Articoli di riviste: "Nonlinearity equalization"

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Golani, Ori, Meir Feder e Mark Shtaif. "Equalization Methods for Out-of-Band Nonlinearity Mitigation in Fiber-Optic Communications". Applied Sciences 9, n. 3 (2 febbraio 2019): 511. http://dx.doi.org/10.3390/app9030511.

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In recent years, it has been established that the adverse effects of nonlinear interference noise (NLIN) can be mitigated using adaptive equalization methods. As such, a wide variety of adaptive equalization methods have been used to treat nonlinearity, in different transmission scenarios. This paper reviews the principles of out-of-band nonlinearity mitigation using adaptive equalization. Statistical properties of NLIN that can be exploited for mitigation are discussed, as well as the cost and benefit of various types of equalizers. In particular we describe the equivalence between the NLIN and time-dependent inter-symbol-interference (ISI) and discuss ways in which the ISI coefficients can be characterized theoretically and experimentally. We further discuss the effectiveness of existing ISI mitigation algorithms, and explain the need for designing customized algorithms that take advantage of the various correlation properties characterizing the ISI coefficients. This paper is intended to be a practical reference for researchers who want to apply equalization algorithms or design new methods for nonlinearity mitigation.

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Chen, Yuanjie. "Blind equalization with criterion with memory nonlinearity". Optical Engineering 31, n. 6 (1992): 1200. http://dx.doi.org/10.1117/12.57519.

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Chen, Qianwen, Xiong Chen, David J. Pommerenke e Ming Yu. "Balanced Intermodulation Reference With Flat Frequency Response Using Nonlinearity Equalization". IEEE Transactions on Electromagnetic Compatibility 62, n. 6 (dicembre 2020): 2634–37. http://dx.doi.org/10.1109/temc.2020.2981462.

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Miao, Pu, Weibang Yin, Hui Peng e Yu Yao. "Study of the Performance of Deep Learning-Based Channel Equalization for Indoor Visible Light Communication Systems". Photonics 8, n. 10 (18 ottobre 2021): 453. http://dx.doi.org/10.3390/photonics8100453.

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The inherent impairments of visible light communication (VLC) in terms of nonlinearity of light-emitting diode (LED) and the optical multipath restrict bit error rate (BER) performance. In this paper, a model-driven deep learning (DL) equalization scheme is proposed to deal with the severe channel impairments. By imitating the block-by-block signal processing block in orthogonal frequency division multiplexing (OFDM) communication, the proposed scheme employs two subnets to replace the signal demodulation module in traditional system for learning the channel nonlinearity and the symbol de-mapping relationship from the training data. In addition, the conventional solution and algorithm are also incorporated into the system architecture to accelerate the convergence speed. After an efficient training, the distorted symbols can be implicitly equalized into the binary bits directly. The results demonstrate that the proposed scheme can address the overall channel impairments efficiently and can recover the original symbols with better BER performance. Moreover, it can still work robustly when the system is complicated by serious distortions and interference, which demonstrates the superiority and validity of the proposed scheme in channel equalization.

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Ruqi Zhang, Ruqi Zhang, Jianfeng Li Jianfeng Li, Zhitong Huang Zhitong Huang e Yuefeng Ji Yuefeng Ji. "Adaptive frequency domain pre-equalization for white-LED nonlinearity in OFDM-based visible light communication systems". Chinese Optics Letters 13, n. 7 (2015): 072302–72305. http://dx.doi.org/10.3788/col201513.072302.

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Kumar Orappanpara Soman, Sunish. "A tutorial on fiber Kerr nonlinearity effect and its compensation in optical communication systems". Journal of Optics 23, n. 12 (22 novembre 2021): 123502. http://dx.doi.org/10.1088/2040-8986/ac362a.

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Abstract The advent of silica-based low-cost standard single-mode fibers revolutionized the whole communication industry. The deployment of optical fibers in the networks induces a paradigm shift in the communication technologies used for long-haul information transfer. However, the communication using the optical fibers is affected by several linear and nonlinear effects. The most common linear effects are attenuation and chromatic dispersion, whereas the dominant nonlinear effect is the Kerr effect. The Kerr effect induces a power-dependent nonlinear distortion for the signal propagating in the optical fiber. The detrimental effects of the Kerr nonlinearity limit the capacity of long-haul optical communication systems. Fiber Kerr nonlinearity compensation using digital signal processing (DSP) techniques has been well investigated over several years. In this paper, we provide a comprehensive tutorial, including the fundamental mathematical analysis, on the characteristics of the optical fiber channel, the origin of the Kerr nonlinearity effect, the theory of the pulse propagation in the optical fiber, and the numerical and analytical tools for solving the pulse propagation equation. In addition, we provide a concise review of various DSP techniques for fiber nonlinearity compensation, such as digital back-propagation, Volterra series-based nonlinearity equalization, perturbation theory-based nonlinearity compensation, and phase conjugation. We also carry out numerical simulation and the complexity evaluation of the selected nonlinearity compensation techniques.

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Siuzdak, Jerzy. "Comparison of the Nonlinear Dynamic Pre- and Post-LED Equalization". Sensors 22, n. 5 (24 febbraio 2022): 1782. http://dx.doi.org/10.3390/s22051782.

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Visible Light Communications (VLC) have gained much popularity lately. In such a system, a white LED (Light-Emitting Diode) plays a double role as a light source and a transmitter. The main problem here is that the LED exhibits a low bandwidth and high nonlinearity, so the equalization of the LED nonlinear dynamic response is necessary. For this, various equalizers are used. This paper compares the pre- and post-equalizer performance in terms of the received signal quality for a channel that includes a nonlinear element of limited bandwidth, such as an LED. Multilevel Pulse Amplitude Modulation (PAM) was selected as the signal format, as well as a variant of the Volterra series equalizer as the compensating element. The results obtained may be used for the correction of the dynamic characteristics of LEDs applied in VLC systems. For the sake of comparison, we used Modulation Error Ratio (MER) values at the receiver output. The dynamic nonlinear behavior was modeled by a Wiener–Hammerstein device, whereas the post/pre-equalizer was based on the dynamic deviation reduction-based Volterra series. The obtained results indicate that the post- and pre-equalizer performed comparably for the linear/moderately nonlinear channels and for a high noise level. In the case of high nonlinearity and a large SNR (Signal–to–Noise Ratio) values, the post-equalizer performed somewhat better in terms of MER by a few dB at maximum.

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Scarano, Gaetano, Andrea Petroni, Mauro Biagi e Roberto Cusani. "Blind Fractionally Spaced Channel Equalization for Shallow Water PPM Digital Communications Links". Sensors 19, n. 21 (23 ottobre 2019): 4604. http://dx.doi.org/10.3390/s19214604.

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Underwater acoustic digital communications suffer from inter-symbol interference deriving from signal distortions caused by the channel propagation. Facing such kind of impairment becomes particularly challenging when dealing with shallow water scenarios characterized by short channel coherence time and large delay spread caused by time-varying multipath effects. Channel equalization operated on the received signal represents a crucial issue in order to mitigate the effect of inter-symbol interference and improve the link reliability. In this direction, this contribution presents a preliminary performance analysis of acoustic digital links adopting pulse position modulation in severe multipath scenarios. First, we show how the spectral redundancy offered by pulse position modulated signals can be fruitfully exploited when using fractional sampling at the receiver side, which is an interesting approach rarely addressed by the current literature. In this context, a novel blind equalization scheme is devised. Specifically, the equalizer is blindly designed according to a suitably modified Bussgang scheme in which the zero-memory nonlinearity is replaced by a M-memory nonlinearity, M being the pulse position modulation order. Numerical results not only confirm the feasibility of the technique described here, but also assess the quality of its performance. An extension to a very interesting complex case is also provided.

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Asif, Rameez, Rabeea Basir e Ramshah Ahmad. "Signal Processing Algorithms for Down-Stream Traffic in Next Generation 10 Gbit/s Fixed-Grid Passive Optical Networks". Advances in OptoElectronics 2014 (22 giugno 2014): 1–4. http://dx.doi.org/10.1155/2014/296781.

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We have analyzed the impact of digital and optical signal processing algorithms, that is, Volterra equalization (VE), digital backpropagation (BP), and optical phase conjugation with nonlinearity module (OPC-NM), in next generation 10 Gbit/s (also referred to as XG) DP-QPSK long haul WDM (fixed-grid) passive optical network (PON) without midspan repeaters over 120 km standard single mode fiber (SMF) link for downstream signals. Due to the compensation of optical Kerr effects, the sensitivity penalty is improved by 2 dB by implementing BP algorithm, 1.5 dB by VE algorithm, and 2.69 dB by OPC-NM. Moreover, with the implementation of NL equalization technique, we are able to get the transmission distance of 126.6 km SMF for the 1 : 1024 split ratio at 5 GHz channel spacing in the nonlinear region.

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Mauda, R., e M. Pinchas. "16QAM Blind Equalization via Maximum Entropy Density Approximation Technique and Nonlinear Lagrange Multipliers". Scientific World Journal 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/548714.

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Recently a new blind equalization method was proposed for the 16QAM constellation input inspired by the maximum entropy density approximation technique with improved equalization performance compared to the maximum entropy approach, Godard’s algorithm, and others. In addition, an approximated expression for the minimum mean square error (MSE) was obtained. The idea was to find those Lagrange multipliers that bring the approximated MSE to minimum. Since the derivation of the obtained MSE with respect to the Lagrange multipliers leads to a nonlinear equation for the Lagrange multipliers, the part in the MSE expression that caused the nonlinearity in the equation for the Lagrange multipliers was ignored. Thus, the obtained Lagrange multipliers were not those Lagrange multipliers that bring the approximated MSE to minimum. In this paper, we derive a new set of Lagrange multipliers based on the nonlinear expression for the Lagrange multipliers obtained from minimizing the approximated MSE with respect to the Lagrange multipliers. Simulation results indicate that for the high signal to noise ratio (SNR) case, a faster convergence rate is obtained for a channel causing a high initial intersymbol interference (ISI) while the same equalization performance is obtained for an easy channel (initial ISI low).

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Tian, Daming, Pu Miao, Hui Peng, Weibang Yin e Xiaorui Li. "Volterra-Aided Neural Network Equalization for Channel Impairment Compensation in Visible Light Communication System". Photonics 9, n. 11 (10 novembre 2022): 845. http://dx.doi.org/10.3390/photonics9110845.

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This paper addresses the channel impairment to enhance the system performance of visible light communication (VLC). Inspired by the model-solving procedure in the conventional equalizer, the channel impairment compensation is formulated as a spatial memory pattern prediction problem, then we propose efficient deep-learning (DL)-based nonlinear post-equalization, combining the Volterra-aided convolutional neural network (CNN) and long-short term memory (LSTM) neural network, to mitigate the system nonlinearity and then recover the original transmitted signal from the distorted one at the receiver end. The Volterra structure is employed to construct a spatial pattern that can be easily interpreted by the proposed scheme. Then, we take advantage of the CNN to extract the implicit feature of channel impairments and utilize the LSTM to predict the memory sequence. Results demonstrate that the proposed scheme can provide a fairly fast convergence during the training stage and can effectively mitigate the overall nonlinearity of the system at testing. Furthermore, it can recover the original signal accurately and exhibits an excellent bit error rate performance as compared with the conventional equalizer, demonstrating the prospect and validity of this methodology for channel impairment compensation.

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Giacoumidis, Elias, Son T. Le, Mohammad Ghanbarisabagh, Mary McCarthy, Ivan Aldaya, Sofien Mhatli, Mutsam A. Jarajreh et al. "Fiber nonlinearity-induced penalty reduction in CO-OFDM by ANN-based nonlinear equalization". Optics Letters 40, n. 21 (30 ottobre 2015): 5113. http://dx.doi.org/10.1364/ol.40.005113.

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Schaedler, Maximilian, Christian Bluemm, Maxim Kuschnerov, Fabio Pittalà, Stefano Calabrò e Stephan Pachnicke. "Deep Neural Network Equalization for Optical Short Reach Communication". Applied Sciences 9, n. 21 (2 novembre 2019): 4675. http://dx.doi.org/10.3390/app9214675.

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Nonlinear distortion has always been a challenge for optical communication due to the nonlinear transfer characteristics of the fiber itself. The next frontier for optical communication is a second type of nonlinearities, which results from optical and electrical components. They become the dominant nonlinearity for shorter reaches. The highest data rates cannot be achieved without effective compensation. A classical countermeasure is receiver-side equalization of nonlinear impairments and memory effects using Volterra series. However, such Volterra equalizers are architecturally complex and their parametrization can be numerical unstable. This contribution proposes an alternative nonlinear equalizer architecture based on machine learning. Its performance is evaluated experimentally on coherent 88 Gbaud dual polarization 16QAM 600 Gb/s back-to-back measurements. The proposed equalizers outperform Volterra and memory polynomial Volterra equalizers up to 6th orders at a target bit-error rate (BER) of 10 − 2 by 0.5 dB and 0.8 dB in optical signal-to-noise ratio (OSNR), respectively.

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ITOH, Toshihiro, Tomofumi FURUTA, Hiroyuki FUKUYAMA e Koichi MURATA. "Effects of Preamplifier Nonlinearity on PMD Equalization with Electronic Dispersion Compensation for 43G DQPSK". IEICE Transactions on Electronics E94-C, n. 7 (2011): 1187–92. http://dx.doi.org/10.1587/transele.e94.c.1187.

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Li, Jianfeng, Zhitong Huang, Xiaoshuang Liu e Yuefeng Ji. "Hybrid time-frequency domain equalization for LED nonlinearity mitigation in OFDM-based VLC systems". Optics Express 23, n. 1 (9 gennaio 2015): 611. http://dx.doi.org/10.1364/oe.23.000611.

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Кононов, Владимир, e Vladimir Kononov. "DESIGN OF HIGH-RESOLUTION (12-14 BIT) MULTI-CHIP CONVEYOR ADCS WITH 0.5-1 GHZ CONVERSION FREQUENCY". Modeling of systems and processes 12, n. 2 (24 ottobre 2019): 38–51. http://dx.doi.org/10.12737/article_5db1e3e611e279.71740920.

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Low-voltage amplifiers of telescopic type based on CMOS technology with design norms of 180 nm and 60 nm are investigated. It is established that the best characteristics of nonlinearity in the frequency range up to 250 MHz have amplifiers manufactured by technology with design standards of 180 nm. It is shown that amplifiers of the folded CAS-code type with a supply voltage of 2.5-3 V and cascade transistors with maximum permissible interelectrode voltages of 1.2-1.8 V can degrade and even catastrophically fail when exposed to TKCH. Technical solutions for calibration of bias and gain equalization of channels in the creation of 4-channel 12-14-bit ADC with conversion frequency up to 1 GHz and 0.5 GHz are considered. It is shown that the multi-chip use of this type of ADC in the form of microassemblies allows to achieve typical values of integral nonlinearity, signal-to-noise ratio and dynamic range.

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Giacoumidis, Elias, Amir Matin, Jinlong Wei, Nick J. Doran, Liam P. Barry e Xu Wang. "Blind Nonlinearity Equalization by Machine-Learning-Based Clustering for Single- and Multichannel Coherent Optical OFDM". Journal of Lightwave Technology 36, n. 3 (1 febbraio 2018): 721–27. http://dx.doi.org/10.1109/jlt.2017.2778883.

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Zhang, Junfeng, Wei Chen, Mingyi Gao e Gangxiang Shen. "K-means-clustering-based fiber nonlinearity equalization techniques for 64-QAM coherent optical communication system". Optics Express 25, n. 22 (24 ottobre 2017): 27570. http://dx.doi.org/10.1364/oe.25.027570.

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Xu, Sicong, Bohan Sang, Lingchuan Zeng e Li Zhao. "Two-Lane DNN Equalizer Using Balanced Random-Oversampling for W-Band PS-16QAM RoF Delivery over 4.6 km". Sensors 23, n. 10 (10 maggio 2023): 4618. http://dx.doi.org/10.3390/s23104618.

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For W-band long-range mm-wave wireless transmission systems, nonlinearity issues resulting from photoelectric devices, optical fibers, and wireless power amplifiers can be handled by deep learning equalization algorithms. In addition, the PS technique is considered an effective measure to further increase the capacity of the modulation-constraint channel. However, since the probabilistic distribution of m-QAM varies with the amplitude, there have been difficulties in learning valuable information from the minority class. This limits the benefit of nonlinear equalization. To overcome the imbalanced machine learning problem, we propose a novel two-lane DNN (TLD) equalizer using the random oversampling (ROS) technique in this paper. The combination of PS at the transmitter and ROS at the receiver improved the overall performance of the W-band wireless transmission system, and our 4.6-km ROF delivery experiment verified its effectiveness for the W-band mm-wave PS-16QAM system. Based on our proposed equalization scheme, we achieved single-channel 10-Gbaud W-band PS-16QAM wireless transmission over a 100 m optical fiber link and a 4.6 km wireless air-free distance. The results show that compared with the typical TLD without ROS, the TLD-ROS can improve the receiver‘s sensitivity by 1 dB. Furthermore, a reduction of 45.6% in complexity was achieved, and we were able to reduce training samples by 15.5%. Considering the actual wireless physical layer and its requirements, there is much to be gained from the joint use of deep learning and balanced data pre-processing techniques.

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Fan, Xin, Junyan Wang, Haifeng Wang e Changgao Xia. "Contrast-Controllable Image Enhancement Based on Limited Histogram". Electronics 11, n. 22 (21 novembre 2022): 3822. http://dx.doi.org/10.3390/electronics11223822.

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To address the technical shortcomings of conventional histogram equalization (HE), such as over-enhancement and artifacts, we propose a histogram-constrained and contrast-tunable HE technique for digital image enhancement. Firstly, the input image histogram is partitioned into two parts, the main histogram and the constrained histogram, by a cumulative probability density threshold; second, the main histogram is redistributed equally in the whole grayscale range; and finally, the nonlinearity of the constrained histogram is mapped to the main histogram. The experimental averages show that the values of the two metrics, information entropy and MS-SSIM, processed by the algorithms in this paper, are more accurate compared to the other six excellent algorithms.

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Huang, Yi, Aiqun Hu, Jiayi Fan, Huifeng Tian, Xuebao Li e Yanfang Zheng. "An Efficient Transmitter Feature Extraction Scheme with IQ Imbalance and Nonlinearity in TDD OFDM Systems". Electronics 12, n. 19 (30 settembre 2023): 4108. http://dx.doi.org/10.3390/electronics12194108.

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Radio frequency (RF) fingerprints have been an emerging research topic for the last decade. Numerous algorithms for recognition have been proposed. However, very few algorithms for the accurate extraction of IQI and PA nonlinearity are available, especially when multiple paths are considered. In this study, we present a scheme that uses the transmitter in-phase/quadrature-phase imbalance (IQI) and the power amplifier (PA) nonlinearity as RF fingerprint features in time-division duplexing (TDD) OFDM systems, which are always considered to be harmful to data transmission. The scheme consists of two round trips with four steps for two cases: in the first, the IQI and PA nonlinearity are unknown at the terminal; in the second, they are known at the terminal. A channel state information (CSI)-tracking algorithm based on the sliding-window least squares method is first adopted at the terminal. In case A, the obtained CSI is sent to the base station (BS) to remove its impact there; in case B, this removal is conducted directly by using pre-equalization at the terminal. Then, by following a sequential iterative approach, the IQI and nonlinearity are individually calculated. Theoretical analyses reveal how CSI estimation errors influence subsequent estimates at the BS in these two cases. Furthermore, the approximate unbiasedness is verified. The theoretical variance and Cramer–Rao lower bound (CRLB) are also given. It is indicated that the theoretical minimum variance in case B is lower than that in case A from the perspective of the CRLB. The numerical results demonstrate the efficiency of the scheme in comparison with existing techniques in the literature.

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Jin, Cenqin, Nikita A. Shevchenko, Junqiu Wang, Yunfei Chen e Tianhua Xu. "Wideband Multichannel Nyquist-Spaced Long-Haul Optical Transmission Influenced by Enhanced Equalization Phase Noise". Sensors 23, n. 3 (29 gennaio 2023): 1493. http://dx.doi.org/10.3390/s23031493.

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Enhanced equalization phase noise (EEPN), generated from the uncompensated dispersion experienced by laser phase noises, can cause serious damage to the transmission quality of optical fiber systems. In this work, the performance of a wideband Nyquist-spaced long-haul nonlinear optical fiber communication systems suffering from EEPN is investigated and discussed through split-step numerical simulations and analytical models based on the perturbation analysis, in the cases of digital nonlinearity compensation (NLC) and electronic dispersion compensation (EDC). The efficiency and the accuracy of the analytical models were validated via simulations, considering the different symbol rates and modulation formats. The performance of the C-band transmission was comprehensively studied based on the model. Our results reveal that the growth of symbol rates and transmission distances aggravates the distortions in the C-band system.

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Li, Yajie, Shoudong Liu, Yongli Zhao, Chao Lei e Jie Zhang. "Blind nonlinearity equalization by machine-learning-based clustering for QAM-based quantum noise stream cipher transmission". China Communications 19, n. 8 (agosto 2022): 127–37. http://dx.doi.org/10.23919/jcc.2022.08.010.

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Ding, Jiazheng, Tianhua Xu, Cenqin Jin, Ziyihui Wang, Jian Zhao e Tiegen Liu. "Impact of Equalization-Enhanced Phase Noise on Digital Nonlinearity Compensation in High-Capacity Optical Communication Systems". Sensors 20, n. 15 (26 luglio 2020): 4149. http://dx.doi.org/10.3390/s20154149.

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Equalization-enhanced phase noise (EEPN) can severely degrade the performance of long-haul optical fiber transmission systems. In this paper, the impact of EEPN in Nyquist-spaced dual-polarization quadrature phase shift keying (DP-QPSK), dual-polarization 16-ary quadrature amplitude modulation (DP-16QAM), and DP-64QAM optical transmission systems is investigated considering the use of electrical dispersion compensation (EDC) and multi-channel digital backpropagation (MC-DBP). Our results demonstrate that full-field DBP (FF-DBP) is more susceptible to EEPN compared to single-channel and partial-bandwidth DBP. EEPN-induced distortions become more significant with the increase of the local oscillator (LO) laser linewidth, and this results in degradations in bit-error-rates (BERs), achievable information rates (AIRs), and AIR-distance products in optical communication systems. Transmission systems using higher-order modulation formats can enhance information rates and spectral efficiencies, but will be more seriously degraded by EEPN. It is found that degradations on AIRs, for the investigated FF-DBP schemes, in the DP-QPSK, the DP-16QAM, and the DP-64QAM systems are 0.07 Tbit/s, 0.11 Tbit/s, and 0.57 Tbit/s, respectively, due to the EEPN with an LO laser linewidth of 1 MHz. It is also seen that the selection of a higher-quality LO laser can significantly reduce the bandwidth requirement and the computational complexity in the MC-DBP.

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Guo, Hong, Dan Dan Han e Hong Guo Zhang. "Design and Implementation of High-Precision Digital Audio Equalizer Based on FPGA". Applied Mechanics and Materials 427-429 (settembre 2013): 1187–90. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1187.

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By adjusting a variety of different frequency signals, equalizer compensates or optimizes system deficiencies, equalizer is used in voice, communication systems, mechanical vibration, fault diagnosis and many other fields. Traditional analog equalizer has low precision, phase nonlinearity and more distortion characteristics. On the basis of this, a new digital equalization algorithm that abandon the traditional single FIR filter implementation method is proposed, frequency division effect is achieved by multiple phase sub-band filter row and multi-channel digital potentiometer, so the cost of digital equalizer design and difficulty of implementation are reduced, and flexibility of digital equalizer settings on the real-time system is improved. In this paper, QuartusII is applied to functional simulation, and downloaded into the FPGA via JTAG interface to verify the correctness of the results.

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You, Yue, Wenjia Zhang, Lin Sun, Jiangbing Du, Chenyu Liang, Fan Yang e Zuyuan He. "Time skewing and amplitude nonlinearity mitigation by feedback equalization for 56 Gbps VCSEL-based PAM-4 links". Optics Communications 410 (marzo 2018): 909–15. http://dx.doi.org/10.1016/j.optcom.2017.11.051.

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A. Jarajreh, Mutsam. "Reduced-Complexity Artificial Neural Network Equalization for Ultra-High-Spectral-Efficient Optical Fast-OFDM Signals". Applied Sciences 9, n. 19 (27 settembre 2019): 4038. http://dx.doi.org/10.3390/app9194038.

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Digital-based artificial neural network (ANN) machine learning is harnessed to reduce fiber nonlinearities, for the first time in ultra-spectrally-efficient optical fast orthogonal frequency division multiplexed (Fast-OFDM) signals. The proposed ANN design is of low computational load and is compared to the benchmark inverse Volterra-series transfer function (IVSTF)-based nonlinearity compensator. The two aforementioned schemes are compared for long-haul single-mode-fiber-based links at 9.69 Gb/s direct-detected optical Fast-OFDM signals. It is shown that an 80 km extension in transmission-reach is feasible when using ANN compared to IVSTF. This occurs because ANN can tackle stochastic nonlinear impairments, such as parametric noise amplification. Using ANN, the dynamic parameters requirements of the sub-ranging quantizers can also be relaxed compared to linear equalization, such as the reduction of the optimum clipping ratio and quantization bits by 2 dB and 2-bits, respectively, and by 2 dB and 2 bits when compared to the IVTSF equalizer.

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Cao, Bingyao, Kechen Yuan, Hu Li, Shuaihang Duan, Yuwen Li e Yuanjiang Ouyang. "The Performance Improvement of VLC-OFDM System Based on Reservoir Computing". Photonics 9, n. 3 (14 marzo 2022): 185. http://dx.doi.org/10.3390/photonics9030185.

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Nonlinear effects have been restricting the development of high-speed visible light communication (VLC) systems. Neural network (NN) has become an effective means to alleviate the nonlinearity of a VLC system due to its powerful ability to fit complicated functions. However, the complex training process of traditional NN limits its application in high-speed VLC. Without performance penalty, reservoir computing (RC) simplifies the training process of NN by training only part of the network connection weights, and has become an alternative scheme to NN. For the indoor visible light orthogonal frequency division multiplexing (VLC-OFDM) system, this paper studies the signal recovery effect of the pilot-assisted reservoir computing (PA-RC) frequency domain equalization algorithm. The pilot information is added to the feature engineering of RC to improve the accuracy of channel estimation by traditional least squares (LS) algorithm. The performance of 64 quadrature amplitude modulation (QAM) signal under different transmission rates and peak to peak voltage (Vpp) conditions is demonstrated in the experiments. Compared with the traditional frequency domain equalization algorithms, PA-RC can further expand the Vpp range that meets the 7% hard-decision forward error correction (FEC) limit of 3.8 × 10−3. At the rate of 240 Mbps, the BER of the system is reduced by about 90%, and the utilization rate of the available frequency band of the system reaches 100%. The results show that PA-RC can effectively improve the transmission performance of VLC system well, and has strong generalization ability.

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Yang, Zhiqun, Wenbo Yu, Guanju Peng, Yaping Liu e Lin Zhang. "Recent Progress on Novel DSP Techniques for Mode Division Multiplexing Systems: A Review". Applied Sciences 11, n. 4 (3 febbraio 2021): 1363. http://dx.doi.org/10.3390/app11041363.

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This paper provides an overview of latest progress on the novel advanced digital signal processing (DSP) techniques for long-haul mode division multiplexing (MDM) systems with high capacity. Space-division multiplexing (SDM) techniques have been developed for a period to increase the capacity of optical communication system by at least one order of magnitude through MDM techniques using few-mode fibers (FMFs) or multi-core multiplexing (MCM) using multi-core fibers (MCFs). The signals in MDM links are mainly impaired by the linear and nonlinear effects in FMFs, making DSP techniques become necessary to undo these impairments. In this paper, we not only review the advanced multiple-input multiple-output (MIMO) DSP techniques for compensating linear impairments in FMFs, but also enclose the state of the art of novel DSP techniques to deal with nonlinear effects. Firstly, we introduce the MIMO schemes for equalizing modal crosstalk and modal dispersion. Then, we focus on the fast tracking of time-varying (TV) channels in FMF links through frequency-domain (FD) recursive least square (RLS) algorithm. Besides, we also cover the mainstream DSP solutions for mode-dependent loss (MDL) and several possible methods to compensate nonlinearity in FMF. Moreover, artificial intelligence (AI) technologies are also discussed for its high nonlinearity tolerance and may bring a revolution in MDM systems on the process of channel equalization, link monitoring, etc. In the end, a brief conclusion and perspective will be provided.

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Zhao, Weikang, Qinghua Guo, Jun Tong, Jiangtao Xi, Yanguang Yu e Pingjuan Niu. "Frequency Domain Equalization and Post Distortion for LED Communications With Orthogonal Polynomial Based Joint LED Nonlinearity and Channel Estimation". IEEE Photonics Journal 10, n. 4 (agosto 2018): 1–11. http://dx.doi.org/10.1109/jphot.2018.2848655.

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Zhang, Yuchen, Xue Chen, Tao Yang, Jialin You, Guiqing Sun, Zhiyuan Ji e Yan Zhao. "A Chromatic Dispersion-Tolerant Frequency Offset Estimation Algorithm Based on Pilot Tone for Digital Subcarrier Multiplexing Systems". Photonics 11, n. 2 (27 gennaio 2024): 118. http://dx.doi.org/10.3390/photonics11020118.

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A digital subcarrier multiplexing (DSCM) system has been proposed as a possible solution for large capacity and long-distance coherent optical transmissions due to its high tolerances for chromatic dispersion (CD), equalization-enhanced phase noise (EEPN) and fiber nonlinearity. In a DSCM receiver, for subcarrier-demultiplex to occur properly, frequency offset estimation (FOE) must be implemented before demultiplexing. It is beneficial to decrease complexity and EEPN by compensating CD on each subcarrier. Therefore, a high CD tolerance is indispensable for the FOE algorithm in a DSCM receiver. However, the mainstream blind FOE algorithms for single-carrier systems, such as the 4th power fast Fourier transform algorithm, could not work for DSCM systems. To deal with this challenge, a pilot tone-based FOE algorithm with high CD tolerance is proposed and verified using simulations and offline experiments. The final estimation accuracy of about 10 MHz of the proposed two-stage FOE is achieved at low computational complexity. Simulations and offline experiments show that DSCM systems with the proposed algorithm have a 0.5~1 dB Q-factor improvement over Nyquist single-carrier systems.

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Zhao, Jian, Yaping Liu e Tianhua Xu. "Advanced DSP for Coherent Optical Fiber Communication". Applied Sciences 9, n. 19 (8 ottobre 2019): 4192. http://dx.doi.org/10.3390/app9194192.

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In this paper, we provide an overview of recent progress on advanced digital signal processing (DSP) techniques for high-capacity long-haul coherent optical fiber transmission systems. Not only the linear impairments existing in optical transmission links need to be compensated, but also, the nonlinear impairments require proper algorithms for mitigation because they become major limiting factors for long-haul large-capacity optical transmission systems. Besides the time domain equalization (TDE), the frequency domain equalization (FDE) DSP also provides a similar performance, with a much-reduced computational complexity. Advanced DSP also plays an important role for the realization of space division multiplexing (SDM). SDM techniques have been developed recently to enhance the system capacity by at least one order of magnitude. Some impressive results have been reported and have outperformed the nonlinear Shannon limit of the single-mode fiber (SMF). SDM introduces the space dimension to the optical fiber communication. The few-mode fiber (FMF) and multi-core fiber (MCF) have been manufactured for novel multiplexing techniques such as mode-division multiplexing (MDM) and multi-core multiplexing (MCM). Each mode or core can be considered as an independent degree of freedom, but unfortunately, signals will suffer serious coupling during the propagation. Multi-input–multi-output (MIMO) DSP can equalize the signal coupling and makes SDM transmission feasible. The machine learning (ML) technique has attracted worldwide attention and has been explored for advanced DSP. In this paper, we firstly introduce the principle and scheme of coherent detection to explain why the DSP techniques can compensate for transmission impairments. Then corresponding technologies related to the DSP, such as nonlinearity compensation, FDE, SDM and ML will be discussed. Relevant techniques will be analyzed, and representational results and experimental verifications will be demonstrated. In the end, a brief conclusion and perspective will be provided.

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Niu, Wenqing, Jifan Cai, Zhiteng Luo, Jianyang Shi e Nan Chi. "Support Vector Machine-Based Soft Decision for Consecutive-Symbol-Expanded 4-Dimensional Constellation in Underwater Visible Light Communication System". Photonics 9, n. 11 (26 ottobre 2022): 804. http://dx.doi.org/10.3390/photonics9110804.

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Nowadays, underwater visible light communication (UVLC) has become one of the key technologies for high-speed underwater wireless communication. Because of the limited modulation bandwidth and nonlinearity of the optoelectronic devices in the UVLC system, the combination of inter-symbol interference and nonlinear impairment will inevitably degrade the transmission performance. Advanced digital signal processing methods including equalization and decoding are required. In the past few years, Support vector machine (SVM) has been widely investigated in quadrature amplitude modulation (QAM) for soft decision in the decoding process. However, previous works only consider 2-dimensional (2-D) separate symbol, ignoring the correlation between consecutive symbols. In this paper, we propose to use SVM for soft decision with a 4-dimensional (4-D) constellation by concatenating two consecutive symbols. To deal with the increasing computational complexity in the SVM training phase, bit-based binary SVM multi-class strategy and an edge-detection-based data pre-processing method are employed. In this paper, we demonstrate a carrierless amplitude and phase (CAP) 16-QAM UVLC system. Experimental results indicate that the performance is greatly improved when using consecutive-symbol-expanded 4-D constellation with SVM for soft decision.

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Xu, Zengyi, Jianyang Shi, Wenqing Niu, Guojin Qin, Ruizhe Jin, Zhixue He e Nan Chi. "Transfer Learning Strategy in Neural Network Application for Underwater Visible Light Communication System". Sensors 22, n. 24 (17 dicembre 2022): 9969. http://dx.doi.org/10.3390/s22249969.

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Post-equalization using neural network (NN) is a promising technique that models and offsets the nonlinear distortion in visible light communication (VLC) channels, which is recognized as an essential component in the incoming 6G era. NN post-equalizer is good at modeling complex channel effects without previously knowing the law of physics during the transmission. However, the trained NN might be weak in generalization, and thus consumes considerable computation in retraining new models for different channel conditions. In this paper, we studied transfer learning strategy, growing DNN models from a well-trained ‘stem model’ instead of exhaustively training multiple models from randomly initialized states. It extracts the main feature of the channel first whose signal power balances the signal-to-noise ratio and the nonlinearity, and later focuses on the detailed difference in other channel conditions. Compared with the exhaustive training strategy, stem-originated DNN models achieve 64% of the working range with five times the training efficiency at most or more than 95% of the working range with 150% higher efficiency. This finding is beneficial to improving the feasibility of DNN application in real-world UVLC systems.

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Joseph, Nisha Mary, e Puttamadappa C. "Highly Accurate Technique for CO-OFDM Channel Estimation Technique Using Extreme Learning Machine (ELM)". WSEAS TRANSACTIONS ON ELECTRONICS 14 (9 marzo 2023): 7–23. http://dx.doi.org/10.37394/232017.2023.14.2.

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In wireless systems, channel estimation is considered a problematic technology, due to the fact of the difference in time between wireless channels and the noise effect. Orthogonal frequency-division multiplexing (OFDM) is a promising candidate for future optical communications and has received wide concern. The article proposed a Coherent Optical (CO) orthogonal frequency division multiplexing (OFDM) scheme, which gives a scalable and flexible solution for increasing the transmission rate, being extremely robust to chromatic dispersion as well as polarization mode dispersion. Nevertheless, both coherent detection and OFDM are prone to phase noise due to the phase mismatch between the laser oscillators at the transmitter and receiver sides and the relatively long OFDM symbol duration compared to that of single carrier communications. An Extreme Learning Machine (ELM) with Pilot Assisted Equalization (PEM) is proposed for compensation of impairments caused by fibre nonlinearity in coherent optical communication systems. Channel estimation using ELM and the value of distortion is sent to the OSTBC receiving end based on the distortion information the data is decoded and pilot data is removed. FFT is applied to the data and QPSK demodulation is done in the data to get its original form. In addition, the article utilized a free-space optical communication system of multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) with a modified receiver structure. Simulation reveals that the proposed model exhibits significant BER (0.0112) performance and provides better spectral efficiency as compared with conventional systems and less computational complexity. This suggested that the proposed method shows better performance by using the CO-OFDM-FSO-MIMO-ELM-based channel estimation technique for high-speed data communication networks in real-time scenarios respectively.

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Chengqi Zhang*, Ling Guan** e Zheru Chi. "Introduction to the Special Issue on Learning in Intelligent Algorithms and Systems Design". Journal of Advanced Computational Intelligence and Intelligent Informatics 3, n. 6 (20 dicembre 1999): 439–40. http://dx.doi.org/10.20965/jaciii.1999.p0439.

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Learning has long been and will continue to be a key issue in intelligent algorithms and systems design. Emulating the behavior and mechanisms of human learning by machines at such high levels as symbolic processing and such low levels as neuronal processing has long been a dominant interest among researchers worldwide. Neural networks, fuzzy logic, and evolutionary algorithms represent the three most active research areas. With advanced theoretical studies and computer technology, many promising algorithms and systems using these techniques have been designed and implemented for a wide range of applications. This Special Issue presents seven papers on learning in intelligent algorithms and systems design from researchers in Japan, China, Australia, and the U.S. Neural Networks: Emulating low-level human intelligent processing, or neuronal processing, gave birth of artificial neural networks more than five decades ago. It was hoped that devices based on biological neural networks would possess characteristics of the human brain. Neural networks have reattracted researchers' attention since the late 1980s when back-propagation algorithms were used to train multilayer feed-forward neural networks. In the last decades, we have seen promising progress in this research field yield many new models, learning algorithms, and real-world applications, evidenced by the publication of new journals in this field. Fuzzy Logic: Since L. A. Zadeh introduced fuzzy set theory in 1965, fuzzy logic has increasingly become the focus of many researchers and engineers opening up new research and problem solving. Fuzzy set theory has been favorably applied to control system design. In the last few years, fuzzy model applications have bloomed in image processing and pattern recognition. Evolutionary Algorithms: Evolutionary optimization algorithms have been studied over three decades, emulating natural evolutionary search and selection so powerful in global optimization. The study of evolutionary algorithms includes evolutionary programming (EP), evolutionary strategies (ESs), genetic algorithms (GAs), and genetic programming (GP). In the last few years, we have also seen multiple computational algorithms combined to maximize system performance, such as neurofuzzy networks, fuzzy neural networks, fuzzy logic and genetic optimization, neural networks, and evolutionary algorithms. This Special Issue also includes papers that introduce combined techniques. Wang et al present an improved fuzzy algorithm for enhanced eyeground images. Examination of the eyeground image is effective in diagnosing glaucoma and diabetes. Conventional eyeground image quality is usually too poor for doctors to obtain useful information, so enhancement is required to eliminate this. Due to details and uncertainties in eyeground images, conventional enhancement such as histogram equalization, edge enhancement, and high-pass filters fail to achieve good results. Fuzzy enhancement enhances images in three steps: (1) transferring an image from the spatial domain to the fuzzy domain; (2) conducting enhancement in the fuzzy domain; and (3) returning the image from the fuzzy domain to the spatial domain. The paper detailing this proposes improved mapping and fast implementation. Mohammadian presents a method for designing self-learning hierarchical fuzzy logic control systems based on the integration of evolutionary algorithms and fuzzy logic. The purpose of such an approach is to provide an integrated knowledge base for intelligent control and collision avoidance in a multirobot system. Evolutionary algorithms are used as in adaptation for learning fuzzy knowledge bases of control systems and learning, mapping, and interaction between fuzzy knowledge bases of different fuzzy logic systems. Fuzzy integral has been found useful in data fusion. Pham and Wagner present an approach based on the fuzzy integral and GAs to combine likelihood values of cohort speakers. The fuzzy integral nonlinearly fuses similarity measures of an utterance assigned to cohort speakers. In their approach, Gas find optimal fuzzy densities required for fuzzy fusion. Experiments using commercial speech corpus T146 show their approach achieves more favorable performance than conventional normalization. Evolution reflects the behavior of a society. Puppala and Sen present a coevolutionary approach to generating behavioral strategies for cooperating agent groups. Agent behavior evolves via GAs, where one genetic algorithm population is evolved per individual in the cooperative group. Groups are evaluated by pairing strategies from each population and best strategy pairs are stored together in shared memory. The approach is evaluated using asymmetric room painting and results demonstrate the superiority of shared memory over random pairing in consistently generating optimal behavior patterns. Object representation and template optimization are two main factors affecting object recognition performance. Lu et al present an evolutionary algorithm for optimizing handwritten numeral templates represented by rational B-spline surfaces of character foreground-background-distance distribution maps. Initial templates are extracted from training a feed-forward neural network instead of using arbitrarily chosen patterns to reduce iterations required in evolutionary optimization. To further reduce computational complexity, a fast search is used in selection. Using 1,000 optimized numeral templates, the classifier achieves a classification rate of 96.4% while rejecting 90.7% of nonnumeral patterns when tested on NIST Special Database 3. Determining an appropriate number of clusters is difficult yet important. Li et al based their approach based on rival penalized competitive learning (RPCL), addressing problems of overlapped clusters and dependent components of input vectors by incorporating full covariance matrices into the original RPCL algorithm. The resulting learning algorithm progressively eliminates units whose clusters contain only a small amount of training data. The algorithm is applied to determine the number of clusters in a Gaussian mixture distribution and to optimize the architecture of elliptical function networks for speaker verification and for vowel classification. Another important issue on learning is Kurihara and Sugawara's adaptive reinforcement learning algorithm integrating exploitation- and exploration-oriented learning. This algorithm is more robust in dynamically changing, large-scale environments, providing better performance than either exploitation- learning or exploration-oriented learning, making it is well suited for autonomous systems. In closing we would like to thank the authors who have submitted papers to this Special Issue and express our appreciation to the referees for their excellent work in reading papers under a tight schedule.

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Portodasilva, Edson, e Metodi Plamenov Yankov. "Adaptive Turbo Equalization for Nonlinearity Compensation in WDM Systems". Journal of Lightwave Technology, 2021, 1. http://dx.doi.org/10.1109/jlt.2021.3111095.

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Koike-Akino, Toshiaki, Ye Wang, David Millar, Keisuke Kojima e Kieran Parsons. "Neural Turbo Equalization: Deep Learning for Fiber-Optic Nonlinearity Compensation". Journal of Lightwave Technology, 2020, 1. http://dx.doi.org/10.1109/jlt.2020.2976479.

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Pinter, Stephen Z., e Xavier N. Fernando. "Equalization of Multiuser Wireless CDMA Downlink Considering Transmitter Nonlinearity Using Walsh Codes". EURASIP Journal on Wireless Communications and Networking 2007, n. 1 (21 febbraio 2007). http://dx.doi.org/10.1155/2007/49525.

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Tawade, Laxman, Umesh Pinjarkar, Kavita Awade, Abida Bapu Aboobacker, Manisha Gosavi e Yogeshwari Bhatlawande. "An Optical OFDM Modem with Adaptive Volterra Equalizer". Journal of Optical Communications 36, n. 1 (1 gennaio 2015). http://dx.doi.org/10.1515/joc-2014-0043.

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AbstractIt addresses orthogonal frequency division multiplexing (OFDM) transmission over optical links with high spectral efficiency, i.e. by using high-order quadrature amplitude modulation (QAM) schemes as a mapping method prior to the OFDM multicarrier representation. Here we address especially coherent optical OFDM modem in long distance which is affected by nonlinear distortion caused by fiber nonlinearity. Fiber nonlinearity is a majo performance-limiting factor in advanced optical communication systems. We proposed a nonlinear electrical equalization scheme based on the Volterra model. To compare with other popular linear compensation technique such as the least mean square (LMS), simulation results are presented to demonstrate the capability of a Volterra model based electrical equalizer used in a coherent optical orthogonal frequency division multiplexing system. It is shown that the Volterra model based equalizer can significantly reduce nonlinear distortion.

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Masaad, Sarah, Emmanuel Gooskens, Stijn Sackesyn, Joni Dambre e Peter Bienstman. "Photonic reservoir computing for nonlinear equalization of 64-QAM signals with a Kramers–Kronig receiver". Nanophotonics, 19 ottobre 2022. http://dx.doi.org/10.1515/nanoph-2022-0426.

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Abstract Photonic reservoirs are machine learning based systems that boast energy efficiency and speediness. Thus they can be deployed as optical processors in fiber communication systems to aid or replace digital signal equalization. In this paper, we simulate the use of a passive photonic reservoir to target nonlinearity-induced errors originating from self-phase modulation in the fiber and from the nonlinear response of the modulator. A 64-level quadrature-amplitude modulated signal is directly detected using the recently proposed Kramers–Kronig (KK) receiver. We train the readout weights by backpropagating through the receiver pipeline, thereby providing extra nonlinearity. Statistically computed bit error rates for fiber lengths of up to 100 km fall below 1 × 10−3 bit error rate, outperforming an optical feed-forward equalizer as a linear benchmark. This can find applications in inter-datacenter communications that benefit from the hardware simplicity of a KK receiver and the low power and low latency processing of a photonic reservoir.

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Li, Dongjie, Mingrui Wang, Yu Zhang e Changhe Zhai. "Application of an improved VGG and RPN network in precision parts recognition". Journal of Intelligent & Fuzzy Systems, 21 luglio 2023, 1–17. http://dx.doi.org/10.3233/jifs-231730.

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Although various automatic or semi-automatic recognition algorithms have been proposed for tiny part recognition, most of them are limited to expert knowledge base-based target recognition techniques, which have high false detection rates, low recognition accuracy and low efficiency, which largely limit the quality as well as efficiency of tiny part assembly. Therefore, this paper proposes a precision part image preprocessing method based on histogram equalization algorithm and an improved convolutional neural network (i.e. Region Proposal Network(RPN), Visual Geometry Group(VGG)) model for precision recognition of tiny parts. Firstly, the image is restricted to adaptive histogram equalization for the problem of poor contrast between part features and the image background. Second, a custom central loss function is added to the recommended frame extraction RPN network to reduce problems such as excessive intra-class spacing during classification. Finally, the local response normalization function is added after the nonlinear activation function and pooling layer in the VGG network, and the original activation function is replaced by the Relu function to overcome the problems such as high nonlinearity and serious overfitting of the original model. Experiments show that the improved VGG model achieves 95.8% accuracy in precision part recognition and has a faster recognition speed than most existing convolutional networks trained on the same test set.

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Salama, Gerges M., Amira A. Mohamed e Haitham F. Abdalla. "Evaluating DNN and LSTM nonlinear compensators for enhanced performance in DCO-OFDM system". Journal of Optical Communications, 31 gennaio 2024. http://dx.doi.org/10.1515/joc-2023-0392.

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Abstract This study proposes a deep neural network (DNN) and long-short-term memory (LSTM) nonlinear compensators method for direct current (DC)-biased optical orthogonal frequency division multiplexing (DCO-OFDM) in indoor visible light communication (VLC) conventional to handle the nonlinearity and retrieve the high-fidelity signals, and compared in terms of performance and complexity. Unlike the data training after fast Fourier transform in existing deep neural network schemes, this study proposes a scheme that uses the time domain waveform data output by photodiodes for direct equalization. The OFDM signal at the receiving end is equalized, which can mitigate hybrid linear and nonlinear impairments and save spectrum resources without requiring the pilots’ assistance. Compared with conventional receivers based on different guide frequencies and existing DL-based reception methods, the proposed adaptive receiver approach yields better bit error rate performance at different signal-to-noise ratios. This research reveals the extreme sensitivity of the LSTM’s performance to system SNR. LSTM outperforms DNN in high signal-to-noise ratio (SNR) situations, but at low SNR, even with high complexity, LSTM falls short of DNN’s performance.

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