Title :
Neural network-based adaptive beamforming for one- and two-dimensional antenna arrays
Author :
Zooghby, A.H.El. ; Christodoulou, C.G. ; Georgiopoulos, M.
Author_Institution :
Dept. of Electr. & Comput. Eng., Central Florida Univ., Orlando, FL, USA
fDate :
12/1/1998 12:00:00 AM
Abstract :
We present a neural network approach to the problem of finding the weights of one- (1-D) and two-dimensional (2-D) adaptive arrays. In modern cellular satellite mobile communications systems and in global positioning systems (GPSs), both desired and interfering signals change their directions continuously. Therefore, a fast tracking system is needed to constantly track the users and then adapt the radiation pattern of the antenna to direct multiple narrow beams to desired users and nulls interfering sources. In the approach suggested in this paper, the computation of the optimum weights is accomplished using three-layer radial basis function neural networks (RBFNN). The results obtained from this network are in excellent agreement with the Wiener solution
Keywords :
Global Positioning System; adaptive antenna arrays; array signal processing; cellular radio; direction-of-arrival estimation; interference suppression; linear antenna arrays; mobile satellite communication; radial basis function networks; radiofrequency interference; telecommunication computing; 1D antenna array; 2D antenna array; GPS; Wiener solution; adaptive arrays; cellular satellite mobile communications systems; fast tracking system; global positioning systems; interfering signals; interfering sources nulling; linear array; neural network-based adaptive beamforming; optimum weights; radiation pattern; three-layer radial basis function neural networks; weights; Adaptive arrays; Adaptive systems; Antenna radiation patterns; Array signal processing; Artificial satellites; Computer networks; Mobile communication; Neural networks; Radial basis function networks; Two dimensional displays;
Journal_Title :
Antennas and Propagation, IEEE Transactions on
