Proposal of Hybrid N OAM-MPPM Technique for Gamma-Gamma Turbulence Channel With Pointing Error and Different Deep Learning Techniques
| Autor: | Hossam M. H. Shalaby, Shimaa El-Meadawy, Abeer D. Algarni, Walid El-Shafai, Ahmed E. A. Farghal, Naglaa F. Soliman, Fathi E. Abd El-Samie, Nabil A. Ismail |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Free-space optic (FSO)
General Computer Science MathematicsofComputing_GENERAL multiple pulse-position modulation (MPPM) Computer Science::Digital Libraries Gamma gamma ComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATION pointing error (PE) General Materials Science Computer Science::Symbolic Computation Pointing error Physics Turbulence business.industry Deep learning High Energy Physics::Phenomenology General Engineering TK1-9971 TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES ComputingMethodologies_DOCUMENTANDTEXTPROCESSING Computer Science::Programming Languages orbital angular momentum (OAM) Artificial intelligence Electrical engineering. Electronics. Nuclear engineering business Algorithm Communication channel |
| Zdroj: | IEEE Access, Vol 10, Pp 10295-10309 (2022) |
| ISSN: | 2169-3536 |
| Popis: | A hybrid multi-state orbital angular momentum-multi pulse-position modulation ( $N$ OAM-MPPM) scheme over gamma-gamma free-space optical ( ${\Gamma \Gamma }$ -FSO) channel is studied in this paper. In our study, all atmospheric and pointing error impacts are taken into account. Expressions for the parameters of ${\Gamma \Gamma }$ -FSO-pointing error channel are derived. In addition, approximate-tight upper bounds on the bit-error rates (BERs) of $N$ OAM and $N$ OAM-MPPM techniques are developed over ${\Gamma \Gamma }$ -FSO-pointing error channels, considering the influences of beam divergence and pointing error (PE). The ${\Gamma \Gamma }$ -FSO-PE channel parameters and the BER expressions are evaluated numerically and verified by simulation. It turned out that the analytical results are nearly the same as those obtained from simulation under different turbulence scenarios and OAM modes. The results demonstrate that under variable turbulence conditions, the $N$ OAM-MPPM technique outperforms both ordinary $N$ OAM and MPPM systems. Furthermore, different deep learning (DL) techniques, namely random forest (RF), convolution neural network (CNN), and auto-encoder (AE), are employed to get the optimum classification accuracy using different datasets of $N$ OAM-MPPM over ${\Gamma \Gamma }$ -PE channel model. Finally, the results indicate that AE has the best performance metrics compared to other models using different datasets. |
| Databáze: | OpenAIRE |
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