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


Read the full article 
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).

References
 1.     Visser H.J., Vullers R.J.M. RF energy harvesting and transport for wireless sensor network applications: principles and requirements. Proceedings of the IEEE, 2013, vol.101, no. 6, pp. 1410–1423.doi: 10.1109/JPROC.2013.2250891
2.     Bogatyrev A.V., Bogatyrev V.A. The reliability of the cluster real-time systems with fragmentation and redundant service requests. Informatsionnye Tekhnologii, 2016, vol. 22, no. 6, pp. 409–416.
3.     Nishimoto H., Kawahara Y., Asami T. Prototype implementation of ambient RF energy harvesting wireless sensor networks. Proceedings of IEEE Sensors, 2010, pp. 1282–1287. doi: 10.1109/ICSENS.2010.5690588
4.     Dolgov A., Zane R., Popovic Z. Power management system for online low power RF energy harvesting optimization. IEEE Transactions on Circuits and Systems I, 2010, vol. 57, no. 7, pp. 1802–1811. doi: 10.1109/TCSI.2009.2034891
5.     Liu L., Zhang R., Chua K.C. Wireless information transfer with opportunistic energy harvesting. IEEE Transactions on Wireless Communications, 2013, vol. 12, no. 1, pp. 288–300. doi: 10.1109/TWC.2012.113012.120500
6.     Varshney L.R. Transporting information and energy simultaneously. Proc. IEEE Int. Symp. onInformation Theory, ISIT. Toronto, Canada, 2008, pp. 1612–1616. doi: 10.1109/ISIT.2008.4595260
7.     Ding Z., Perlaza S.M., Esnaola I., Poor H.V. Power allocation strategies in energy harvesting wireless cooperative networks. IEEE Transactions on Wireless Communications, 2014, vol. 13, no. 2, pp. 846–860. doi: 10.1109/TWC.2013.010213.130484
8.     Zhang R., Ho C.K. MIMO broadcasting for simultaneous wireless information and power transfer. IEEE Transactions on Wireless Communications, 2013, vol. 12, no. 5, pp. 1989–2001. doi: 10.1109/TWC.2013.031813.120224
9.     Xing C., Wang N., Ni J., Fei Z., Kuang J. MIMO beamforming designs with partial CSI under energy harvesting constraints. IEEE Signal Processing Letters, 2013, vol. 20, no. 4, pp. 363–366. doi: 10.1109/LSP.2013.2247999
10.  Ju H., Zhang R. A novel mode switching scheme utilizing random beamforming for opportunistic energy harvesting. IEEE Transactions on Wireless Communications, 2014, vol. 13, no. 4, pp. 2150–2162. doi: 10.1109/TWC.2014.030314.131101
11.  Boyd S., Vandenberghe L. Convex Optimization. Cambridge, Cambridge University Press, 2004, 730 p.
12.  Yuan Y. Recent advances in trust region algorithms. Mathematical Programming, 2015,vol. 151, no. 1, pp. 249–281. doi: 10.1007/s10107-015-0893-2
13.  Zheng X.J., Sun X.L., Li D. Convex relaxations for nonconvex quadratically constrained quadratic programming: matrix cone decomposition and polyhedral approximation. Mathematical Programming, 2011, vol. 129, no. 2, pp. 301–329. doi: 10.1007/s10107-011-0466-y
14.  Bakin E.A., Ivanov I.S., Shelest M.N., Turlikov A.M. Analysis of energy harvesting efficiency for power supply of WBAN nodes in heterogeneous scenarios. Proc. 8th Int. Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT. Lisbon, Portugal, 2016, pp. 111–118. doi: 10.1109/ICUMT.2016.7765342
15.  Cisco Aironet 1600 Series Access Point. Data Sheet. Available at: http://www.cisco.com/c/en/us/products/collateral/wireless/
aironet-1600-series/data_sheet_c78-715702.pdf (accessed: 10.06.2017)
16.  Rothammel K., Krischke A. Antennenbuch. Berlin, 1966.
17.  Propagation data and prediction methods for the planning of indoor radio communication systems and the radio local area networks in the frequency range 900 MHz to 100 GHz. ITU-R Recommendations P.1238-5. Geneva, 2001.
Bogatyrev V.A. An interval signal method of dynamic interrupt handling with load balancing. Automatic Control and Computer Sciences, 2000, vol. 34, no. 6, pp. 51–57.
Copyright 2001-2017 ©
Scientific and Technical Journal
of Information Technologies, Mechanics and Optics.
All rights reserved.

Яндекс.Метрика