On the Robustness of Deep Learning-aided Symbol Detectors to Varying Conditions and Imperfect Channel Knowledge (2401.12645v1)
Abstract: Recently, a data-driven Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm tailored to channels with intersymbol interference has been introduced. This so-called BCJRNet algorithm utilizes neural networks to calculate channel likelihoods. BCJRNet has demonstrated resilience against inaccurate channel tap estimations when applied to a time-invariant channel with ideal exponential decay profiles. However, its generalization capabilities for practically-relevant time-varying channels, where the receiver can only access incorrect channel parameters, remain largely unexplored. The primary contribution of this paper is to expand upon the results from existing literature to encompass a variety of imperfect channel knowledge cases that appear in real-world transmissions. Our findings demonstrate that BCJRNet significantly outperforms the conventional BCJR algorithm for stationary transmission scenarios when learning from noisy channel data and with imperfect channel decay profiles. However, this advantage is shown to diminish when the operating channel is also rapidly time-varying. Our results also show the importance of memory assumptions for conventional BCJR and BCJRNet. An underestimation of the memory largely degrades the performance of both BCJR and BCJRNet, especially in a slow-decaying channel. To mimic a situation closer to a practical scenario, we also combined channel tap uncertainty with imperfect channel memory knowledge. Somewhat surprisingly, our results revealed improved performance when employing the conventional BCJR with an underestimated memory assumption. BCJRNet, on the other hand, showed a consistent performance improvement as the level of accurate memory knowledge increased.
- O. Simeone, “A very brief introduction to machine learning with applications to communication systems,” IEEE Transactions on Cognitive Communications and Networking, vol. 4, no. 4, 2018.
- T. O’Shea and J. Hoydis, “An introduction to deep learning for the physical layer,” IEEE Transactions on Cognitive Communications and Networking, vol. 3, no. 4, 2017.
- S. Dörner, S. Cammerer, J. Hoydis, and S. ten Brink, “Deep learning based communication over the air,” IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 1, 2018.
- B. Karanov, M. Chagnon, F. Thouin, T. A. Eriksson, H. Bülow, D. Lavery, P. Bayvel, and L. Schmalen, “End-to-end deep learning of optical fiber communications,” Journal of Lightwave Technology, vol. 36, no. 20, 2018.
- B. Karanov, D. Lavery, P. Bayvel, and L. Schmalen, “End-to-end optimized transmission over dispersive intensity-modulated channels using bidirectional recurrent neural networks,” Opt. Express, vol. 27, no. 14, Jul 2019.
- F. A. Aoudia and J. Hoydis, “End-to-end learning of communications systems without a channel model,” in 2018 52nd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, Oct. 2018.
- H. Ye, G. Y. Li, B.-H. F. Juang, and K. Sivanesan, “Channel agnostic end-to-end learning based communication systems with conditional GAN,” in 2018 IEEE Globecom Workshops (GC Wkshps), Abu Dhabi, United Arab Emirates, Dec. 2018.
- T. J. O’Shea, T. Roy, and N. West, “Approximating the void: Learning stochastic channel models from observation with variational generative adversarial networks,” in 2019 International Conference on Computing, Networking and Communications (ICNC), Honolulu, Hawaii, USA, Feb. 2019.
- B. Karanov, M. Chagnon, V. Aref, D. Lavery, P. Bayvel, and L. Schmalen, “Concept and experimental demonstration of optical IM/DD end-to-end system optimization using a generative model,” in 2020 Optical Fiber Communications Conference and Exhibition (OFC), San Diego, California, USA, Mar. 2020.
- N. Farsad and A. Goldsmith, “Neural network detection of data sequences in communication systems,” IEEE Transactions on Signal Processing, vol. 66, no. 21, 2018.
- N. Shlezinger, Y. C. Eldar, N. Farsad, and A. J. Goldsmith, “ViterbiNet: Symbol detection using a deep learning based Viterbi algorithm,” in 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Cannes, France, Jul. 2019.
- E. Nachmani, E. Marciano, L. Lugosch, W. J. Gross, D. Burshtein, and Y. Be’ery, “Deep learning methods for improved decoding of linear codes,” IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 1, 2018.
- N. Farsad, N. Shlezinger, A. J. Goldsmith, and Y. C. Eldar, “Data-driven symbol detection via model-based machine learning,” in 21 IEEE Statistical Signal Processing Workshop (SSP), Rio de Janeiro, Brazil, Jul. 2021.
- N. Shlezinger, N. Farsad, Y. C. Eldar, and A. J. Goldsmith, “Learned factor graphs for inference from stationary time sequences,” IEEE Transactions on Signal Processing, vol. 70, 2022.
- N. Shlezinger, J. Whang, Y. C. Eldar, and A. G. Dimakis, “Model-based deep learning,” Proceedings of the IEEE, 2023.
- L. Bahl, J. Cocke, F. Jelinek, and J. Raviv, “Optimal decoding of linear codes for minimizing symbol error rate (corresp.),” IEEE Transactions on Information Theory, vol. 20, no. 2, 1974.
- G. Forney, “The Viterbi algorithm,” Proceedings of the IEEE, vol. 61, no. 3, 1973.
- M. Chagnon, B. Karanov, and L. Schmalen, “Experimental demonstration of a dispersion tolerant end-to-end deep learning-based IM-DD transmission system,” in 2018 European Conference on Optical Communication (ECOC), Rome, Italy, Sep. 2018.
- B. Karanov, L. Schmalen, and A. Alvarado, “Distance-agnostic auto-encoders for short reach fiber communications,” in 2021 Optical Fiber Communications Conference and Exhibition (OFC), Washington DC, USA, Jun. 2021.