Robust Adaptive Sidelobe and Interference Suppression for Uniform Circular Array Antenna Based on Back-propagation Neural Network

Abstract In the last decades, the uniform circular array antenna has been used extensively in wireless and satellite communications, especially in 5G. However, it suffers from high side-lobe interference. This paper proposes Back-propagation neural network (BPNN) algorithm to reduce the side-lobe levels. Via the gradient descent algorithm to adjust the weight of each neural network neuron. The result achieved excellent side-lobe suppression. The Back-propagation compares with other adaptive side-lobe suppression techniques (LMS) algorithms and (MVDR) to validate the algorithm’s efficiency. The method has a good learning impact and forms a deep narrow notch at main lobe interference.