Evolution strategy for collaborative beamforming in wireless sensor networks
The aim of this research is to improve the efficiency of the phase synchronisation algorithm in order to achieve collaborative beamforming (CB) in wireless sensor networks (WSNs). Generally, CB uses a group of distributed wireless sensor nodes, which collectively transmit a common message with diffe...
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| Format: | Thesis |
| Language: | English English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://eprints.ums.edu.my/id/eprint/41464/1/24%20PAGES.pdf https://eprints.ums.edu.my/id/eprint/41464/2/FULLTEXT.pdf |
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| Summary: | The aim of this research is to improve the efficiency of the phase synchronisation algorithm in order to achieve collaborative beamforming (CB) in wireless sensor networks (WSNs). Generally, CB uses a group of distributed wireless sensor nodes, which collectively transmit a common message with different proper weights to an intended location. This group of distributed wireless sensor nodes intrinsically act as a set of virtual antenna array and inherit the natural highly directional transmission properties from conventional antenna array. However, distinct of conventional antenna array, each sensor node in CB has an independent local oscillator. It becomes a vital problem to achieve CB as the distributed sensor nodes are unaware of their phase relationship. An iterative algorithm using evolution strategy (ES) is proposed to achieve phase alignment at the intended location in static channels, which require one-bit feedback from the receiver destination. By implementing ES in phase synchronisation, each sensor node independently adjusts its phase perturbation size accordingly to speed up the phase synchronisation. Evaluations have been carried out through simulation and result show that the performance using ES is improved by 18.7 % convergence speed as compared to the conventional one-bit feedback (C1BF) approach. In addition, inverse phase perturbation is introduced for the improved ES (IES) which further improved the convergence speed by 31.6 % over the C1BF approach. Adaptive-IES is proposed for time-varying channels and the results show that the Adaptive-IES has the ability to detect channel changes. Therefore, it can be concluded that the proposed algorithm is robust in practical implementation. |
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