Reduced-Complexity Artificial Neural Network Equalization for Ultra-High-Spectral-Efficient Optical Fast-OFDM Signals

Abstract

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.

A survey of applied machine learning techniques for optical orthogonal frequency division multiplexing based networks

“…Deep neural networks have been applied to predict unknown image transmission at the end part of MMF [48]. On the other hand, several digital signal processing (DSP) equalizers including deterministic algorithms and machine learning such as artificial neural network (ANN) [49,50], K-nearest neighbors (KNN) [39], Wiener-Hammerstein nonlinear equalizer (WH-NLE) model [51], Volterra-NLE model [52] and classification tree (CT) [53] have been proposed to compensate dispersion, nonlinearity and polarization channel effects in both singlemode fiber (SMF) and MMF/FMF using also a dual-polarization scheme with an effective 2x2MIMO at the receiver. In long-haul transmission system, another important noise effect that be tackled only with machine learning is the stochastic parametric noise amplification (PNA) which is essentially the interplay between optical amplification and nonlinearity.…”

A Performance Analysis of a Hybrid OCDMA-PON Configuration Based on IM/DD Fast-OFDM Technique for Access Network

“…Thanks to the discrete cosine transform (DCT), the Fast-OFDM technique provides twice the bandwidth efficiency of the fast Fourier transform (FFT) used by the classical OFDM technique. Furthermore, Fast-OFDM is an emergent and cost-effective technique used to reduce the system complexity, and it has been widely applied in multimode fiber (MMF) [20,21] and single mode fiber (SMF) as well [22].…”

Special Issue on “Optics for AI and AI for Optics”

“…Generating, transmitting, and recovering such high-volume data requires advanced signal processing and networking technologies with high performance and cost-and-power efficiency. AI is especially useful for optimization and performance prediction for systems that exhibit complex behaviors [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. In this aspect, traditional signal processing algorithms may not be as efficient as AI algorithms.…”

“…The Special Issue is launched to bring optics and AI together to address the challenges that each face, which are difficult to address alone. There are 12 selected contributions for the special session, representing the fascinating progress in the combined area of optics and AI, ranging from photonic neural network (NN) architecture [5] to AI-enabled advances in optical communications including both physical layer transceiver signal processing [10][11][12][13][14][15][16][17] and network layer performance monitoring [18,19], as well as the potential role of AI in quantum communications [20].…”

“…Elias Giacoumidis et al proposed a density-based spatial clustering of applications with noise (DBSCAN) algorithm to address this challenge, which shows a significant performance improvement compared with conventional K-means clustering [16]. In another work, Mutsam A. Jarajreh indicated that compared to the Volterra nonlinear equalizer, an NN was shown to be able to relax the requirement on other system parameters such as the signal quantization bits and clipping ratio, which is valuable for practical implementation [17].…”