Artificial Neural Network-Based Transmission Power Control for Underwater Wireless Optical Communication System

Mohammed Salim, Omar N.; Adnan, Salah A.; Mutlag, Ammar H.

doi:10.30684/etj.2022.136247.1309

	Artificial Neural Network-Based Transmission Power Control for Underwater Wireless Optical Communication System
Engineering and Technology Journal
Article 16, Volume 41, Issue 2, February 2023, Pages 456-466 PDF (1.19 M)
Document Type: Research Paper
DOI: 10.30684/etj.2022.136247.1309
Authors
Omar N. Mohammed Salim¹; Salah A. Adnan^* ²; Ammar H. Mutlag¹
¹Middle Technical University
²Laser and optoelectronics engineering, university of technology Iraq Baghdad
Abstract
Several scientists have proposed using an underwater wireless optical communication system (UWOC) to deliver high-speed data services using the abundant optical spectrum. However, wireless optical signal propagation faces an antagonistic environment when using undersea channels due to various factors like scattering, absorption, turbulence, and optical link misalignment between the transmitter and the receiver. These factors will attenuate the optical signal and lead to degrading system performance. To reduce these factors impact on the communication system's performance, transmitted optical power (OTP) should be increased. Since the UWOC system is battery-powered, increasing OTP will consume more electrical power. Therefore, it is necessary to adjust OTP to a value commensurate with the underwater channel changes. So, an ANN model is proposed in this article for link adaptation, which can adjust the OTP level in tandem with the underwater channel conditions. Data for training, testing, and validation of the proposed system reliability was collected experimentally, and tap water was used as a transmission medium. Evaluation of the proposed model outcome demonstrates that reliable performance is achieved in predicting OTP needed in multiple scenarios. The MSE of the predicted OTP is(9.5×10-3,1.5×10-2, and 1.7×10-2) dBm in the training, testing, and validation stages, respectively. The regression values of the training, testing and validation sets are (0.9997,0.9990, and 0.9996). The results achieved by the proposed model prove it is reliable to be applied in UWOC systems.
Highlights
A 450 nm Underwater Wireless Optical Communication System was implemented in this study. Bit error rate was measured in the tap water channel for 2Mbit/s,10Mbit/s, and 20Mbit/s. The optimization for striking a balance between low power consumption and reliable data transfer in underwater ambient was investigated. A FFBP-ANN model-based transmission power control for the UWOC system has been adopted. power needed in multiple scenarios.
Keywords
UWOC; ANN; transmitted power control; neural network; back-propagation algorithm

References
[1] H. Zhan, Y. Peng, B. Chen, L. Wang, W. Wang, and S. Zhao, Diffractive deep neural network based adaptive optics scheme for vortex beam in oceanic turbulence, Opt. Express. 30 (2022) 23305. doi: 10.1364/oe.462241. [2] S. A. Adnan, A. Alchalaby, and H. A. Hassan, Future Optimization Algorithm to Estimate Attenuation in 532 nm Laser Beam of UWOC-Channel: Improved Neural Network Model, MMEP. 8 (2021) 453–460. doi:10.18280/MMEP.080316. [3] S. Adnan, M. Ali, and F. Hakwar, The Air Bubbles Effect for Underwater Optical Wireless Communication Using 650 nm Wavelength, Eng. Tech. journal, 37 (2019) 398–403. doi: 10.30684/etj.37.10a.3. [4] F. Saad, S. A. A. Ibrahim, and M. A. A. Ali, Performance of Underwater Wireless Optical Communication System under Salty Water, IJONS . 9 (2019) 16890–16894. doi:10.4028/ IJONS.398.29. [5] G. Schirripa Spagnolo, L. Cozzella, and F. Leccese, Underwater Optical Wireless Communications: Overview, Sensors (Basel), 20 (2020) doi: 10.3390/s20082261. [6] S. A. Adnan, H. A. Hassan, A. Alchalaby, and A. C. Kadhim, Experimental study of underwater wireless optical communication from clean water to turbid harbor under various conditions, IJDNE, 16 (2021) 219–226. doi:10.18280/ijdne.160212. [7] Z. Zhou, W. Guan, and S. Wen, Recognition and evaluation of constellation diagram using deep learning based on underwater wireless optical communication, arXiv preprint,2020.doi:/10.48550/arXiv.2007.05890 [8] H. Lu, W. Chen, and M. Jiang, Deep Learning Aided Misalignment-Robust Blind Receiver for Underwater Optical Communication, IEEE Wireless Communications Letters, 10 (2021) 1984–1988. doi:10.1109/LWC.2021.3089554. [9] C. Gabriel, M. A. Khalighi, S. Bourennane, P. Léon, and V. Rigaud, Monte-Carlo-Based Channel Characterization for Underwater Optical Communication Systems, JOCN, 5 (2013) 1–12. doi: 10.1364/JOCN.5.000001. [10] C. D. Mobley, D. Stramski, W. Paul Bissett, and E. Boss, Optical modeling of ocean waters: Is the case 1 - case 2 classification still useful?, Oceanography, 17 (2004) 60–67. doi: 10.5670/OCEANOG.2004.48. [11] S. A. Adnan, M. A. Ali, A. C. Kadhim, M. Sadeq, and M. Riaz, Investigating the performance of underwater wireless optical communication with intensity modulation direct detection technique, Optics InfoBase Conference Papers, 71 (2017) 1-3. doi: 10.1364/PV.2017.JW5A.14. [12] C. D. Mobley et al., Comparison of numerical models for computing underwater light fields, Applied Optics, vol. 32 (1993) 7484–7504. doi: 10.1364/AO.32.007484. [13] Z. Zhou, Z. Peng, J. H. Cui, and Z. Shi, Efficient multipath communication for time-critical applications in underwater acoustic sensor networks, IEEE/ACM Transactions on Networking, 19 (2011) 28–41. doi:10.1109/TNET.2010.2055886. [14] S. Han, Y. Noh, R. Liang, R. Chen, Y. J. Cheng, and M. Gerla, Evaluation of underwater optical-acoustic hybrid network, China Communications, 11 (2014) 49–59.doi: 10.1109/CC.2014.6880460. [15] A. Celik, N. Saeed, B. Shihada, T. Y. Al-Naffouri, and M. S. Alouini, A Software-Defined Opto-Acoustic Network Architecture for Internet of Underwater Things, IEEE Communications Magazine, 58 (2020) 88–94. doi:10.1109/MCOM.001.1900593. [16] H. A. Atiyah and M. Y. Hassan, Outdoor Localization in Mobile Robot with 3D LiDAR Based on Principal Component Analysis and K-Nearest Neighbors Algorithm, Eng. Tech. journal, 39 (2021) 965-976. doi:10.30684/etj.v39i6.2032. [17] H. M. Ahmed and S. R. Hameed, Eye Diseases Classification Using Back Propagation Artificial Neural Network, Eng. Tech. journal, 39 (2021) 11–20.doi: 10.30684/ETJ.V39I1B.1363. [18] O. N. Mohammed Salim, New neuro-fuzzy system-based holey polymer fibers drawing process, AIP Advances, 7 (2017) 105301.doi: 10.1063/1.4998270. [19] Z. Munadhil, S. K. Gharghan, A. H. Mutlag, A. Al-Naji, and J. Chahl, Neural Network-Based Alzheimer’s Patient Localization for Wireless Sensor Network in an Indoor Environment, IEEE Access, 8 (2020) 150527–150538. doi:10.1109/ACCESS.2020.3016832. [20] X. Xie, S. Peng, and X. Yang, Deep Learning-Based Signal-To-Noise Ratio Estimation Using Constellation Diagrams, Mob. Inf. Syst., 2020 (2020) 1-9.doi: 10.1155/2020/8840340. [21] W. Karner, O. Nemethova, and M. Rupp, Link error prediction in wireless communication systems with quality based power control, IEEE ICC, 2007, 5076–5081. doi: 10.1109/ICC.2007.838. [22] J. Zou, Y. Han, and S. S. So, Overview of artificial neural networks, Methods in Molecular Biology, 458 (2008) 15–23. doi: 10/1007/978-1-60327-101-1. [23] J. Schmidhuber, Deep learning in neural networks: An overview, Neural Networks, 61 (2015) 85–117. doi:0.1016/J.NEUNET.2014.09.003. [24] I. Hameed, P. V. Tuan, and I. Koo, Exploiting a deep neural network for efficient transmit power minimization in a wireless powered communication network, Applied Sciences (Switzerland), 10 (2020).doi:10.3390/app10134622. [25] R. Jiao, X. Huang, X. Ma, L. Han, and W. Tian, A Model Combining Stacked Auto Encoder and Back Propagation Algorithm for Short-Term Wind Power Forecasting, IEEE Access, 6 (2018) 17851–17858. doi:10.1109/ACCESS.2018.2818108. [26] S. R. Devi et al., Performance Comparison of Artificial Neural Network Models for Daily Rainfall Prediction, Machine Intelligence Research, 13 (2016) 417–427.doi: 10.1007/S11633-016-0986-2. [27] R. M. Palnitkar and J. Cannady, A review of adaptive neural networks, Conference Proceedings - IEEE SOUTHEASTCON, 2004, 38–47.doi:10.1109/SECON.2004.1287896. [28] R. A. Mohammed, O. N. M. Salim, A. H. Al-Nakkash, and A. A. S. Alabdullah, Proposed APs Distribution Optimization Algorithm: Aware of Interference (APD-AI), IOP Conference Series: Materials Science & Eng., 745,2020, 012040. doi: 10.1088/1757-899X/745/1/012040. [29] A. H. Mutlag, O. N. M. Salim, and S. Q. Mahdi, Optimum PID controller for airplane wing tires based on gravitational search algorithm, Bulletin of EEI, 10 (2021)1905–1913. doi:10.11591/EEI.V10I4.2953. [30] A. H. Mutlag, S. Q. Mahdi, S. K. Gharghan, O. N. M. Salim, A. Al-Naji, and J. Chahl, Improved Control System Based on PSO and ANN for Social Distancing for Patients With COVID-19, IEEE Access, 10 (2022) 63797–63811. doi: 10.1109/ACCESS.2022.3183124. [31] A. H. Mutlag, S. Qays Mahdi, and O. N. Mohammed Salim, A Comparative Study of an Artificial Intelligence-Based Vehicle Airbag Controller, IEEE 18th, CSPA Proceeding, 2022, 85–90.doi: 10.1109/CSPA55076.2022.9782038. [32] A. Malekian and N. Chitsaz, Concepts, procedures, and applications of artificial neural network models in streamflow forecasting, Advances in Streamflow Forecasting, (2021) 115–147. doi: 10.1016/B978-0-12-820673-7.00003-2. [33] A. H. Mutlag, S. Q. Mahdi, and O. N. M. Salim, A New Switching Controller Based Soft Computing-High Accuracy Implementation of Artificial Neural Network, IJCSCN, 7 (2017). 01–16.doi: 10.11591/IJC.V10I4.2953. [34] C. Yang, H. Kim, S. P. Adhikari, and L. O. Chua, A Circuit-Based Neural Network with Hybrid Learning of Backpropagation and Random Weight Change Algorithms, Sensors, 17 (2016) 16.doi:10.3390/S17010016. [35] H. A. R. Akkar, S. Qasim, and G. Haddad, Diagnosis of Lung Cancer Disease Based on Back-Propagation Artificial Neural Network Algorithm, Eng. Tech. journal, 38 (2020) 184–196.doi:10.30684/ETJ.V38I3B.1666. [36] X. Lin et al., All-optical machine learning using diffractive deep neural networks, Science (1979), 361 (2018) 1004–1008. doi: 10.1126/science.aat8084. [37] I. M. Jaber; H. A. R. Akkar; H. R. Hatem, OFDM Channel Estimation Based on Intelligent Systems. Eng. Tech. journal, 32 (2014) 305-324. [38] N. Farsad and A. Goldsmith, Neural network detection of data sequences in communication systems, IEEE Transactions on Signal Processing, 66 (2018) 5663–5678. doi: 10.1109/TSP.2018.2868322. [39] M. Yang, B. Xie, Y. Dou, and G. Xue, Cascade Forward Artificial Neural Network based Behavioral Predicting Approach for the Integrated Satellite-terrestrial Networks, Mobile Netw Appl. 27 (2022) 1569–1577. doi:/10.1007/s11036-021-01875-6. [40] M. M. M. Elshamy, N. T. Artem, V. U. Evgeniya, and M. Z. Elgendy, Comparison of feed-forward, cascade-forward, and Elman algorithms models for determination of the elastic modulus of pavement layers, ACM Series.(2021) 46–53. doi:10.1145/3465222.3465235. [41] W. Jia, D. Zhao, T. Shen, Y. Tang, and Y. Zhao, Study on optimized Elman neural network classification algorithm based on PLS and CA, Comput. Intell. Neurosci.2014 (2014)12.doi: 10.1155/2014/724317. [42] G. Ren, Y. Cao, S. Wen, T. Huang, and Z. Zeng, A modified Elman neural network with a new learning rate scheme, Neurocomputing, 286 (2018) 11–18. doi: 10.1016/J.NEUCOM.2018.01.046. [43] A. M. Hemeida et al., Nature-inspired algorithms for feed-forward neural network classifiers: A survey of one decade of research, Ain Shams Eng. Journal. Ain Shams University, 2020. doi: 10.1016/j.asej.2020.01.007. [44] W. Wang, C. Zhang, W. Wang, and C. Zhang, Bifurcation of a feed forward neural network with delay and application in image contrast enhancement, MBE . 17 (2020)387–403.doi:10.3934/MBE.2020021. [45] J. M. P. Menezes and G. A. Barreto, Long-term time series prediction with the NARX network: An empirical evaluation, Neurocomputing, 71 (2008) 3335–3343. doi: 10.1016/J.NEUCOM.2008.01.030. [46] H. Xie, H. Tang, and Y. H. Liao, Time series prediction based on narx neural networks: An advanced approach, Proceedings of the 2009 ICMLC, 3 (2009) 1275–1279.doi: 10.1109/ICMLC.2009.5212326. [47] Q. Liu, W. Chen, H. Hu, Q. Zhu, and Z. Xie, An Optimal NARX Neural Network Identification Model for a Magnetorheological Damper With Force-Distortion Behavior, Front. Mater. 7 (2020) 10. doi:10.3389/FMATS.2020.00010/BIBTEX. [48] Robert, R.D, Gregory D.Hager.2020. Deep learning: RNNs and LSTM, Handbook of Medical Image Computing and Computer Assisted Intervention, Vol.21, pp. 503–519.doi: 10.1016/B978-0-12-816176-0.00026-0. [49] A. Lazar, G. Pipa, and J. Triesch, SORN: A self-organizing recurrent neural network, Front. Comput. Neurosci. 3 (2009) 1-9.doi: 10.3389/NEURO.10.023.2009/BIBTEX.
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