Performance of Signal Strength prediction in Data transmission Using Elliott wave Theory

Abstract

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.

An Artificial Intelligence Mechanism for the Prediction of Signal Strength in Drones to IoT Devices in Smart Cities

“…The radio wave signal strength prediction model based on deep learning currently has an MSE of 8.59, which is a significant practical reference value in engineering practice. By an article in [10], it is possible to predict future signals using prior signal samples. Forecasting improves customer service when amplitude and nonsystematic phase signals are applied in real-world signal processing settings.…”