Abstract

The enhanced remote area communication (eRAC) scenario is an important growth point in the communication market. In some remote areas where optical fiber access cannot be realized or the laying cost is too high, fixed wireless access (FWA) is an appropriate supplementary solution for eRAC. Adopting analog radio over fiber (A-RoF) technology to implement FWA can overcome the bandwidth limitation of electronic devices and realize high-frequency carrier communication economically to achieve high-capacity wireless communication. Also, probabilistic shaping (PS) technology can be combined with A-RoF to further improve the flexibility of the network and coverage of service provision. However, in the PS-A-RoF network, the high RF power introduces more undesired nonlinear effects into the network, and it is often necessary to deploy supervised machine learning (ML) compensation modules in wireless receivers (WRs). But the module performances are affected by the uneven probability distribution of PS-QAM constellation points. In this paper, we employ the PS-A-RoF nonlinear model to theoretically investigate the correlation between the distribution of training symbols and the wireless A-RoF system’s performance. Our analysis reveals that reducing the variance of training symbol power contributes to a lower BER in the A-RoF network. We introduce a borderline random over-sampling (B-ROS) that matches with the PS-A-RoF nonlinear model, instead of the mainstream ROS, which is only at the data level. Based on the B-ROS scheme, only the minority examples below the borderline are over-sampled to reach a better variance performance. Introducing the B-ROS method into the supervised complex value nonlinear compensation module can further improve the decision accuracy of WRs with the restoration of phase information, without increasing additional computational resource consumption. The vector noise power, training symbol power variance, and noise factor metrics have been calculated to optimize the borderline value of our ML-based approach. We also present experimental data on the proof-of-concept A-RoF experiment for PS-64QAM. The results demonstrate a promising nonlinear compensation performance of the B-ROS WR, and the optimal borderline agrees well with the one deduced from the theoretical model under certain transmission conditions. Our proposed B-ROS scheme lessens the training size demand and can improve the receiver sensitivity by 0.51 dB compared to the common ML-based WR and by 0.7 dB compared to the conventional ROS scheme.

© 2024 Optica Publishing Group

Photonics-aided 335 GHz PS-64QAM wireless transmission over 200 m employing complex-valued NN classification and random sampling techniques

Tangyao Xie, Jianguo Yu, and Wen Zhou
Opt. Express 31(6) 10333-10347 (2023)

Evolutionary neuron-level transfer learning for QoT estimation in optical networks

Yuhang Zhou, Zhiqun Gu, Jiawei Zhang, and Yuefeng Ji
J. Opt. Commun. Netw. 16(4) 432-448 (2024)

Amplified radio-over-fiber system linearization using recurrent neural networks

Luiz Augusto Melo Pereira, Luciano Leonel Mendes, Carmelo José Albanez Bastos Filho, and Arismar Cerqueira Sodré
J. Opt. Commun. Netw. 15(3) 144-154 (2023)

Experimental investigation of machine-learning-based soft-failure management using the optical spectrum

Lars E. Kruse, Sebastian Kühl, Annika Dochhan, and Stephan Pachnicke
J. Opt. Commun. Netw. 16(2) 94-103 (2024)

Prediction-model-assisted reinforcement learning algorithm for handover decision-making in hybrid LiFi and WiFi networks

Dayrene Frometa Fonseca, Borja Genoves Guzman, Giovanni Luca Martena, Rui Bian, Harald Haas, and Domenico Giustiniano
J. Opt. Commun. Netw. 16(2) 159-170 (2024)