Enhanced Multi-Level Signal Recovery in Mobile Fronthaul Network Using DNN Decoder

Zhou, Qi; Lü, Feng; Xu, Mu; Peng, Peng‐Chun; Liu, Siming; Shen, Shuyi; Zhang, Rui; Yao, Shuang; Finkelstein, Jeffrey; Chang, Gee‐Kung

doi:10.1109/lpt.2018.2852601

Cited by 20 publications

(10 citation statements)

References 11 publications

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“…q, r represents the indices of the memory k is nonlinearity index and R c and R b exhibit the leading and lagging delay tap lengths, respectively. Since, in [62][63][64][65][66][67][68][69][70][71], it was established that GMP is better as compared to MP, therefore for simplicity, evaluation with GMP is included in this paper.…”

Section: Generalized Memory Polynomial (Gmp)mentioning

confidence: 99%

Experimental Demonstration and Performance Enhancement of 5G NR Multiband Radio over Fiber System Using Optimized Digital Predistortion

et al. 2021

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This paper presents an experimental realization of multiband 5G new radio (NR) optical front haul (OFH) based radio over fiber (RoF) system using digital predistortion (DPD). A novel magnitude-selective affine (MSA) based DPD method is proposed for the complexity reduction and performance enhancement of RoF link followed by its comparison with the canonical piece wise linearization (CPWL), decomposed vector rotation method (DVR) and generalized memory polynomial (GMP) methods. Similarly, a detailed study is shown followed by the implementation proposal of novel neural network (NN) for DPD followed by its comparison with MSA, CPWL, DVR and GMP methods. In the experimental testbed, 5G NR standard at 20 GHz with 50 MHz bandwidth and flexible-waveform signal at 3 GHz with 20 MHz bandwidth is used to cover enhanced mobile broad band and small cells scenarios. A dual drive Mach Zehnder Modulator having two distinct radio frequency signals modulates a 1310 nm optical carrier using distributed feedback laser for 22 km of standard single mode fiber. The experimental results are presented in terms of adjacent channel power ratio (ACPR), error vector magnitude (EVM), number of estimated coefficients and multiplications. The study aims to identify those novel methods such as MSA DPD are a good candidate to deploy in real time scenarios for DPD in comparison to NN based DPD which have a slightly better performance as compared to the proposed MSA method but has a higher complexity levels. Both, proposed methods, MSA and NN are meeting the 3GPP Release 17 requirements.

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Section: Generalized Memory Polynomial (Gmp)mentioning

confidence: 99%

Experimental Demonstration and Performance Enhancement of 5G NR Multiband Radio over Fiber System Using Optimized Digital Predistortion

et al. 2021

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“…Leveraging these advantages, models based on neural networks have become a promising candidate for physical layer signal processing in RoF systems [80][81][82][83][84][85][86][87][88][89][90][91][92][93][94]. Compared with other types of optical communication systems, because of the RF carrier, the wireless communication link and the analog optical link, the RoF system suffers more from complicated impairments, especially the nonlinear impairments, which are challenging for conventional signal processing schemes.…”

Section: Summary Of Neural Network-based Signal Processing In Rof Systemsmentioning

confidence: 99%

“…In previous studies, the FCNN is utilized as an equalizer to suppress various types of impairments, especially the nonlinear impairments. In addition to this, the FCNN has also been proposed and studied for both equalization and decoding in one step [88], as shown in Figure 10. In this type of FCNN decoder, the output layer typically uses the cross-entropy loss function and the Softmax function and hence, all possible output values with the corresponding probability are computed.…”

Section: Neural Network Decoders In Rof Systemsmentioning

confidence: 99%

Machine Learning Techniques in Radio-over-Fiber Systems and Networks

Lee

Kandeepan

et al. 2020

Photonics

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The radio-over-fiber (RoF) technology has been widely studied during the past decades to extend the wireless communication coverage by leveraging the low-loss and broad bandwidth advantages of the optical fiber. With the increasing need for wireless communications, using millimeter-waves (mm-wave) in wireless communications has become the recent trend and many attempts have been made to build high-throughput and robust mm-wave RoF systems during the past a few years. Whilst the RoF technology provides many benefits, it suffers from several fundamental limitations due to the analog optical link, including the fiber chromatic dispersion and nonlinear impairments. Various approaches have been proposed to address these limitations. In particular, machine learning (ML) algorithms have attracted intensive research attention as a promising candidate for handling the complicated physical layer impairments in RoF systems, especially the nonlinearity during signal modulation, transmission and detection. In this paper, we review recent advancements in ML techniques for RoF systems, especially those which utilize ML models as physical layer signal processors to mitigate various types of impairments and to improve the system performance. In addition, ML algorithms have also been widely adopted for highly efficient RoF network management and resource allocation, such as the dynamic bandwidth allocation and network fault detection. In this paper, we also review the recent works in these research domains. Finally, several key open questions that need to be addressed in the future and possible solutions of these questions are also discussed.

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“…The activation functions are all Selu function in the hidden layers, which can be expressed by Eq. (2) [25], [26].…”

Section: B Setting Up a Dnn Structurementioning

confidence: 99%

Deep Neural Network Based Chromatic Dispersion Estimation With Ultra-Low Sampling Rate for Optical Fiber Communication Systems

Guo¹,

Guo²,

Yang³

et al. 2019

IEEE Access

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Fast and low-cost chromatic dispersion (CD) estimation is very important for fiber link that is dynamically reconfigurable in the next-generation optical networks. In this paper, a novel CD estimation method based on the deep neural network (DNN) with ultra-low sampling rate is proposed for the optical fiber transmission systems. The method can estimate CD for under-sampled signals by the trained DNN with a weighted average. To demonstrate the feasibility of the method, the simulation of a 28-GBaud fivechannel optical fiber transmission system with 100-2000-km SSMF transmission is carried, where the sampling rate of the analog-to-digital converter (ADC) at the receiver is only 500 MHz. The results show that the maximum mean absolute error (MAE) of the estimated CD is less than 75 ps/nm with the reference cumulative CD from 1600 to 32 000 ps/nm for QPSK and 16QAM signals. Meanwhile, the robustness of the proposed method is verified for amplifier spontaneous emission (ASE) and nonlinear (NL) noise. Furthermore, we experimentally demonstrated that the maximum MAE is less than 75 ps/nm when the transmission distance varies from 100 to 800 km in 20-GBaud QPSK optical fiber transmission system. In conclusion, the proposed DNN-based CD estimation method shows great potential for the cost-effective under-sampled systems. INDEX TERMS Metrology, fiber optics, fiber nonlinear optics, chromatic dispersion.

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Enhanced Multi-Level Signal Recovery in Mobile Fronthaul Network Using DNN Decoder

Cited by 20 publications

References 11 publications

Experimental Demonstration and Performance Enhancement of 5G NR Multiband Radio over Fiber System Using Optimized Digital Predistortion

Experimental Demonstration and Performance Enhancement of 5G NR Multiband Radio over Fiber System Using Optimized Digital Predistortion

Machine Learning Techniques in Radio-over-Fiber Systems and Networks

Deep Neural Network Based Chromatic Dispersion Estimation With Ultra-Low Sampling Rate for Optical Fiber Communication Systems

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