Beamforming using learning based algorithms
With an increasing number of subscribers to the terrestrial cellular satellite-based services, there is a resultant rise in the demand for the data rate, and there is a growing need for advanced antenna and signal processing schemes that improve the power and the spectral efficiencies. Adaptive beamforming using antenna arrays is one such technique. When multiple signals are impinging on the antenna array, beamforming can be used for increasing the signal to noise ratio (SNR) (achieved by increasing the Directivity of the formed beam along the direction of interest) and thereby for source separation/interference mitigation. In this thesis, we propose several new algorithms to improve the practical effectiveness of beamforming. These algorithms range from computationally complex closed-form solution to iterative estimation and optimization techniques. Out of all these algorithms, some require precise knowledge of the channel model, or some are based on prior assumptions, which, when violated, will deteriorate the performance of the system. The Neural Network (NN) based solutions are gaining popularity in communication system design. The NN operates in the blind mode, and it does not require a detailed a-priori mathematical model of the channel. It has shown some promising results in terms of accurately approximating some known algorithms with reduced complexity. The NN can effectively trade the performance with the complexity. Most of the applications of the NN aim at reducing the computational complexity of the existing approaches; little or no efforts have been spent to come up with an indigenous approach to do beamforming using NN.We have proposed a few beamforming schemes using NN. Our results show that the learned models can provide improvements in the suppression of interference and the number of pilot symbols required.
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