Journal of information and communication convergence engineering

  • 제16권1호
  • /
  • Pages.1-5
  • /
  • 2018
  • /
  • 2234-8255(pISSN)
  • /
  • 2234-8883(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
권호 표지
DOI QR코드

DOI QR Code

Nonlinear Compensation Using Artificial Neural Network in Radio-over-Fiber System

  • 투고 : 2017.04.29
  • 심사 : 2018.01.02
  • 발행 : 2018.03.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In radio-over-fiber (RoF) systems, nonlinear compensation is very important to meet the error vector magnitude (EVM) requirement of the mobile network standards. In this study, a nonlinear compensation technique based on an artificial neural network (ANN) is proposed for RoF systems. This technique is based on a backpropagation neural network (BPNN) with one hidden layer and three neuron units in this study. The BPNN obtains the inverse response of the system to compensate for nonlinearities. The EVM of the signal is measured by changing the number of neurons and the hidden layers in a RoF system modeled by a measured data. Based on our simulation results, it is concluded that one hidden layer and three neuron units are adequate for the RoF system. Our results showed that the EVMs were improved from 4.027% to 2.605% by using the proposed ANN compensator.

키워드

E1ICAW_2018_v16n1_1_f0001.png 이미지

Fig. 1. The neural network: (a) biological neuron and (b) its computational

E1ICAW_2018_v16n1_1_f0002.png 이미지

Fig. 2. Structure of an ANN.

E1ICAW_2018_v16n1_1_f0003.png 이미지

Fig. 3. Simulation setup for the ANN compensator in the RoF system.

E1ICAW_2018_v16n1_1_f0004.png 이미지

Fig. 4. Signal constellation (a) without and (b) with the ANN compensator.

Table 1. EVM results according to number of hidden layer neural units

E1ICAW_2018_v16n1_1_t0001.png 이미지

Table 2. System EVM with and without the ANN compensator

E1ICAW_2018_v16n1_1_t0002.png 이미지

참고문헌

  1. T. Matsumoto, M. Koga, K. Noguchi, and S. Aizawa, "Proposal for neural-network applications to fiber-optic transmission," in Proceedings of the International Joint Conference on Neural Networks, San Diego, CA, pp. 75-80, 1990. DOI: 10.1109/IJCNN.1990.137549.
  2. T. F. B. de Sousa and M. A. C. Fernandes, "Multilayer perceptron equalizer for optical communication systems," in Proceedings of the 2013 SBMO/IEEE MTT-S International Microwave & Optoelectronics Conference, Rio de Janeiro, Brazil, pp. 1-5, 2013. DOI: 10.1109/IMOC.2013.6646479.
  3. R. Abdolee, R. Ngah, V. Vakilian, and T. A. Rahman, "Application of radio-over-fiber (ROF) in mobile communication," in Proceedings of the Asia-Pacific Conference on Applied Electromagnetics, Melaka, Malaysia, pp. 1-5, 2007. DOI: 10.1109/APACE.2007.4603945.
  4. M. Xu, F. Lu, J. Wang, L. Cheng, D. Guidotti, and G. K. Chang, "Key technologies for next-generation digital RoF mobile front-haul with statistical data compression and multiband modulation," Journal of Lightwave Technology, vol. 35, no. 17, pp. 3671-3679, 2017. DOI: 10.1109/jlt.2017.2715003.
  5. S. M. Kim, "Limits of digital unit-remote radio unit distance and cell coverage induced by time division duplex profile in mobile WiMAX systems," International Journal of Communication Systems, vol. 26, no. 2, pp. 250-258, 2013. DOI: 10.1002/dac.1356.
  6. S. H. Cho, H. Park, H. S. Chung, K. H. Doo, S. S. Lee, & J. H. Lee, "Cost-effective next generation mobile fronthaul architecture with multi-IF carrier transmission scheme," in Proceedings of the Optical Fiber Communications Conference and Exhibition, San Francisco, CA, 2014. DOI: 10.1364/OFC.2014.Tu2B.6.
  7. S. M. Kim, "Nonlinearity detection and compensation in radio over fiber systems using a monitoring channel," Journal of Information and Communication Convergence Engineering, vol. 13, no. 3, pp. 167-171, 2015. DOI: 10.6109/jicce.2015.13.3.167.
  8. A. C. Najarro and S. M. Kim, "Predistortion for frequency-dependent nonlinearity of a laser in RoF systems," Journal of Information and Communication Convergence Engineering, vol. 14, no. 3, pp. 147-152, 2016. DOI: 10.6109/jicce.2016.14.3.147.
  9. D. H. Nguyen and B. Widrow, "Neural networks for self-learning control systems," IEEE Control Systems Magazine, vol. 10, no. 3, pp. 18-23, 1990. DOI: 10.1109/37.55119.
  10. J. Gill, B. Singh, and S. Singh, "Training backpropagation neural networks with genetic algorithm for weather forecasting," in Proceedings of the 8th International Symposium on Intelligent Systems and Informatics, Subotica, Serbia, pp. 465-469, 2010. DOI: 10.1109/SISY.2010.5647319.
  11. C. Ozkan and F. Sunar, "The comparison of activation functions for multispectral Landsat TM image classification," Photogrammetric Engineering & Remote Sensing, vol. 69, no. 11, pp. 1225-1234, 2003. DOI: 10.14358/PERS.69.11.1225.
  12. Caltech, "Machine learning (JC_lecture 10 sides)," [Internet], Available: https://itunes.apple.com/us/course/machine-learning/id515364596.
  13. A. Reynaldi, S. Lukas, and H. Margaretha, "Backpropagation and Levenberg-Marquardt algorithm for training finite element neural network," in Proceedings of the 6th UKSim/AMSS European Symposium on Computer Modeling and Simulation, Valetta, Malta, pp. 89-94, 2012. DOI: 10.1109/EMS.2012.56.

피인용 문헌

  1. Earthquake Ground Motion Matching on a Small Electric Shaking Table Using a Combined NN-PDFF Controller vol.2020, pp.None, 2020, https://doi.org/10.1155/2020/7260590