A novel bio-inspired optimization algorithm for solving peak-to-average power ratio problem in DC-biased optical systems
•We introduce a novel nature-inspired optimization algorithm, called tree growth optimization (TGO) algorithm to overcome the search complexity of the partial transmit sequence.•We apply the proposed algorithm to the DC-biased optical signals to reduce the high peak-to-average power ratio.•The propo...
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| Veröffentlicht in: | Optical fiber technology Jg. 60; S. 102383 |
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| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier Inc
01.12.2020
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| Schlagworte: | |
| ISSN: | 1068-5200, 1095-9912 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | •We introduce a novel nature-inspired optimization algorithm, called tree growth optimization (TGO) algorithm to overcome the search complexity of the partial transmit sequence.•We apply the proposed algorithm to the DC-biased optical signals to reduce the high peak-to-average power ratio.•The proposed TGO algorithm is a multi-agent algorithm that simulates the effort of trees to grow and survive in nature.•The TGO helps to find a near-optimal set of phase factors among a large set of phase factors in solution space.•The performance of the proposed algorithm is evaluated using a set of benchmarks and compared with several counterpart methods. The results reveal that the proposed algorithm outperforms its counterparts.
In this paper, a tree growth optimization (TGO) algorithm is introduced to diminish the computational complexity of the partial transmit sequence in exploring the optimal phase factors. The proposed TGO algorithm is an efficient method for reducing the high peak-to-average power ratio of optical orthogonal frequency division multiplexing signals. The problem of the peak-to-average power ratio causes inter-modulation between sub-carriers due to the non-linearity of the fiber optics and some devices such as power amplifiers and analog-to-digital converter. The performance of the proposed algorithm is evaluated using a set of benchmarks and compared with several counterpart methods. The results reveal that the TGO outperforms its counterparts in terms of solution quality and computational complexity. |
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| ISSN: | 1068-5200 1095-9912 |
| DOI: | 10.1016/j.yofte.2020.102383 |