| 1. |
- He, Zonglong, 1994, et al.
(author)
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Experimental Demonstration of Learned Pulse Shaping Filter for Superchannels
- 2022
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In: 2022 Optical Fiber Communications Conference and Exhibition, OFC 2022 - Proceedings.
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Conference paper (peer-reviewed)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.
(author)
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Periodicity-Enabled Size Reduction of Symbol Based Predistortion for High-Order QAM
- 2022
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In: Journal of Lightwave Technology. - 0733-8724 .- 1558-2213. ; 40:18, s. 6168-6178
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Journal article (peer-reviewed)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.
(author)
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Symbol-Based Supervised Learning Predistortion for Compensating Transmitter Nonlinearity
- 2021
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In: 2021 European Conference on Optical Communication, ECOC 2021. - 9781665438681
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Conference paper (peer-reviewed)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. |
- Mateos Ramos, José Miguel, 1998, et al.
(author)
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End-to-End Learning for Integrated Sensing and Communication
- 2022
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In: IEEE International Conference on Communications. - 1550-3607. ; 2022-May, s. 1942-1947
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Conference paper (peer-reviewed)abstract
- Integrated sensing and communication (ISAC) aims to unify radar and communication systems through a combination of joint hardware, joint waveforms, joint signal design, and joint signal processing. At high carrier frequencies, where ISAC is expected to play a major role, joint designs are challenging due to several hardware limitations. Model-based approaches, while powerful and flexible, are inherently limited by how well the models represent reality. Under model deficit, data-driven methods can provide robust ISAC performance. We present a novel approach for data-driven ISAC using an auto-encoder (AE) structure. The approach includes the proposal of the AE architecture, a novel ISAC loss function, and the training procedure. Numerical results demonstrate the power of the proposed AE, in particular under hardware impairments.
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| 5. |
- Rivetti, Steven, 1998, et al.
(author)
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Spatial Signal Design for Positioning via End-to-End Learning
- 2023
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In: IEEE Wireless Communications Letters. - 2162-2345 .- 2162-2337. ; 12:3, s. 525-529
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Journal article (peer-reviewed)abstract
- This letter considers the problem of end-to-end (E2E) learning for joint optimization of transmitter precoding and receiver processing for mmWave downlink positioning. Considering a multiple-input single-output (MISO) scenario, we propose a novel autoencoder (AE) architecture to estimate user equipment (UE) position with multiple base stations (BSs) and demonstrate that E2E learning can match model-based design, both for angle-of-departure (AoD) and position estimation, under ideal conditions without model deficits and outperform it in the presence of hardware impairments.
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| 6. |
- Song, Jinxiang, 1995
(author)
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Autoencoders for Physical-Layer Communications: Approaches and Applications
- 2023
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Doctoral thesis (other academic/artistic)abstract
- The ever-growing demand for higher data rates has driven continuous developments in communication systems over the years. As upcoming high-bandwidth services require even higher data rates, future digital communication infrastructures must undergo continuous upgrades to provide increased capacity. Recently, machine learning has surfaced as a potential tool to augment this capacity further. A particularly promising avenue lies in the application of autoencoders. These can concurrently optimize both the transmitter and receiver tailored to a specific channel model and performance metric, a paradigm commonly referred to as end-to-end autoencoder learning. In this thesis, we study different aspects of using machine learning for physical-layer communications, spanning wireless and optical communication in terms of applications, and unsupervised, supervised, and reinforcement learning in terms of methodologies. The main contributions of this thesis are listed as follows. Firstly, to overcome the challenge that standard end-to-end autoencoder learning requires a differentiable channel model for gradient-based transmitter optimization, Paper A and Paper B explore reinforcement learning-based transmitter optimization. In Paper A, considering that reinforcement learning-based training necessitates sending a feedback signal from the receiver to the transmitter, we propose a novel method for the feedback signal quantization. Simulation results demonstrate that the proposed quantization scheme facilitates effective transmitter learning with limited feedback. In Paper B, reinforcement learning is applied to mitigate transmitter hardware impairments. A novel digital predistorter based on neural networks is introduced and trained in a back-to-back optical fiber transmission experiment. Experimental results demonstrate that the proposed digital predistorter effectively mitigates transmitter impairments, outperforming commonly used baseline schemes. Secondly, Paper C and Paper D focus on supervised learning, with an emphasis on improving the interpretability of end-to-end autoencoder learning-based communication systems. In Paper C, a novel model-based autoencoder is proposed for nonlinear systems. By decomposing the autoencoder-based transceivers into concatenations of smaller neural networks, the proposed method allows for the visualization of each learned functional block, improving the interpretability of the learned transmission scheme. Paper D interprets the learned solution from a different perspective by carefully selecting baseline schemes. We demonstrate that, for the linear systems considered in Paper D, machine learning methods do not significantly outperform conventional model-based approaches. Instead, they learn invertible transformations of these model-based solutions. Lastly, Paper E focuses on unsupervised learning, addressing the problem of blind channel equalization for both linear and non-linear channels. By introducing a constraint to the latent representation of a standard autoencoder, a novel autoencoder-based blind equalizer is formulated. Simulation results demonstrate that, for both linear and nonlinear channels, the proposed equalizer can achieve similar performance as conventional data-aided equalizers while outperforming state-of-the-art blind methods.
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| 7. |
- Song, Jinxiang, 1995, et al.
(author)
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Benchmarking and Interpreting End-to-end Learning of MIMO and Multi-User Communication
- 2022
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In: IEEE Transactions on Wireless Communications. - 1558-2248 .- 1536-1276. ; 21:9, s. 7287-7298
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Journal article (peer-reviewed)abstract
- End-to-end autoencoder (AE) learning has the potential of exceeding the performance of human-engineered transceivers and encoding schemes, without a priori knowledge of communication-theoretic principles. In this work, we aim to understand to what extent and for which scenarios this claim holds true when comparing with fair benchmarks. Our particular focus is on memoryless multiple-input multiple-output (MIMO) and multi-user (MU) systems. Four case studies are considered: two point-to-point (closed-loop and open-loop MIMO) and two MU scenarios (MIMO broadcast and interference channels). For the point-to-point scenarios, we explain some of the performance gains observed in prior work through the selection of improved baseline schemes that include geometric shaping as well as bit and power allocation. For the MIMO broadcast channel, we demonstrate the feasibility of a novel AE method with centralized learning and decentralized execution. Interestingly, the learned scheme performs close to nonlinear vector-perturbation precoding and significantly outperforms conventional zero-forcing. Lastly, we highlight potential pitfalls when interpreting learned communication schemes. In particular, we show that the AE for the considered interference channel learns to avoid interference, albeit in a rotated reference frame. After de-rotating the learned signal constellation of each user, the resulting scheme corresponds to conventional time sharing with geometric shaping.
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| 8. |
- Song, Jinxiang, 1995, et al.
(author)
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Benchmarking End-to-end Learning of MIMO Physical-Layer Communication
- 2020
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In: 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings.
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Conference paper (peer-reviewed)abstract
- End-to-end data-driven machine learning (ML) of multiple-input multiple-output (MIMO) systems has been shown to have the potential of exceeding the performance of engineered MIMO transceivers, without any a priori knowledge of communication-theoretic principles. In this work, we aim to understand to what extent and for which scenarios this claim holds true when comparing with fair benchmarks. We study closed-loop MIMO, open-loop MIMO, and multi-user MIMO (MU-MIMO) and show that the gains of ML-based communication in the former two cases can be to a large extent ascribed to implicitly learned geometric shaping and bit and power allocation, not to learning new spatial encoders. For MU-MIMO, we demonstrate the feasibility of a novel method with centralized learning and decentralized executing, outperforming conventional zero-forcing. For each scenario, we provide explicit descriptions as well as open-source implementations of the selected neural-network architectures.
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| 9. |
- Song, Jinxiang, 1995, et al.
(author)
-
Blind Frequency-Domain Equalization Using Vector-Quantized Variational Autoencoders
- 2023
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In: 2023 European Conference on Optical Communications, ECOC 2023. ; In press
-
Conference paper (peer-reviewed)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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| 10. |
- Song, Jinxiang, 1995, et al.
(author)
-
End-to-end Autoencoder for Superchannel Transceivers with Hardware Impairments
- 2021
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In: 2021 Optical Fiber Communications Conference and Exhibition, OFC 2021 - Proceedings.
-
Conference paper (peer-reviewed)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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