| 1. |
- Lenz, Andreas, et al.
(författare)
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Concatenated Codes for Recovery From Multiple Reads of DNA Sequences
- 2021
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Ingår i: 2020 IEEE Information Theory Workshop, ITW 2020.
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Konferensbidrag (refereegranskat)abstract
- Decoding sequences that stem from multiple transmissions of a codeword over an insertion, deletion, and substitution channel is a critical component of efficient deoxyribonucleic acid (DNA) data storage systems. In this paper, we consider a concatenated coding scheme with an outer low-density parity-check code and either an inner convolutional code or a block code. We propose two new decoding algorithms for inference from multiple received sequences, both combining the inner code and channel to a joint hidden Markov model to infer symbolwise a posteriori probabilities (APPs). The first decoder computes the exact APPs by jointly decoding the received sequences, whereas the second decoder approximates the APPs by combining the results of separately decoded received sequences. Using the proposed algorithms, we evaluate the performance of decoding multiple received sequences by means of achievable information rates and Monte-Carlo simulations. We show significant performance gains compared to a single received sequence.
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| 2. |
- Maarouf, Issam, et al.
(författare)
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Concatenated Codes for Multiple Reads of a DNA Sequence
- 2023
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Ingår i: IEEE Transactions on Information Theory. - 0018-9448 .- 1557-9654. ; 69:2, s. 910-927
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Tidskriftsartikel (refereegranskat)abstract
- Decoding sequences that stem from multiple transmissions of a codeword over an insertion, deletion, and substitution channel is a critical component of efficient deoxyribonucleic acid (DNA) data storage systems. In this paper, we consider a concatenated coding scheme with an outer nonbinary low-density parity-check code or a polar code and either an inner convolutional code or a time-varying block code. We propose two novel decoding algorithms for inference from multiple received sequences, both combining the inner code and channel to a joint hidden Markov model to infer symbolwise a posteriori probabilities (APPs). The first decoder computes the exact APPs by jointly decoding the received sequences, whereas the second decoder approximates the APPs by combining the results of separately decoded received sequences and has a complexity that is linear with the number of sequences. Using the proposed algorithms, we evaluate the performance of decoding multiple received sequences by means of achievable information rates and Monte-Carlo simulations. We show significant performance gains compared to a single received sequence. In addition, we succeed in improving the performance of the aforementioned coding scheme by optimizing both the inner and outer codes.
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| 3. |
- Welter, Lorenz, et al.
(författare)
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Index-Based Concatenated Codes for the Multi-Draw DNA Storage Channel
- 2023
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Ingår i: 2023 IEEE Information Theory Workshop, ITW 2023. ; , s. 383-388
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Konferensbidrag (refereegranskat)abstract
- We consider error-correcting coding for DNA-based storage. We model the DNA storage channel as a multi-draw IDS channel where the input data is chunked into M short DNA strands, which are copied a random number of times, and the channel outputs a random selection of N noisy DNA strands. The retrieved DNA strands are prone to insertion, deletion, and substitution (IDS) errors. We propose an index-based concatenated coding scheme consisting of the concatenation of an outer code, an index code, and an inner synchronization code, where the latter two tackle IDS errors. We further propose a mismatched joint index-synchronization code maximum a posteriori probability decoder with optional clustering to infer symbolwise a posteriori probabilities for the outer decoder. We compute achievable information rates for the outer code and present Monte-Carlo simulations for information-outage probabilities and frame error rates on synthetic and experimental data, respectively.
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| 4. |
- Xhemrishi, Marvin, et al.
(författare)
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Computational Code-Based Privacy in Coded Federated Learning
- 2022
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Ingår i: IEEE International Symposium on Information Theory - Proceedings. - 2157-8095. ; 2022-June, s. 2034-2039
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Konferensbidrag (refereegranskat)abstract
- We propose a privacy-preserving federated learning (FL) scheme that is resilient against straggling devices. An adaptive scenario is suggested where the slower devices share their data with the faster ones and do not participate in the learning process. The proposed scheme employs code-based cryptography to ensure computational privacy of the private data, i.e., no device with bounded computational power can obtain information about the other devices' data in feasible time. For a scenario with 25 devices, the proposed scheme achieves a speed-up of 4.7 and 4 for 92 and 128 bits security, respectively, for an accuracy of 95% on the MNIST dataset compared with conventional mini-batch FL.
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