The present study introduces the implementation of antenna array beamforming based on a new neural network (NN) structure. The NN comprises two hidden layers, which use different interconnectivity patterns. The first one is divided in sublayers, which are equal in number to the inputs of the NN. Each sublayer communicates only with the respective input but is fully interconnected with the second hidden layer. The NN training is performed by using data sets derived by a well-known beamforming technique called minimum variance distortionless response. The trained NN is capable of serving as adaptive beamformer that makes a linear antenna array steer the main lobe towards a desired signal and place nulls towards respective interference signals in the presence of additive zero-mean Gaussian noise. The performance of the trained NN is tested by estimating the mean absolute deviation of main lobe and null directions from their respective desired directions.
|Title of host publication
|2016 International Conference on Telecommunications and Multimedia, TEMU 2016
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 24 Aug 2016
|2016 International Conference on Telecommunications and Multimedia - Heraklion, Heraklion, Greece
Duration: 25 Jul 2016 → 27 Jul 2016
|2016 International Conference on Telecommunications and Multimedia
|25/07/16 → 27/07/16