4Gbaud PS-16QAM D-Band Fiber-Wireless Transmission over 4.6 km by Using Balance Complex-Valued NN Equalizer with Random Oversampling

Abstract

D-band (110–170 GHz) is a promising direction for the future of 6th generation mobile networks (6G) for high-speed mobile communication since it has a large available bandwidth, and it can provide a peak rate of hundreds of Gbit/s. Compared with the traditional electrical approach, photonics millimeter wave (mm-wave) generation in D-band is more practical and effectively overcomes the bottleneck of electrical devices. However, long-distance D-band wireless transmission is still limited by some key factors such as large absorption loss and nonlinear noises. Deep neural network algorithms are regarded as an important technique to model the nonlinear wireless behavior, among which the study on complex-value equalization is critical, especially in coherent detection systems. Moreover, probabilistic shaping is useful to improve the transmission capacity but also causes an imbalanced machine learning issue. In this paper, we propose a novel complex-valued neural network equalizer coupled with balanced random oversampling (ROS). Thanks to the adaptive deep learning method for probabilistic shaping-quadrature amplitude modulation (PS-QAM), we successfully realize a 135 GHz 4Gbaud PS-16QAM with a shaping entropy of 3.56 bit/symbol wireless transmission over 4.6 km. The bit error ratio (BER) of 4Gbaud PS-16QAM can be decreased to a soft-decision forward error correction (SD-FEC) with a 25% overhead of 2×10−2. Therefore, we can achieve a net rate of an 11.4 Gbit/s D-band radio-over-fiber (ROF) delivery over 4.6 km air free wireless distance.

Low-Complexity Pruned Convolutional Neural Network Based Nonlinear Equalizer in Coherent Optical Communication Systems

“…Neural network algorithms have achieved excellent performance in the nonlinear equalization of optical communication systems, attributable to their powerful nonlinear mapping capabilities for inputs and outputs. Examples include the artificial neural network (ANN) [22,23], the deep neural network (DNN) [24], the convolutional neural network (CNN) [25], the recurrent neural network (RNN) [26][27][28][29], the soft deep neural network (SDNN) [30], the sparsity learning deep neural network [31], the complex-valued neural network [32], the probabilistic neural network (PNN) [33], the echo state network [34] and Bayesian neural networks (BNN) [35]. On this basis, the neuralnetwork-aided perturbation-theory-based fiber nonlinearity compensation technique has been widely investigated and has demonstrated its effectiveness in estimating complex nonlinear distortion fields with perturbation triplets as the input features [36].…”