Bossert, and A. Wachter-Zeh, “Timing Attack Resilient Decoding Algorithms for Physical Unclonable Functions,” in International ITG Conference on Systems, Communications and Coding (SCC), Feb. 2017, Hamburg, Germany.

[24] T. Etzion and A. Wachter-Zeh, “Vector Network Coding based on Subspace Codes Outperforms Scalar Linear Network Coding,” in IEEE Int. Symp. Inf. Theory (ISIT), Jul. 2016, Barcelona, Spain.

[23] S. Puchinger and A. Wachter-Zeh, “Sub-quadratic Decoding of Gabidulin Codes,” in IEEE Int. Symp. Inf. Theory (ISIT), Jul. 2016, Barcelona, Spain.

[22] C. Schoeny, A. Wachter-Zeh, R. Gabrys, and E. Yaakobi, “Codes Correcting a Burst of Deletions or Insertions,” in IEEE Int. Symp. Inf. Theory (ISIT), Jul. 2016, Barcelona, Spain.

[21] A. Zeh and A. Wachter-Zeh, “Improved Erasure List Decoding Locally Repairable Codes using Alphabet- Dependent List Recovery,” in IEEE Int. Symp. Inf. Theory (ISIT), Jul. 2016, Barcelona, Spain.

[20] A. Wachter-Zeh and E. Yaakobi, “Masking Trapped Charge in Flash Memories,” in 53nd Annual Allerton Conference on Communication, Control, and Computing, Oct. 2015, Monticello, IL, USA.

[19] N. Raviv and A. Wachter-Zeh, “Some Gabidulin Codes Cannot be List Decoded Efficiently at any Radius,” in IEEE Int. Symp. Inf. Theory (ISIT), Jun. 2015, Hong Kong, China. 3/6

[18] A. Wachter-Zeh and E. Yaakobi, “Codes for Partially Stuck-at Memory Cells,” in Int. ITG Conf. on Systems, Communications and Coding (SCC), Feb. 2015, Hamburg, Germany.

[17] H. Bartz and A. Wachter-Zeh, “Efficient Interpolation-Based Decoding of Interleaved Subspace and Gabidulin Codes,” in 52nd Annual Allerton Conference on Communication, Control, and Computing, Oct. 2014, Monticello, IL, USA.

[16] A. Wachter-Zeh, “List Decoding of Crisscross Error Patterns,” in IEEE Int. Symp. Inf. Theory (ISIT), Jul. 2014, Honolulu, HI, USA.

[15] S. Puchinger, A. Wachter-Zeh, and M. Bossert, “Improved Decoding of Partial Unit Memory Codes Using List Decoding of Reed–Solomon Codes,” in Int. Zurich Sem. Comm. (IZS), Feb. 2014, Zurich, Switzerland.

[14] A. Wachter-Zeh, “Bounds on Polynomial-Time List Decoding of Rank Metric Codes,” in IEEE Int. Symp. Inf. Theory (ISIT), Jul. 2013, Istanbul, Turkey.

[13] A. Zeh, A. Wachter-Zeh, M. Gadouleau, and S. Bezzateev, “Generalizing Bounds on the Minimum Distance of Cyclic Codes Using Cyclic Product Codes,” in IEEE Int. Symp. Inf. Theory (ISIT), Jul. 2013, Istanbul, Turkey.

[12] A. Wachter-Zeh and A. Zeh, “Interpolation-Based Decoding of Interleaved Gabidulin Codes,” in Int. Workshop Coding Cryptogr. (WCC), Apr. 2013, Bergen, Norway.

[11] V. Sidorenko, A. Wachter-Zeh, and D. Chen, “On fast Decoding of Interleaved Gabidulin Codes,” in Int. Symp. Probl. Redundancy Inf. Control Systems, Sep. 2012, pp. 78–83, St. Petersburg, Russia.

[10] A. Wachter-Zeh, M. Stinner, and M. Bossert, “Efficient Decoding of Partial Unit Memory Codes of Arbitrary Rate,” in IEEE Int. Symp. Inf. Theory (ISIT), Jul. 2012, pp. 2356–2360, Boston, MA, USA.

[9] A. Wachter-Zeh and V. Sidorenko, “Rank Metric Convolutional Codes for Random Linear Network Coding,” in IEEE Int. Symp. Network Coding (Netcod), Jul. 2012, Boston, MA, USA.

[8] A. Wachter-Zeh, “Bounds on List Decoding Gabidulin Codes,” in Int. Workshop Alg. Combin. Coding Theory (ACCT), Jun. 2012, pp. 329–334, Pomorie, Bulgaria.

[7] A. Zeh, A. Wachter-Zeh, and M. Bossert, “Unambiguous Decoding of Generalized Reed-Solomon Codes beyond Half the Minimum Distance,” in Int. Zurich Sem. Comm. (IZS), Feb. 2012, Zurich, Switzerland.

[6] A. Wachter, V. Sidorenko, M. Bossert, and V. Zyablov, “Partial Unit Memory Codes Based on Gabidulin Codes,” in IEEE Int. Symp. Inf. Theory (ISIT), Aug. 2011, pp. 2487–2491, St. Petersburg, Russia.

[5] A. Zeh, A. Wachter, and S. Bezzateev, “Efficient Decoding of Some Classes of Binary Cyclic Codes beyond the Hartmann–Tzeng Bound,” in IEEE Int. Symp. Inf. Theory (ISIT), Aug. 2011, pp. 1017–1021, St. Petersburg, Russia.

[4] A. Wachter, V. Afanassiev, and V. Sidorenko, “Fast Decoding of Gabidulin Codes,” in Int. Workshop Coding Cryptogr. (WCC), Apr. 2011, pp. 433–442, Paris, France.

[3] A. Wachter, V. Sidorenko, and M. Bossert, “A Fast Linearized Euclidean Algorithm for Decoding Gabidulin Codes,” in Int. Workshop Alg. Combin. Coding Theory (ACCT), Sep. 2010, pp. 298–303, Novosibirsk, Russia.

[2] A. Wachter, V. Sidorenko, and M. Bossert, “A Basis for all Solutions of the Key Equation for Gabidulin Codes,” in IEEE Int. Symp. Inf. Theory (ISIT), Jun. 2010, pp. 1143–1147, Austin, TX, USA.

[1] S. Kampf, A. Wachter, and M. Bossert, “A Method for Soft-Decision Decoding of Reed-Solomon Codes Based on the Extended Euclidean Algorithm,” in ITG Conf. Source Channel Coding (SCC), Jan. 2010, pp. 1–6, Siegen, Germany.

#### Theses

[3] A. Wachter-Zeh, “Decoding of Block and Convolutional Codes in Rank Metric,” Ph.D. dissertation, Ulm University and University of Rennes 1, Ulm, Germany and Rennes, France, Oct. 2013.

[2] A. Wachter, “Soft-Decision Decoding of Reed–Solomon Codes Beyond Half the Minimum Distance with the Euclidean Algorithm,” Master’s thesis, Ulm University, Ulm, Germany, Oct. 2009.

[1] A. Wachter, “Signal Processing Algorithms for Air-Based Radar Systems,” Bachelor’s thesis, DHBW Ravensburg, Sep. 2007.

#### Book Chapter

[1] M. Bossert, V. Sidorenko, and A. Wachter-Zeh, “Coding Techniques for Transmitting Packets Through Complex Communication Networks,” in Communications in Interference Limited Networks, ser. Signals and Communication Technology, W. Utschick, Ed. Springer International Publishing, 2016, pp. 1–29.