A Novel Nonlinear Equalizer for Probabilistic Shaping 64-QAM Based on Constellation Segmentation and Support Vector Machine

The probability distribution of probabilistic shaping 64 quadrature amplitude modulation (PS-64QAM) is uneven. The traditional M-ary support vector machine (SVM) algorithm is incompatible with the data set with uneven distribution. In order to solve the problem, we propose a novel nonlinear equalizer (NLE) for PS-64QAM based on constellation segmentation (CS) and SVM, called CS M-ary SVM NLE. The performance of CS M-ary SVM NLE has been demonstrated in a 120 Gb/s PS-64QAM coherent optical communication system. The experimental results show that after employing the proposed scheme, the launched optical power dynamic range (LOPDR) of PS-64QAM can be increased by 1.6 dBm compared with the situation without NLE. In addition, aided by the proposed scheme, the LOPDR of PS-64QAM is increased by 0.6 dBm than M-ary SVM NLE. Compared with employing M-ary SVM NLE and without employing NLE, when employing the proposed scheme, the Q factor is improved about 0.50 dB and 0.96 dB, respectively. The number of support vectors (SVs) and CPU running time for both NLE schemes are collected to measure the complexity of the two NLE schemes. The results show that the complexity of the proposed scheme is lower than that of the M-ary SVM scheme under the entire measured launched optical power range from −5 dBm to +5 dBm

A Novel Nonlinear Equalizer for Probabilistic Shaping 64-QAM Based on Constellation Segmentation and Support Vector Machine

The probability distribution of probabilistic shaping 64 quadrature amplitude modulation (PS-64QAM) is uneven. The traditional M-ary support vector machine (SVM) algorithm is incompatible with the data set with uneven distribution. In order to solve the problem, we propose a novel nonlinear equalizer (NLE) for PS-64QAM based on constellation segmentation (CS) and SVM, called CS M-ary SVM NLE. The performance of CS M-ary SVM NLE has been demonstrated in a 120 Gb/s PS-64QAM coherent optical communication system. The experimental results show that after employing the proposed scheme, the launched optical power dynamic range (LOPDR) of PS-64QAM can be increased by 1.6 dBm compared with the situation without NLE. In addition, aided by the proposed scheme, the LOPDR of PS-64QAM is increased by 0.6 dBm than M-ary SVM NLE. Compared with employing M-ary SVM NLE and without employing NLE, when employing the proposed scheme, the Q factor is improved about 0.50 dB and 0.96 dB, respectively. The number of support vectors (SVs) and CPU running time for both NLE schemes are collected to measure the complexity of the two NLE schemes. The results show that the complexity of the proposed scheme is lower than that of the M-ary SVM scheme under the entire measured launched optical power range from −5 dBm to +5 dBm

Machine-Learning-Aided Optical Fiber Communication System

The fiber optical network offers high speed, large bandwidth, and a high degree of reliability. However, the development of optical communication technology has hit a bottleneck due to several challenges such as energy loss, cost, and system capacity approaching the Shannon limit. As a powerful tool, machine learning technology provides a strong driving force for the development of various industries and greatly promotes the development of society. Machine learning also provides a new possible solution to achieve greater transmission capacities and longer transmission distances in optical communications. In this article, we introduce the application of machine learning in optical communication network systems. Three use cases are presented to evaluate the feasibility of our proposed architecture. In the transmission layer, the principal-component-based phase estimation algorithm is used for phase noise recovery in coherent optical systems, and the K-means algorithm is adopted to reduce the influence of nonlinear noise in probabilistic shaping systems. As for the network layer, the long short-term memory algorithm and the genetic algorithm are suitable for making traffic predictions and determining reasonable placement locations of remote radio heads in centralized radio access networks. Extensive simulations and experiments are conducted to evaluate the proposed algorithm in comparison to the state-of-the-art schemes. The results show the performance of three use cases. Machine learning algorithms applied to the transmission layer can greatly promote the performance of digital signal processing without increasing the complexity. Machine learning algorithms applied to the network layer can provide a more appropriate channel allocation plan in the era of high-speed communication. Ultimately, the intent of this article is to serve as a basis for stimulating more research in machine learning in optical communications

Blind Modulation Format Identification Based on Principal Component Analysis and Singular Value Decomposition

As optical networks evolve towards flexibility and heterogeneity, various modulation formats are used to match different bandwidth requirements and channel conditions. For correct reception and efficient compensation, modulation format identification (MFI) becomes a critical issue. Thus, a novel blind MFI method based on principal component analysis (PCA) and singular value decomposition (SVD) is proposed. Based on square operation and PCA, the influence of phase rotation is removed, which avoids phase rotation-related discussions and training. By performing SVD on the density matrix about constellation, a denoise method is implemented and the quality of the constellation is improved. In the subsequent processing, the denoised density matrix is used as the feature of the support vector machine (SVM), and the identification of seven modulation formats such as BPSK, QPSK, 8PSK, 8QAM, 16QAM, 32QAM and 64QAM is realized. The results show that lower OSNR values are required for the 100% accurate identification of all modulation formats to be achieved, which are 5 dB, 7 dB, 8 dB, 11 dB, 14 dB, 14 dB and 15 dB. Moreover, the proposed method still retains the advantage, even when the number of samples decrease, which is beneficial for low-complexity implementation

Enhancing the Anti-Dispersion Capability of the AO-OFDM System via a Well-Designed Optical Filter at the Transmitter

This paper proposes a novel method to improve the anti-dispersion ability of the all-optical orthogonal frequency division multiplexing (AO-OFDM) system. By replacing the Sinc-shaped filter with a Gauss-shaped filter for sub-carrier generation and inserting a cyclic prefix (CP), the impact of dispersion on the system can be significantly mitigated. Formula derivation and numerical analysis of the pulse-shaping function of the AO-OFDM system in the time domain for each cycle indicated that the pulse-shaping function generated by the Gauss-shaped filter was less affected by the dispersion effect than that of the Sinc-shaped filter. Meanwhile, less inter-carrier crosstalk between carriers was also observed. After carrying out system transmission simulations employing these two different filters, we found that the AO-OFDM system based on the Gauss-shaped filter could greatly improve the anti-dispersion ability compared with the system based on a Sinc-shaped filter. When the parameter settings in both schemes were identical, that is, the number of subcarriers was 32 and the power of a single subcarrier was −13 dBm, the bit error rate (BER) of the system based on the proposed Gauss-shaped filter after 60 km SMF transmission was only 1.596 × 10−3, while the BER of the traditional Sinc-shaped filter based system scheme was as high as 8.545 × 10−2

Blind Modulation Format Identification Based on Principal Component Analysis and Singular Value Decomposition

As optical networks evolve towards flexibility and heterogeneity, various modulation formats are used to match different bandwidth requirements and channel conditions. For correct reception and efficient compensation, modulation format identification (MFI) becomes a critical issue. Thus, a novel blind MFI method based on principal component analysis (PCA) and singular value decomposition (SVD) is proposed. Based on square operation and PCA, the influence of phase rotation is removed, which avoids phase rotation-related discussions and training. By performing SVD on the density matrix about constellation, a denoise method is implemented and the quality of the constellation is improved. In the subsequent processing, the denoised density matrix is used as the feature of the support vector machine (SVM), and the identification of seven modulation formats such as BPSK, QPSK, 8PSK, 8QAM, 16QAM, 32QAM and 64QAM is realized. The results show that lower OSNR values are required for the 100% accurate identification of all modulation formats to be achieved, which are 5 dB, 7 dB, 8 dB, 11 dB, 14 dB, 14 dB and 15 dB. Moreover, the proposed method still retains the advantage, even when the number of samples decrease, which is beneficial for low-complexity implementation

A Novel 64 QAM-OFDM Optical Access System Based on Bit Reconstruction

This paper proposes a novel orthogonal frequency division multiplexing (OFDM) optical access scheme based on bit reconstruction. In this method, correlation is introduced into the data information of optical line terminals (OLT) through the logical coding circuits and partition mapping. Even after passing through the optical fibre channel, the strong correlation after bit reconstruction can still be used in the optical network unit (ONU) for reliable decoding. In the simulation experiments, a 60 Gbit/s bit reconstruction 64 quadrature amplitude modulation (QAM) OFDM signal was successfully transmitted over a 10/20 km single-mode fibre (SMF). The simulation results show that the proposed scheme can effectively achieve reliable transmission with gains of about 1.3 dB and 0.51 dB at a 20% soft decision-forward error correction (SD-FEC) threshold, respectively. The proposed scheme is a promising candidate for a next-generation passive optical network (NGPON) solution

Synthesis of Fibrous Phosphorus Micropillar Arrays with Pyro‐Phototronic Effects

The bottom-up preparation of two-dimensional material micro-nano structures at scale facilitates the realisation of integrated applications in optoelectronic devices. Fibrous Phosphorus (FP), an allotrope of black phosphorus (BP), is one of the most promising candidate materials in the field of optoelectronics with its unique crystal structure and properties. [1] However, to date, there are no bottom-up micro-nano structure preparation methods for crystalline phosphorus allotropes. [1c, 2] Herein, we present the bottom-up preparation of fibrous phosphorus micropillar (FP-MP) arrays via a low-pressure gas-phase transport (LP-CVT) method that controls the directional phase transition from amorphous red phosphorus (ARP) to FP. In addition, self-powered photodetectors (PD) of FP-MP arrays with pyro-phototronic effects achieved detection beyond the band gap limit. Our results provide a new approach for bottom-up preparation of other crystalline allotropes of phosphorus

Synthesis of Fibrous Phosphorus Micropillar Arrays with Pyro‐Phototronic Effects

The bottom-up preparation of two-dimensional material micro-nano structures at scale facilitates the realisation of integrated applications in optoelectronic devices. Fibrous Phosphorus (FP), an allotrope of black phosphorus (BP), is one of the most promising candidate materials in the field of optoelectronics with its unique crystal structure and properties. However, to date, there are no bottom-up micro-nano structure preparation methods for crystalline phosphorus allotropes. Herein, we present the bottom-up preparation of fibrous phosphorus micropillar (FP-MP) arrays via a low-pressure gas-phase transport (LP-CVT) method that controls the directional phase transition from amorphous red phosphorus (ARP) to FP. In addition, self-powered photodetectors (PD) of FP-MP arrays with pyro-phototronic effects achieved detection beyond the bandgap limit. Our results provide a new approach for bottom-up preparation of other crystalline allotropes of phosphorus