| 1. |
- He, Zonglong, 1994, et al.
(författare)
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Experimental Demonstration of Learned Pulse Shaping Filter for Superchannels
- 2022
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Ingår i: 2022 Optical Fiber Communications Conference and Exhibition, OFC 2022 - Proceedings.
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Konferensbidrag (refereegranskat)abstract
- We demonstrate a pulse shaping filter enabled by machine learning for spectral superchannels. In contrast to a 1% roll-off root-raised cosine filter, our learned filter reduces the adaptive equalizer length by 47% for the same spectral efficiency.
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| 2. |
- He, Zonglong, 1994, et al.
(författare)
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Periodicity-Enabled Size Reduction of Symbol Based Predistortion for High-Order QAM
- 2022
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Ingår i: Journal of Lightwave Technology. - 0733-8724 .- 1558-2213. ; In press
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Tidskriftsartikel (refereegranskat)abstract
- We experimentally demonstrate a novel size reduction approach for symbol-based look-up table (LUT) digital predistortion (DPD) of the transmitter impairments taking advantage of the periodicity in the pattern-dependent distortions. Compared to other reduced-size LUT schemes, the proposed method can significantly lessen the storage memory requirements with negligible performance penalty for high-order modulation formats. To further alleviate the storage memory restriction, a twice reduced-size LUT scheme is proposed to provide further size reduction. Importantly, given a targeted memory length, we verify the importance of averaging over sufficient occurrences of the patterns to obtain a well-performing LUT. Moreover, it is necessary to evaluate the performance of LUT-based DPD using random data. Finally, we demonstrate a neural network (NN) based nonlinear predistortion technique, which achieves nearly identical performance to the full-size LUT for all employed constellations and is robust against a change of modulation format. The proposed techniques are verified in a back-to-back transmission experiment of 20 Gbaud 64-QAM, 256-QAM, and 1024-QAM signals considering 3 and 5 symbol memory. The performance of the LUT-based DPD is further validated in a noise loading experiment.
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| 3. |
- He, Zonglong, 1994, et al.
(författare)
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Symbol-Based Supervised Learning Predistortion for Compensating Transmitter Nonlinearity
- 2021
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Ingår i: 2021 European Conference on Optical Communication, ECOC 2021. - 9781665438681
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Konferensbidrag (refereegranskat)abstract
- We experimentally demonstrate a symbol-based nonlinear digital predistortion (DPD) technique utilizing supervised learning, which is robust against a change of modulation format. Back-to-back transmission of 30 Gbaud 32, 64 and 256QAM confirms that our scheme significantly outperforms the baseline of arcsine-based predistortion.
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| 4. |
- Song, Jinxiang, 1995, et al.
(författare)
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Blind Frequency-Domain Equalization Using Vector-Quantized Variational Autoencoders
- 2023
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Ingår i: 2023 European Conference on Optical Communications, ECOC 2023. ; In press
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Konferensbidrag (refereegranskat)abstract
- We propose a novel frequency-domain blind equalization scheme for coherent optical communications. The method is shown to achieve similar performance to its recently proposed time-domain counterpart with lower computational complexity, while outperforming the commonly used CMA-based equalizers.
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| 5. |
- Song, Jinxiang, 1995, et al.
(författare)
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End-to-end Autoencoder for Superchannel Transceivers with Hardware Impairments
- 2021
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Ingår i: 2021 Optical Fiber Communications Conference and Exhibition, OFC 2021 - Proceedings.
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Konferensbidrag (refereegranskat)abstract
- We propose an end-to-end learning-based approach for superchannel systems impaired by non-ideal hardware component. Our system achieves up to 60% SER reduction and up to 50% guard band reduction compared with the considered baseline scheme.
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| 6. |
- Song, Jinxiang, 1995, et al.
(författare)
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Model-Based End-to-End Learning for WDM Systems With Transceiver Hardware Impairments
- 2022
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Ingår i: IEEE Journal of Selected Topics in Quantum Electronics. - 1558-4542 .- 1077-260X. ; 28:4
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Tidskriftsartikel (refereegranskat)abstract
- We propose an AE-based transceiver for a WDM system impaired by hardware imperfections. We design our AE following the architecture of conventional communication systems. This enables to initialize the AE-based transceiver to have similar performance to its conventional counterpart prior to training and improves the training convergence rate. We first train the AE in a single-channel system, and show that it achieves performance improvements by putting energy outside the desired bandwidth, and therefore cannot be used for a WDM system. We then train the AE in a WDM setup. Simulation results show that the proposed AE significantly outperforms the conventional approach. More specifically, it increases the spectral efficiency of the considered system by reducing the guard band by 37% and 50% for a root-raised-cosine filter-based matched filter with 10% and 1% roll-off, respectively. An ablation study indicates that the performance gain can be ascribed to the optimization of the symbol mapper, the pulse-shaping filter, and the symbol demapper. Finally, we use reinforcement learning to learn the pulse-shaping filter under the assumption that the channel model is unknown. Simulation results show that the reinforcement-learning-based algorithm achieves similar performance to the standard supervised end-to-end learning approach assuming perfect channel knowledge.
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| 7. |
- Song, Jinxiang, 1995, et al.
(författare)
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Over-the-fiber Digital Predistortion Using Reinforcement Learning
- 2021
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Ingår i: 2021 European Conference on Optical Communication, ECOC 2021. - 9781665438681
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Konferensbidrag (refereegranskat)abstract
- We demonstrate, for the first time, experimental over-the-fiber training of transmitter neural networks (NNs) using reinforcement learning. Optical back-to-back training of a novel NN-based digital predistorter outperforms arcsine-based predistortion with up to 60% bit-error-rate reduction.
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| 8. |
- Ninkovic, Vukan, et al.
(författare)
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Autoencoder-Based Unequal Error Protection Codes
- 2021
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Ingår i: IEEE Communications Letters. - 1558-2558 .- 1089-7798. ; 25:11, s. 3575-3579
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Tidskriftsartikel (refereegranskat)abstract
- We present a novel autoencoder-based approach for designing codes that provide unequal error protection (UEP) capabilities. The proposed approach, based on a generalization of an autoencoder loss function, provides a versatile framework for the design of message-wise and bit-wise UEP codes. Using an associated weight vector, the generalized loss function can be used to trade off error probabilities corresponding to different importance classes and to explore the region of achievable error probabilities. For message-wise UEP, we compare the proposed autoencoder-based UEP codes with a union of random coset codes. For bit-wise UEP, the proposed codes are compared with UEP rateless spinal codes and the superposition of random Gaussian codes. In all cases, the autoencoder-based codes show superior performance while providing design simplicity and flexibility in trading off error protection among different importance classes.
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| 9. |
- Ninkovic, Vukan, et al.
(författare)
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Rateless Autoencoder Codes: Trading off Decoding Delay and Reliability
- 2023
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Ingår i: IEEE International Conference on Communications. - 1550-3607. ; 2023-May, s. 6361-6366
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Konferensbidrag (refereegranskat)abstract
- Most of today's communication systems are designed to target reliable message recovery after receiving the entire encoded message (codeword). However, in many practical scenarios, the transmission process may be interrupted before receiving the complete codeword. This paper proposes a novel rateless autoencoder (AE)-based code design suitable for decoding the transmitted message before the noisy codeword is fully received. Using particular dropout strategies applied during the training process, rateless AE codes allow to trade off between decoding delay and reliability, providing a graceful improvement of the latter with each additionally received codeword symbol. The proposed rateless AEs significantly outperform the conventional AE designs for scenarios where it is desirable to trade off reliability for lower decoding delay.
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| 10. |
- Wu, Yibo, 1996, et al.
(författare)
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Frequency-domain digital predistortion for Massive MU-MIMO-OFDM Downlink
- 2022
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Ingår i: GLOBECOM - IEEE Global Telecommunications Conference. ; , s. 579-584
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Konferensbidrag (refereegranskat)abstract
- Digital predistortion (DPD) is a method commonly used to compensate for the nonlinear effects of power amplifiers (PAs). However, the computational complexity of most DPD algorithms becomes an issue in the downlink of massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM), where potentially up to several hundreds of PAs in the base station (BS) require linearization. In this paper, we propose a convolutional neural network (CNN)-based DPD in the frequency domain, taking place before the precoding, where the dimensionality of the signal space depends on the number of users, instead of the number of BS antennas. Simulation results on generalized memory polynomial (GMP)-based PAs show that the proposed CNN-based DPD can lead to very large complexity savings as the number of BS antenna increases at the expense of a small increase in power to achieve the same symbol error rate (SER).
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