Competitive Advantages:
Optimizes wireless communication by efficient signal selection, Deep learning techniques ensure strongest relay link, This technique shows promising for wireless communications in general such as in vehicle to vehicle communication and wireless sensor networks.
A system and method of propagating signal links by using artificial neural networks using a relay link selection protocol to predict an optimal link or path, providing a reliable mechanism to meet 5G-new radio requirements. The artificial neural networks used in the method classify training and testing datasets into sufficient signal strengths and insufficient signal strengths, such that paths are evaluated for predicted propagation links, and such that the strongest propagation link can be selected. Specifically, a multilayer perceptron method is used to identify and characterize new link candidates using the path loss parameter or the received signal strength, such that optimal links can be selected and updated. To determine the sufficiency of a signal, a threshold energy strength is determined (for example, a threshold of -120 dBm can be used; any energy strength below the threshold is considered a poor propagation and is classified as an insufficient signal).The technology developed by USF researchers applies a deep learning technique called Multi-Layer Perceptron. Since communication signals can be affected by the surrounding environments, the transmitted signal can be reflected, scattered and diffracted via objects that lead to receiving multiple signal components at the receiver side. The MLP method allows for optimum signal selection in an effort to minimize propagation loss and error for wireless communications. It ensures relay selection of the strongest link hence greatly optimizing communications. This can be used by medical diagnostic tools, antennas, and 5G cellular networks.
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