Multilayer Extreme Learning Machine as Equalizer in OFDM-based Radio-over-fiber Systems
Keywords:
Machine Learning, equalizer, 5G TechnologyAbstract
Mobile/wireless networks aim to support diverse services with numerous and sophisticated requirements, such as energy efficiency, spectral efficiency, negligible latency, robustness against time and frequency selective channels, low hardware complexity, among others. From the central station to the base stations, radio-over-fiber orthogonal frequency division multiplexing (RoF-OFDM) schemes with direct-detection are then implemented. Unfortunately, laser phase noise, chromatic fiber dispersion, and carrier frequency offset impair the orthogonality of the subcarriers; hence, deteriorating the performance of the RoF-OFDM system. In order to take all the processing tasks to the cognitive level (the last goal in the telecommunication industry), various extreme learning machines (ELMs), composed by only a single hidden layer, have been recently adopted as equalizers. The reason behind this trend comes from the lower computational complexity, higher detection accuracy, and minimum human intervention of the ELM algorithms. In this article, we introduce a multilayer ELM-based receiver for RoF schemes transmitting phase-correlated OFDM signals affected by phase and frequency errors. Results report that by appropriately setting the hyper-parameters of the multilayer ELMs, the ELM with 3 hidden layers outperforms most of the ELMs reported in the literature (the ELM with 2 hidden layers, original ELM, regularized ELM, and 2 fully-independent ELMs defined in the real domain), as well as the benchmark pilot-assisted equalizer in terms of bit error rate. Nevertheless, this benefit comes with excessive computational cost. Finally, we show that the fully-complex ELM is still the best equalizer taking into account several key metrics.
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J. Beas, G. Castanon, I. Aldaya, A. Aragon-Zavala, and G. Campuzano, “Millimeter-wave frequency radio over fiber systems: A survey,” IEEE Communications Surveys Tutorials, vol. 15, no. 4, pp. 1593–1619, 2013.
J. Perez Santacruz, S. Rommel, U. Johannsen, A. Jurado-Navas, and I. Tafur Monroy, “Candidate waveforms for ARoF in beyond 5G,” Applied Sciences, vol. 10, no. 11, 2020.
Y. He, Y. Chen, Y. Hu, and B. Zeng, “WiFi vision: sensing, recognition, and detection with commodity MIMO-OFDM WiFi,” IEEE Internet of Things Journal, pp. 1–1, 2020.
G. Li, Z. Lin, X. Huang, and J. Li, “A radio over fiber system with simultaneous wireless multi-mode operation based on a multi-wavelength optical comb and pulse-shaped 4QAM-OFDM,” Electronics, vol. 8, no. 10, 2019.
V. Sarup and A. Gupta, “A Study of various trends and enabling technologies in radio over fiber (RoF) systems,” Optik, vol. 126, no. 20, pp. 2606 – 2611, 2015.
L. Zhang, Y. Ming, and J. Li, “Suppression of laser phase noise in direct-detection optical OFDM transmission using phase-conjugated pilots,” Optics Communications, vol. 403, pp. 197 – 204, 2017.
V. A. Thomas, M. El-Hajjar, and L. Hanzo, “Millimeter-wave radio over fiber optical upconversion techniques relying on link nonlinearity,” IEEE Communications Surveys Tutorials, vol. 18, no. 1, pp. 29–53, 2016.
P. Torres Ferrera, L. Pacheco Ramirez, and R. Gutierrez Castrejon, “Next-generation 400 Gb/s Ethernet PMD over SMF at 1310 nm via DD-OFDM with electro-absorption modulator-based transmitters,” IEEE Latin America Transactions, vol. 14, no. 6, pp. 2681–2686, 2016.
D. Zabala-Blanco, C. A. Azurdia-Meza, S. Montejo-Sanchez, and A. Dehghan Firoozabadi, “OFDM tolerance to additive white Gaussian and laser phase noises in optical heterodyning systems accompanied by the feasible pilot-assisted equalization,” Optical Review, vol. 27, no. 2, pp. 156–169, Apr 2020.
V. Kishore, V. S. Prasad, and V. V. Mani, “A blind timing synchronization algorithm for DCO-OFDM VLC systems,” IEEE Photonics Technology Letters, vol. 32, no. 17, pp. 1121–1124, 2020.
L. Gonzalez-Guerrero, H. Shams, I. Fatadin, M. J. Fice, M. Naftaly, A. J. Seeds, and C. C. Renaud, “Single sideband signals for phase noise mitigation in wireless THz-over-fibre systems,” Journal of Lightwave Technology, vol. 36, no. 19, pp. 4527–4534, 2018.
L. Tao, J. Yu, J. Zhang, Y. Shao, and N. Chi, “Reduction of intercarrier interference based on window shaping in OFDM RoF systems,” IEEE Photonics Technology Letters, vol. 25, no. 9, pp. 851–854, 2013.
T. Kanesan, S. T. Le, D. Roque, and A. D. Ellis, “Non-rectangular perfect reconstruction pulse shaping based ICI reduction in CO-OFDM,” Optics Express, vol. 22, no. 2, pp. 1749–1759, Jan 2014.
D. Zabala-Blanco, G. Campuzano, C. A. Azurdia-Meza, and S. Montejo-Sanchez, “Performance enhancement in OFDM systems with ICI utilizing the improved double-jump linear combination pulse,” in IEEE 9th Latin-American Conference on Communications (LATINCOM), 2017, pp. 1–6.
M. R. Kaloop, D. Kumar, P. Samui, A. R. Gabr, J. W. Hu, X. Jin, and B. Roy, “Particle swarm optimization algorithm-extreme learning machine (PSO-ELM) model for predicting resilient modulus of stabilized aggregate bases,” Applied Sciences, vol. 9, no. 16, 2019.
D. Zabala-Blanco, M. Mora, C. A. Azurdia-Meza, A. Dehghan Firoozabadi, P. Palacios Jativa, and I. Soto, “Relaxation of the radiofrequency linewidth for coherent-optical orthogonal frequency-division multiplexing schemes by employing the improved extreme learning
machine,” Symmetry, vol. 12, no. 4, 2020.
D. Zabala-Blanco, G. Campuzano, I. Aldaya, G. Castanon, and C. Vargas-Rosales, “Impact of partial phase decorrelation on the performance of pilot-assisted millimeter-wave RoF-OFDM systems,” Physical Communication, vol. 26, pp. 106 – 115, 2018.
B. Lim and Y. Ko, “SIR analysis of OFDM and GFDM waveforms with timing offset, CFO, and phase noise,” IEEE Transactions on Wireless Communications, vol. 16, no. 10, pp. 6979–6990, 2017.
D. Zabala-Blanco, M. Mora, C. A. Azurdia-Meza, and A. Dehghan Firoozabadi, “Extreme learning machines to combat phase noise in RoFOFDM schemes,” Electronics, vol. 8, no. 9, 2019.
A. Ferreira, B. Dias, J. A. de Oliveira, A. Alves, J. D. Marconi, G. Campuzano, J. Pita, and I. Aldaya, “Optimization of OFDM Parameters for 10-Gbps Long Reach Coherent PONs,” in 21st International Conference on Transparent Optical Networks (ICTON), 2019, pp. 1–4.
D. Wang, L. Yuan, J. Lei, G. wu, S. Li, R. Ding, and D. Wang, “Joint channel/frequency offset estimation and correction for coherent optical FBMC/OQAM system,” Optical Fiber Technology, vol. 39, pp. 87 – 94, 2017.
C. Zhang, Y. Ueng, C. Studer, and A. Burg, “Artificial intelligence for 5G and beyond 5G: implementations, algorithms, and optimizations,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 10, no. 2, pp. 149–163, 2020.
M. G. Kibria, K. Nguyen, G. P. Villardi, O. Zhao, K. Ishizu, and F. Kojima, “Big data analytics, machine learning, and artificial intelligence in next-generation wireless networks,” IEEE Access, vol. 6, pp. 32 328–32 338, 2018.
G. Huang, G.-B. Huang, S. Song, and K. You, “Trends in extreme learning machines: A review,” Neural Networks, vol. 61, pp. 32 – 48, 2015.
G. Kaur and G. Kaur, “Performance analysis of Wilcoxon-based machine learning nonlinear equalizers for coherent optical OFDM,” Optical and Quantum Electronics, vol. 50, no. 6, p. 256, Jun 2018.
D. F. Carrera, D. Zabala-Blanco, C. Vargas-Rosales, and C. A. Meza-Azurdia, “Extreme learning machine-based receiver for multi-user massive MIMO systems,” IEEE Communications Letters, 2020.
J. Liu, K. Mei, X. Zhang, D. Ma, and J. Wei, “Online extreme learning machine-based channel estimation and equalization for OFDM systems,” IEEE Communications Letters, vol. 23, no. 7, pp. 1276–1279, 2019.
L. Yang, Q. Zhao, and Y. Jing, “Channel equalization and detection with ELM-based regressors for OFDM systems,” IEEE Communications Letters, vol. 24, no. 1, pp. 86–89, 2020.
L. Kasun, H. Zhou, G.-B. Huang, and C. Vong, “Representational learning with ELMs for big data,” IEEE Intelligent Systems, vol. 28, pp. 31–34, 2013.
J. Tang, C. Deng, and G. Huang, “Extreme learning machine for multilayer perceptron,” IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 4, pp. 809–821, 2016.
S. Ding, N. Zhang, X. Xu, L. Guo, and J. Zhang, “Deep extreme learning machine and its application in EGG classification,” Mathematical Problems in Engineering, vol. 2014, p. 129021, Nov 2014.
I. Aldaya, J. Beas, G. Castanon, G. Campuzano, and A. Aragon-Zavala, “A survey of key-enabling components for remote millimetric wave generation in radio over fiber networks,” Optics & Laser Technology, vol. 49, pp. 213 – 226, 2013.
S. Kamal, C. A. Azurdia-Meza, and K. Lee, “Suppressing the effect of ICI power using dual sinc pulses in OFDM-based systems,” AEU - International Journal of Electronics and Communications, vol. 70, no. 7, pp. 953 – 960, 2016.
