Performance of Signal Strength prediction in Data transmission Using Elliott wave Theory
The article describes an algorithm for predicting the future signals with the aid of past signal samples. In the real signal processing environment, the amplitude and unsystematic in phase signal are lead to more complex to estimation the signal, thereby, customer service is enhanced by forecast. The forecast of financial marketplace are usually done by means of Elliot wave theory. In this article possibility and applicability survey of the EW Theory is proposed in the paper towards the power of the signal forecast. In nature, the EW theory has free declining environment, and also uncomfortable based on the customer and base station and height of the antenna. The proposed algorithm has tested in real life conditions, considering both, the pedestrian persons and the people travelling at 60 Km/Hr. Consequently, the predicted result incorporates the practical signal strength based on increasing distribution utility, signal to intervention noise ratio (SNR) and instability at their subsequent time. The end result of the algorithm shows 68% of successful prediction.
C.H. Lee, & C. J. Yu, (2004, March). An intelligent handoff algorithm for wireless communication systems using grey prediction and fuzzy decision system, In IEEE International Conference on Networking, Sensing and Control, 2004 (Vol. 1, pp. 541-546). IEEE.
A.M. Miyim, M. Ismail, R. Nordin, & M.T. Ismail, (2013). Technique for cross-layer vertical handover prediction in 4G wireless networks. Procedia Technology, 11, 114-121.
T.S. Shih, J.S. Su, & H.M. Lee, (2011) Fuzzy seasonal demand and fuzzy total demand production quantities based on intervalvalued fuzzy sets, International Journal of Innovative Computing, Information and Control, 7(5B), 2637-2650.
M. Al-Sanabani, S. Shamala, M. Othman, & Z. Zukarnain, (2008) Mobility Prediction Based Resource Reservation for Handoff in Multimedia Wireless Cellular Networks, International Arab Journal of Information Technology, 5(2), 162-169.
T. Liu, P. Bahl, & I. Chlamtac, (1998). Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks, IEEE Journal on selected areas in communications, 16(6) 922-936.
A. Sleem, & A. Kumar, (2005) Handoff management in wireless data networks using topography-aware mobility prediction, Journal of Parallel and Distributed Computing, 65(8), 963-982.
Y.F. Huang, Y.F. Chen, T.H. Tan, & H.L. Hung, (2010) Applications of fuzzy logic for adaptive interference canceller in CDMA wireless communication systems. International Journal of Innovative Computing, Information and Control, 6(4), 1749- 1761.
M.A. Bhagyaveni, R. Kalidoss, & K.S. Vishvaksenan, (2016) Introduction to analog and digital communication (Vol. 46) River Publishers.
P. M. Balasubramaniam, and A. Sathishkumar, Detection of Human Faces using Image Fusion Method, International Journal of Engineering Science and Technology, 3 (2011) E. Moreton, and P. Smolensky, Typological consequences of local constraint conjunction, Proceedings of the West Coast Conference on Formal Linguistics, 21(2002) 306-319.
N. DeHoratius, and E. Rabinovich, Field research in operations and supply chain management, Journal of Operations Management, 29 (2011) 371-375.
Copyright (c) 2020 Balasubramaniam P.M., Arivoli S, Prabhakaran N
This work is licensed under a Creative Commons Attribution 4.0 International License.
Views: Abstract : 73 | PDF : 29