| 1. |
- Ivanov, Mikhail, 1986, et al.
(författare)
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On the Information Loss of the Max-Log Approximation in BICM Systems
- 2016
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Ingår i: IEEE Transactions on Information Theory. - 0018-9448 .- 1557-9654. ; 62:6, s. 3011 - 3025
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Tidskriftsartikel (refereegranskat)abstract
- We present a comprehensive study of the information rate loss of the max-log approximation for M-ary pulse-amplitude modulation (PAM) in a bit-interleaved coded modulation (BICM) system. It is widely assumed that the calculation of L-values using the max-log approximation leads to an information loss. We prove that this assumption is correct for all M-PAM constellations and labelings with the exception of a symmetric 4-PAM constellation labeled with a Gray code. We also show that for max-log L-values, the BICM generalized mutual information (GMI), which is an achievable rate for a standard BICM decoder, is too pessimistic. In particular, it is proved that the so-called harmonized GMI, which can be seen as the sum of bit-level GMIs, is achievable without any modifications to the decoder. We then study how bit-level channel symmetrization and mixing affect the MI and the GMI for max-log L-values. Our results show that these operations, which are often used when analyzing BICM systems, preserve the GMI. However, this is not necessarily the case when the MI is considered. Necessary and sufficient conditions under which these operations preserve the MI are provided.
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| 2. |
- Buchberger, Andreas, 1990, et al.
(författare)
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Learned Decimation for Neural Belief Propagation Decoders
- 2021
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Ingår i: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. - 1520-6149. ; 2021-June, s. 8273-8277
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Konferensbidrag (refereegranskat)abstract
- We introduce a two-stage decimation process to improve the performance of neural belief propagation (NBP), recently introduced by Nachmani et al., for short low-density parity-check (LDPC) codes. In the first stage, we build a list by iterating between a conventional NBP decoder and guessing the least reliable bit. The second stage iterates between a conventional NBP decoder and learned decimation, where we use a neural network to decide the decimation value for each bit. For a (128,64) LDPC code, the proposed NBP with decimation outperforms NBP decoding by 0.75dB and performs within 1dB from maximum-likelihood decoding at a block error rate of 10-4.
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| 3. |
- Buchberger, Andreas, 1990, et al.
(författare)
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Pruning and Quantizing Neural Belief Propagation Decoders
- 2021
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Ingår i: IEEE Journal on Selected Areas in Communications. - 0733-8716 .- 1558-0008. ; 39:7, s. 1957-1966
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Tidskriftsartikel (refereegranskat)abstract
- We consider near maximum-likelihood (ML) decoding of short linear block codes. In particular, we propose a novel decoding approach based on neural belief propagation (NBP) decoding recently introduced by Nachmani et al. in which we allow a different parity-check matrix in each iteration of the algorithm. The key idea is to consider NBP decoding over an overcomplete parity-check matrix and use the weights of NBP as a measure of the importance of the check nodes (CNs) to decoding. The unimportant CNs are then pruned. In contrast to NBP, which performs decoding on a given fixed parity-check matrix, the proposed pruning-based neural belief propagation (PB-NBP) typically results in a different parity-check matrix in each iteration. For a given complexity in terms of CN evaluations, we show that PB-NBP yields significant performance improvements with respect to NBP. We apply the proposed decoder to the decoding of a Reed-Muller code, a short low-density parity-check (LDPC) code, and a polar code. PB-NBP outperforms NBP decoding over an overcomplete parity-check matrix by 0.27–0.31 dB while reducing the number of required CN evaluations by up to 97%. For the LDPC code, PB-NBP outperforms conventional belief propagation with the same number of CN evaluations by 0.52 dB. We further extend the pruning concept to offset min-sum decoding and introduce a pruning-based neural offset min-sum (PB-NOMS) decoder, for which we jointly optimize the offsets and the quantization of the messages and offsets. We demonstrate performance 0.5 dB from ML decoding with 5-bit quantization for the Reed-Muller code.
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| 4. |
- Buchberger, Andreas, 1990, et al.
(författare)
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Pruning Neural Belief Propagation Decoders
- 2020
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Ingår i: IEEE International Symposium on Information Theory - Proceedings. - 2157-8095. ; 2020-June, s. 338-342
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Konferensbidrag (refereegranskat)abstract
- We consider near maximum-likelihood (ML) decoding of short linear block codes based on neural belief propagation (BP) decoding recently introduced by Nachmani et al.. While this method significantly outperforms conventional BP decoding, the underlying parity-check matrix may still limit the overall performance. In this paper, we introduce a method to tailor an overcomplete parity-check matrix to (neural) BP decoding using machine learning. We consider the weights in the Tanner graph as an indication of the importance of the connected check nodes (CNs) to decoding and use them to prune unimportant CNs. As the pruning is not tied over iterations, the final decoder uses a different parity-check matrix in each iteration. For ReedMuller and short low-density parity-check codes, we achieve performance within 0.27dB and 1.5dB of the ML performance while reducing the complexity of the decoder.
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| 5. |
- Butler, Rick M., et al.
(författare)
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Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation
- 2021
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Ingår i: Journal of Lightwave Technology. - 0733-8724 .- 1558-2213. ; 39:4, s. 949-959
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Tidskriftsartikel (refereegranskat)abstract
- In this article, we propose a model-based machine-learning approach for dual-polarization systems by parameterizing the split-step Fourier method for the Manakov-PMD equation. The resulting method combines hardware-friendly time-domain nonlinearity mitigation via the recently proposed learned digital backpropagation (LDBP) with distributed compensation of polarization-mode dispersion (PMD). We refer to the resulting approach as LDBP-PMD. We train LDBP-PMD on multiple PMD realizations and show that it converges within 1% of its peak dB performance after 428 training iterations on average, yielding a peak effective signal-to-noise ratio of only 0.30 dB below the PMD-free case. Similar to state-of-the-art lumped PMD compensation algorithms in practical systems, our approach does not assume any knowledge about the particular PMD realization along the link, nor any knowledge about the total accumulated PMD. This is a significant improvement compared to prior work on distributed PMD compensation, where knowledge about the accumulated PMD is typically assumed. We also compare different parameterization choices in terms of performance, complexity, and convergence behavior. Lastly, we demonstrate that the learned models can be successfully retrained after an abrupt change of the PMD realization along the fiber.
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| 6. |
- Börjeson, Erik, 1984, et al.
(författare)
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Real-Time Implementation of Machine-Learning DSP
- 2024
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Ingår i: 2024 Optical Fiber Communications Conference and Exhibition, OFC 2024 - Proceedings.
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Konferensbidrag (refereegranskat)abstract
- While ML algorithms can learn and adapt to channel characteristics, implementation of ML-based DSP hardware is challenging. We demonstrate a real-time implementation of a model-based ML equalizer that compensates a non-linear and time-varying channel.
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| 7. |
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| 8. |
- Farsi, Mohammad, 1994, et al.
(författare)
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Improved Polarization Tracking in the Presence of PDL
- 2022
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Ingår i: European Conference on Optical Communication, ECOC. - 9781957171159
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Konferensbidrag (refereegranskat)abstract
- We propose a novel tracking algorithm for optical channels suffering from fast state of polarization (SOP) rotations and polarization-dependent loss (PDL). Unlike gradient descent-based algorithms that require step size adjustment when the channel conditions change, our algorithm performs similarly or better without parameter tuning.
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| 9. |
- Farsi, Mohammad, 1994, et al.
(författare)
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Learning to Extract Distributed Polarization Sensing Data from Noisy Jones Matrices
- 2024
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Ingår i: 2024 Optical Fiber Communication Conference and Exhibition, OFC 2024 - Proceeding. - 2162-2701. - 9781957171326
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Konferensbidrag (refereegranskat)abstract
- We consider the problem of recovering spatially resolved polarization information from receiver Jones matrices. We introduce a physics-based learning approach, improving noise resilience compared to previous inverse scattering methods, while highlighting challenges related to model overparameterization.
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| 10. |
- Farsi, Mohammad, 1994, et al.
(författare)
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Polarization Tracking in the Presence of PDL and Fast Temporal Drift
- 2022
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Ingår i: Journal of Lightwave Technology. - 0733-8724 .- 1558-2213. ; 40:19, s. 6408-6416
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we analyze the effectiveness of polarization tracking algorithms in optical transmission systems suffering from fast state of polarization (SOP) rotations and polarization-dependent loss (PDL). While most of the gradient descent (GD)-based algorithms in the literature may require step size adjustment when the channel condition changes, we propose tracking algorithms that can perform similarly or better without parameter tuning. Numerical simulation results show higher robustness of the proposed algorithms to SOP and PDL drift compared to GD-based algorithms, making them promising candidates to be used in aerial fiber links where the SOP can potentially drift rapidly, and therefore becomes challenging to track.
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