Adaptive interference cancellation in circular arrays with radial basis function neural networks

A new neural network based approach to the problem of adaptive interference nulling using circular arrays is presented. In modern cellular, satellite mobile communications systems and in GPS systems, both desired and interfering signals change their directions continuously. This paper develops a fast tracking system to constantly track the users, and then adapt the radiation pattern of the antenna to direct multiple narrow beams to desired users and nulls to sources of interference. In the approach suggested here, the computation of the optimum weights is viewed as a mapping problem which can be modeled using a three-layer Radial Basis Function Neural Networks (RBFNN) trained with input/output pairs. The results obtained from this network are in excellent agreement with the Wiener solution. It was found that networks implementing these functions are successful in tracking mobile users as they move across the antenna´s field of view.