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| 2. |
- Fan, Yuchuan, et al.
(författare)
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A Comparison of Linear Regression and Deep Learning Model for EVM Estimation in Coherent Optical Systems
- 2022
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Ingår i: 2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2022 - Proceedings. - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- We experimentally investigate EVM estimation approaches based on linear regression and deep learning for 28 Gbaud coherent optical systems. We show that the estimation performances are comparable when the modulation format is known.
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| 4. |
- Fan, Yuchuan, et al.
(författare)
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EVM Estimation for Performance Monitoring in Coherent Optical Systems : An Approach of Linear Regression
- 2022
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Ingår i: Optics InfoBase Conference Papers. - : Optica Publishing Group (formerly OSA). - 9781557528209
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Konferensbidrag (refereegranskat)abstract
- We experimentally demonstrate the effectiveness of a simple linear regression scheme for optical performance monitoring when applied after modulation format identification. It outperforms the FFNN-based benchmark scheme providing 0.2% mean absolute error for EVM estimation., © 2022 The Author(s)
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| 6. |
- Fan, Yuchuan, et al.
(författare)
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Feedforward Neural Network-Based EVM Estimation : Impairment Tolerance in Coherent Optical Systems
- 2022
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Ingår i: IEEE Journal of Selected Topics in Quantum Electronics. - : Institute of Electrical and Electronics Engineers Inc.. - 1077-260X .- 1558-4542. ; 28:4
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Tidskriftsartikel (refereegranskat)abstract
- Error vector magnitude (EVM) is commonly used for evaluating the quality of m-ary quadrature amplitude modulation (mQAM) signals. Recently proposed deep learning techniques for EVM estimation extend the functionality of conventional optical performance monitoring (OPM). In this article, we evaluate the tolerance of our developed EVM estimation scheme against various impairments in coherent optical systems. In particular, we analyze the signal quality monitoring capabilities in the presence of residual in-phase/quadrature (IQ) imbalance, fiber nonlinearity, and laser phase noise. We use feedforward neural networks (FFNNs) to extract the EVM information from amplitude histograms of 100 symbols per IQ cluster signal sequence captured before carrier phase recovery. We perform simulations of the considered impairments, along with an experimental investigation of the impact of laser phase noise. To investigate the tolerance of the EVM estimation scheme to each impairment type, we compare the accuracy for three training methods: 1) training without impairment, 2) training one model for all impairments, and 3) training an independent model for each impairment. Results indicate a good generalization of the proposed EVM estimation scheme, thus providing a valuable reference for developing next-generation intelligent OPM systems.
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| 7. |
- Fan, Yuchuan, et al.
(författare)
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Linear Regression vs. Deep Learning for Signal Quality Monitoring in Coherent Optical Systems
- 2022
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Ingår i: IEEE Photonics Journal. - : Institute of Electrical and Electronics Engineers Inc.. - 1943-0655. ; 14:4
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Tidskriftsartikel (refereegranskat)abstract
- Error vector magnitude (EVM) is a metric for assessing the quality of m-ary quadrature amplitude modulation (mQAM) signals. Recently proposed deep learning techniques, e.g., feedforward neural networks (FFNNs) -based EVM estimation scheme leverage fast signal quality monitoring in coherent optical communication systems. Such a scheme estimates EVM from amplitude histograms (AHs) of short signal sequences captured before carrier phase recovery (CPR). In this work, we explore further complexity reduction by proposing a simple linear regression (LR) -based EVM monitoring method. We systematically compare the performance of the proposed method with the FFNN-based scheme and demonstrate its capability to infer EVM from an AH when the modulation format information is known in advance. We perform both simulation and experiment to show that the LR-based EVM estimation method achieves a comparable accuracy as the FFNN-based scheme. The technique can be embedded with modulation format identification modules to provide comprehensive signal information. Therefore, this work paves the way to design a fast-learning scheme with parsimony as a future intelligent OPM enabler.
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| 8. |
- Fan, Yuchuan
(författare)
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Optical Performance Monitoring in Digital Coherent Communications: Intelligent Error Vector Magnitude Estimation
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The rapid development of data-driven techniques brings us new applications, such asfifth-generation new radio (5G NR), high-definition video, Internet of things (IoT),etc., which has greatly facilitated our daily lives. Optical networks as one fundamen-tal infrastructure are evolving to simultaneously support these high dimensional dataservices, with a feature of flexible, dynamic, and heterogeneous. Optical performancemonitoring (OPM) is a key enabler to guarantee reliable network management andmaintenance, which improving network controllability and resource efficiency. Accu-rately telemetry key performance indicators (KPIs) such as bit error rate (BER) canextend monitoring functionality and secure network management. However, retrievingthe BER level metric is time-consuming and inconvenient for OPM. Low-complexityOPM strategies are highly desired for ubiquitous departments at optical network nodes.This thesis investigates machine learning (ML) based intelligent error vector mag-nitude (EVM) estimation schemes in digital coherent communications, where EVMis widely used as an alternative BER metric for multilevel modulated signals. Wepropose a prototype of EVM estimation, which enables monitoring signal quality froma short observation period. Three alternative ML algorithms are explored to facilitatethe implementation of this prototype, namely convolutional neural networks (CNNs),feedforward neural networks (FFNNs), and linear regression (LR). We show that CNNconjunction with graphical signal representations, i.e., constellation diagrams and am-plitude histograms (AHs), can achieve decent EVM estimation accuracy for signalsbefore and after carrier phase recovery (CPR), which outperforms the conventionalEVM calculation. Moreover, we show that an FFNN-based scheme can reduce poten-tial energy and keep the estimation accuracy by directly operating with AH vectors.Furthermore, the estimation capability is thoroughly studied when the system hasdifferent impairments. Lastly, we demonstrate that a simple LR-designed model canperform as well as FFNN when the information on modulation formats is known. SuchLR-based can be easily implemented with modulation formats identification modulein OPM, providing accurate signal quality information.
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| 9. |
- Jia, Shi, et al.
(författare)
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Integrated dual-laser photonic chip for high-purity carrier generation enabling ultrafast terahertz wireless communications
- 2022
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Ingår i: Nature Communications. - : Springer Nature. - 2041-1723. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Photonic generation of Terahertz (THz) carriers displays high potential for THz communications with a large tunable range and high modulation bandwidth. While many photonics-based THz generations have recently been demonstrated with discrete bulky components, their practical applications are significantly hindered by the large footprint and high energy consumption. Herein, we present an injection-locked heterodyne source based on generic foundry-fabricated photonic integrated circuits (PIC) attached to a uni-traveling carrier photodiode generating high-purity THz carriers. The generated THz carrier is tunable within the range of 0-1.4 THz, determined by the wavelength spacing between the two monolithically integrated distributed feedback (DFB) lasers. This scheme generates and transmits a 131 Gbits(-1) net rate signal over a 10.7-m distance with -24 dBm emitted power at 0.4 THz. This monolithic dual-DFB PIC-based THz generation approach is a significant step towards fully integrated, cost-effective, and energy-efficient THz transmitters. A photonic Terahertz source based on injection-locking an integrated dual-laser chip generates and transmits a 131 Gbps THz signal over 10.7-m distance, showing great potential towards fully integrated and energy-efficient THz transmitters for 6G.
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| 10. |
- Matsenko, Svitlana, et al.
(författare)
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FPGA-Implemented Fractal Decoder with Forward Error Correction in Short-Reach Optical Interconnects
- 2022
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Ingår i: Entropy. - : MDPI AG. - 1099-4300. ; 24:1
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Tidskriftsartikel (refereegranskat)abstract
- Forward error correction (FEC) codes combined with high-order modulator formats, i.e., coded modulation (CM), are essential in optical communication networks to achieve highly efficient and reliable communication. The task of providing additional error control in the design of CM systems with high-performance requirements remains urgent. As an additional control of CM systems, we propose to use indivisible error detection codes based on a positional number system. In this work, we evaluated the indivisible code using the average probability method (APM) for the binary symmetric channel (BSC), which has the simplicity, versatility and reliability of the estimate, which is close to reality. The APM allows for evaluation and compares indivisible codes according to parameters of correct transmission, and detectable and undetectable errors. Indivisible codes allow for the end-to-end (E2E) control of the transmission and processing of information in digital systems and design devices with a regular structure and high speed. This study researched a fractal decoder device for additional error control, implemented in field-programmable gate array (FPGA) software with FEC for short-reach optical interconnects with multilevel pulse amplitude (PAM-M) modulated with Gray code mapping. Indivisible codes with natural redundancy require far fewer hardware costs to develop and implement encoding and decoding devices with a sufficiently high error detection efficiency. We achieved a reduction in hardware costs for a fractal decoder by using the fractal property of the indivisible code from 10% to 30% for different n while receiving the reciprocal of the golden ratio.
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