doi: 10.17586/2226-1494-2017-17-4-670-676


BEAMFORMING ALGORITHM FOR BASE STATION IN SENSOR NETWORK WITH ENERGY HARVESTING

E. A. Bakin, N. V. Apanasenko, I. S. Ivanov, M. N. Shelest


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Article in Russian

For citation: Bakin E.A., Apanasenko N.V., Ivanov I.S., Shelest M.N. Beamforming algorithm for base station in sensor network with energy harvesting. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2017, vol. 17, no. 4, pp. 670–676 (in Russian). doi: 10.17586/2226-1494-2017-17-4-670-676

Abstract

 Subject of Research.We consider beamforming issues for a base station multiple antenna system in a sensor network. The feature of the considered task lies in equipment of network nodes with energy harvesters for accumulation of electromagnetic energy for a battery charge. Hence, beamforming algorithm influences  significantly on general system stability. Method. We propose a method for antenna pattern optimization that considers both characteristics of existing prototypes of harvesters and adaptive adjustment of antenna amplitude-phase components. This method is based on a reduction of the initial optimization problem to a quadratic optimization problem with consequent application of polyhedral approximation. An efficiency of the proposed approach is analyzed via simulations. Main Results. In series of computational experiments we have discovered, that an application of the considered method brings to a significant reduction of a base station transmitted electromagnetic power. Thus, for small networks (three nodes) the gain may achieve up to 4.7 dB in comparison with isotropic radiation, for large networks (thirty six nodes) it is up to 1.8 dB. Practical Relevance. The considered algorithm may be applied in development and operation support of sensor networks with nodes located statically while a constant and reliable power management is a crucial process.


Keywords: sensor network, energy harvesting, multi-antenna transmission, radiation pattern synthesis

Acknowledgements. This research was financially supported by the Foundation for Assistance to Small Innovative Enterprises in course of UMNIK program (contract No. 7009GU2015 dated 03.08.2015).

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