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Neural network-based smart antennas are used for the solution of multiple-source tracking problems in the area of wireless communications. The architecture of the neural network is constructed in two stages, one stage for signal detection and the other for angle of arrival (AOA) estimation. The best candidates for this type of problem are radial basis(More)
Neural networks (NNs) have proven to be a very powerful tool both for one-dimensional (1D) and two-dimensional (2D) direction of arrival (DOA) estimation. By avoiding complex and time-consuming mathematical calculations, NNs estimate DOAs almost instantaneously. This feature makes them very convenient for real-time applications. Further, unlike the well(More)
We are concerned with time space signal processing in CDMA system, which uses specific receiving configuration with RAKE receivers. This configuration is improving the system performances while decreasing the system capacity. Antenna array could provide capacity improvement while keeping the good performances from time processing. Actually this is(More)
This paper considers the joint application of neural networks and antenna array systems in mobile, satellite and sensor systems. The main assumption is that there is a possibility of a large number of neurons in the network according the fact that the human brain uses a huge number of neurons. Results of detail analyze for signal detection, direction of(More)
This paper considers the application of radial basis function neural networks for antenna array systems. An overview of neural network-based direction of arrival estimation is presented, with different radial basis neural networks for signal detection stage. Following that approach, for the case where a large number of neurons can be used, a radial basis(More)
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