Tuning of Extended-Resonance-Based Beamforming System for Visible Light Communication

Herminarto Nugroho, Muhammad Akbar Barrinaya

Abstract


Visible Light Communication (VLC) uses the visible light emitted from Light Emitting Diode (LED) to transmit/receive data. Since the data is transmitted through the light, the connection speed is as fast as the speed of light, making it potential for very fast and massive data exchange. One thing that needs to be considered in VLC is that the power of the signal received by the sensor relies on the angle between the LED and the light sensor used as an antenna. The bigger the angle between LED and light sensor, the less optimal the signal power will be, and surely will affect the speed and reliability of the data transmission. To optimize the signal power, multiple photonic sensors will be used as an antenna to receive the light signal. The signal received from each photonic sensor will be combined to get higher signal power. However, to ensure that all signals from all photonic sensors are constructive to each other, all phase differences must be minimized. This paper proposes the extended-resonance-based beamforming system to be used to minimize the phase difference of the light signals in VLC application. A non-linear optimization method is used to tune the extended-resonance-based beamforming system. Given that the varactor is chosen carefully and sufficient enough, the non-linear optimization method such as active set, interior point, or sequential quadratic programming is able to tune the varactor, so that the beamformer will compensate the phase difference from the incoming signal.

Keywords


Visible light communication; extended-resonance; beamforming; non-linear optimization.

Full Text:

PDF

References


N. Consulting, “Energy Savings Forecast of Solid-State Lighting in General Illumination Applications,†U.S. Dep. Energy Rep., no. August, pp. 2013–2014, 2014.

Y. Tanaka, S. Haruyama, and M. Nakagawa, “Wireless optical trasnsmissions with white colored LED for wireless home links,†IEEE Int. Symp. Pers. Indoor Mob. Radio Commun. PIMRC, vol. 2, pp. 1325–1329, 2000, doi: 10.1109/pimrc.2000.881634.

T. Deepa, H. Mathur, and K. A. Sunitha, “Spectrally efficient multicarrier modulation system for visible light communication,†Int. J. Electr. Comput. Eng., vol. 9, no. 2, p. 1184, 2019, doi: 10.11591/ijece.v9i2.pp1184-1190.

P. H. Pathak, X. Feng, P. Hu, and P. Mohapatra, “Visible Light Communication, Networking, and Sensing: A Survey, Potential and Challenges,†IEEE Commun. Surv. Tutorials, vol. 17, no. 4, pp. 2047–2077, 2015, doi: 10.1109/COMST.2015.2476474.

S. Cho, G. Chen, and J. P. Coon, “Securing visible light communication systems by beamforming in the presence of randomly distributed eavesdroppers,†IEEE Trans. Wirel. Commun., vol. 17, no. 5, pp. 2918–2931, 2018.

S. U. Rehman, S. Ullah, P. H. J. Chong, S. Yongchareon, and D. Komosny, “Visible light communication: a system perspective—overview and challenges,†Sensors, vol. 19, no. 5, p. 1153, 2019.

C. Danakis, M. Afgani, G. Povey, I. Underwood, and H. Haas, “Using a CMOS camera sensor for visible light communication,†2012 IEEE Globecom Work. GC Wkshps 2012, pp. 1244–1248, 2012, doi: 10.1109/GLOCOMW.2012.6477759.

T.-H. Do and M. Yoo, “Visible light communication-based vehicle-to-vehicle tracking using CMOS camera,†IEEE Access, vol. 7, pp. 7218–7227, 2019.

K.-L. Hsu et al., “CMOS camera based visible light communication (VLC) using grayscale value distribution and machine learning algorithm,†Opt. Express, vol. 28, no. 2, pp. 2427–2432, 2020.

H. Nugroho, W. K. Wibowo, A. R. Annisa, and H. M. Rosalinda, “Deep learning for tuning Optical Beamforming Networks,†Telkomnika (Telecommunication Comput. Electron. Control., vol. 16, no. 4, 2018, doi: 10.12928/TELKOMNIKA.v16i4.8176.

H. Nugroho, “Tuning of Optical Beamforming Networks: A Deep Learning Approach.†2015.

A. Meijerink et al., “Phased Array Antenna Steering using a Ring Resonator-based Optical Beam Forming Network,†in Proceedings of the IEEE Symposium on Communications and Vehicular Technology, Nov. 2006, pp. 7–12.

H. Schippers et al., “Broadband Conformal Phased Array with Optical Beamforming for Airborne Satellite Communication,†in Proceedings of the 2008 IEEE Aerospace Conference, Mar. 2008, pp. 1–17.

M. Elhefnawy, “Design and simulation of an analog beamforming phased array antenna,†Int. J. Electr. Comput. Eng., vol. 10, no. 2, pp. 1398–1405, 2020, doi: 10.11591/ijece.v10i2.pp1398-1405.

R. Maneiro-Catoira, J. Brégains, J. A. Garc’ia-Naya, and L. Castedo, “Analog beamforming using time-modulated arrays with digitally preprocessed rectangular sequences,†IEEE Antennas Wirel. Propag. Lett., vol. 17, no. 3, pp. 497–500, 2018.

Y. Ding, V. Fusco, A. Shitvov, Y. Xiao, and H. Li, “Beam index modulation wireless communication with analog beamforming,†IEEE Trans. Veh. Technol., vol. 67, no. 7, pp. 6340–6354, 2018.

D. G. Rabus, Integrated Ring Resonators: The Compendium. Berlin, Heidelberg: Springer, 2007.

A. Tombak and A. Mortazawi, “A Novel Low-Cost Beam-Steering Technique Based on the Extended-Resonance Power-Dividing Method,†IEEE Trans. Microw. Theory Tech., vol. 52, no. 2, pp. 664–670, 2004, doi: 10.1109/TMTT.2003.822031.

R. H. Byrd, N. I. M. Gould, J. Nocedal, and R. A. Waltz, “An algorithm for nonlinear optimization using linear programming and equality constrained subproblems,†Math. Program., vol. 100, no. 1, pp. 27–48, 2004, doi: 10.1007/s10107-003-0485-4.

A. Wächter, “An Interior Point Algorithm for Large-Scale Nonlinear Optimization with Applications in Process Engineering,†PhD thesis, 2002.

P. T. Boggs and J. W. Tolle, “Sequential quadratic programming for large-scale nonlinear optimization,†J. Comput. Appl. Math., vol. 124, no. 1–2, pp. 123–137, 2000, doi: 10.1016/S0377-0427(00)00429-5.

O. D. Montoya, W. Gil-González, and A. Garces, “Sequential quadratic programming models for solving the OPF problem in DC grids,†Electr. Power Syst. Res., vol. 169, pp. 18–23, 2019.

A. Mehmood, A. Zameer, S. H. Ling, A. ur Rehman, and M. A. Z. Raja, “Integrated computational intelligent paradigm for nonlinear electric circuit models using neural networks, genetic algorithms and sequential quadratic programming,†Neural Comput. Appl., vol. 32, no. 14, pp. 10337–10357, 2020.




DOI: http://dx.doi.org/10.18517/ijaseit.12.4.14504

Refbacks

  • There are currently no refbacks.



Published by INSIGHT - Indonesian Society for Knowledge and Human Development