Artificial Neural Networks Oriented Testbed for Multiantenna Wireless Application

Abstract

This research article provides viable research solutions by using Artificial Neural Networks (ANN) for Multiantenna wireless applications with less computational complexity. Artificial Neural Networks Oriented Testbed (ANNOT) is proposed where intelligence through artificial neurons are exploited for training multiantenna wireless application. A feedforward backpropagation network is trained with the required input parameters using training algorithms and its convergence for iterations for target parameters are simulated and developed. The ANNOT intelligently provides the required outputs from the trained values when validated for the tested output parameter in Multiantenna wireless application such as data transmission. Testbed input target parameters are bandwidth, signal power, channel statistics, noise power and output parameters metrics are capacity, probability of error which are executed in matrix laboratory (MATLAB). Obtained results are analyzed in gradient based algorithms and variants of neural networks for mean square error (MSE) against number of iterations/epochs which provide optimized results from ANNOT with less computational complexity. Validation results are also obtained for capacity and probability of error for data transmission multiantenna wireless application.