Distributed beamforming (DBF) is an efficient technique for reliable communications in wireless sensor networks (WSNs). In DBF based networks, the randomly distributed nodes cooperate together to form a randomly distributed antenna array (RAA) which has a main beam directed towards the intended receiver. Due to the nodes randomness, the DBF results in poor pattern characteristics such as high side lobe level (SLL) and pattern asymmetry around the main beam sides. In this paper, a fast deterministic algorithm for SLL reduction of open loop distributed antenna arrays is introduced. Unlike the existing state of the art optimization techniques for SLL reduction, the proposed algorithm provides a fast deterministic solution for energy transmission or the weight of each node without changing its location. Consequently, the exhaustive search burden of the optimization based techniques for the optimum weights is avoided. The simulation results reveal that the proposed algorithm has superior performance to the optimization techniques in terms of execution time, synthesized SLL, and half power beam width (HPBW).
1. Bhattacharyya, K., Phased Array Antennas: Floquet Analysis, Synthesis, BFNs and Active Array Systems, John Wiley & Sons, 2006.
2. Adachi, F., W. Peng, T. Obara, T. Yamamoto, R. Matsukawa, and S. Nakada, "Distributed antenna network for gigabit wireless access," International Journal of Electronics and Communications (AE ¨ U), Vol. 66, No. 8, 605-612, 2012. doi:10.1016/j.aeue.2012.03.010
3. Jung, S. Y. and B. W. Kim, "Near-optimal low-complexity antenna selection scheme for energy-efficient correlated distributed MIMO systems," International Journal of Electronics and Communications (AE ¨ U), Vol. 69, No. 7, 1039-1046, 2015. doi:10.1016/j.aeue.2015.04.002
4. Valenzuela-Valdes, J., F. Luna, R. Luque-Baena, and P. Padilla, "Saving energy in WSNs with beamforming," IEEE, International Conference on Cloud Networking, 255-260, 2014.
5. Ochiai, H., P. Mitran, H. V. Poor, and V. Tarokh, "Collaborative beamforming for distributed wireless ad hoc sensor networks," IEEE Transactions on Signal Processing, Vol. 53, No. 11, 4110-4124, 2005. doi:10.1109/TSP.2005.857028
6. Jayaprakasam, S., S. K. A. Rahim, and C. Y. Leow, "Distributed and collaborative beamforming in wireless sensor networks: Classifications, trends, and research directions," IEEE Communications Surveys & Tutorials, Vol. 19, No. 4, 2092-2116, 2017. doi:10.1109/COMST.2017.2720690
7. Ahmed, M. F. and S. A. Vorobyov, "Sidelobe control in collaborative beamforming via node selection," IEEE Transactions on Signal Processing, Vol. 58, No. 12, 6168-6180, 2012. doi:10.1109/TSP.2010.2077631
8. Liang, S., T. Feng, G. Sun, J. Zhang, and H. Zhang, "Transmission power optimization for reducing sidelobe via bat-chicken swarm optimization in distributed collaborative beamforming," IEEE International Conference on Computer and Communications (ICCC), 2164-2168, 2016.
9. Jayaprakasam, S., S. Rahim, L. C. Yen, and K. Ramanathan, "Genetic algorithm based weight optimization for minimizing sidelobes in distributed random array beamforming," IEEE International Conference on Parallel and Distributed Systems, 623-627, 2013.
10. Jayaprakasam, S., S. K. A. Rahim, C. Y. Leow, and T. O. Ting, "Sidelobe reduction and capacity improvement of open-loop collaborative beamforming in wireless sensor networks," PloS one, Vol. 12, No. 5, 1-33, 2017. doi:10.1371/journal.pone.0175510
11. Jayaprakasam, S., S. K. A. Rahim, C. Y. Leow, and M. F. M. Yusof, "Beampatten optimization in distributed beamforming using multiobjective and metaheuristic method," IEEE Symposium on Wireless Technology and Applications (ISWTA), 86-91, 2014. doi:10.1109/ISWTA.2014.6981202
12. Jayaprakasam, S., S. Rahim, and C. Y. Leow, "PSOGSA-explore: A new hybrid metaheuristic approach for beampattern optimization in collaborative beamforming," Applied Soft Computing, Vol. 30, 229-237, 2015. doi:10.1016/j.asoc.2015.01.024
13. Jayaprakasam, S., S. K. A. Rahim, C. Y. Leow, T. O. Ting, and A. A. Eteng, "Multiobjective beampattern optimization in collaborative beamforming via NSGA-II with selective distance," IEEE Transactions on Antennas and Propagation, Vol. 65, No. 5, 2348-2357, 2017. doi:10.1109/TAP.2017.2684187
14. Shi, W., Y. Li, L. Zhao, and X. Liu, "Controllable sparse antenna array for adaptive beamforming," IEEE Access, Vol. 7, 6412-6423, 2019. doi:10.1109/ACCESS.2018.2889877
15. Li, Y., Y. Wang, and T. Jiang, "Norm-adaption penalized least mean square/fourth algorithm for sparse channel estimation," Signal Processing, Vol. 128, 243-251, 2016. doi:10.1016/j.sigpro.2016.04.003
16. Li, Y., Y. Wang, and T. Jiang, "Sparse-aware set-membership NLMS algorithms and their application for sparse channel estimation and echo cancelation," AEU — International Journal of Electronics and Communications, Vol. 70, No. 7, 895-902, 2016. doi:10.1016/j.aeue.2016.04.001
17. Li, Y., Z. Jiang, W. Shi, X. Han, and B. Chen, "Blocked maximum correntropy criterion algorithm for cluster-sparse system identifications," IEEE Transactions on Circuits and Systems II: Express Briefs, 2019.
18. Yu, K., Y. Li, and X. Liu, "Mutual coupling reduction of a MIMO antenna array using 3-D novel meta-material structures," Applied Computational Electromagnetics Society Journal, Vol. 33, No. 7, 758-763, 2018.
19. Jiang, T., T. Jiao, and Y. Li, "A low mutual coupling MIMO antenna using periodic multi-layered electromagnetic band gap structures," Applied Computational Electromagnetics Society Journal, Vol. 33, No. 3, 305-311, 2018.