Comparative Study of a Deterministic Adaptive Beamforming Technique with Neural Network Implementations
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-08341-9_9
Reference11 articles.
1. Senthilkumar, K.S., Pirapaharan, K., Hoole, P.R.P., Hoole, H.R.H.: Single perceptron model for smart beam forming in array antennas. Int. J. Electr. Comput. Eng. 6(5), 2300– 2309 (2016)
2. Ramezanpour, P., Rezaei, M.J., Mosavi, M.R.: Deep-learning-based beamforming for rejecting interferences. IET Sign. Process. 14, 467–473 (2020)
3. Che, H., Li, C., He, X., Huang, T.: A recurrent neural network for adaptive beamforming and array correction. Neural Netw. 80, 110–117 (2016)
4. Zaharis, Z., Gotsis, K., Sahalos, J.N.: Comparative study of neural network training applied to adaptive beamforming of antenna arrays. Prog. Electromagn. Res 126, 269–283 (2012)
5. Zaharis, Z.D., et al.: Implementation of antenna array beamforming by using a novel neural network structure. In: International Conference Telecommunications and Multimedia (TEMU), Heraklion, Greece (2016)
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