Proposal of Hybrid NOAM-MPPM Technique for Gamma-Gamma Turbulence Channel With Pointing Error and Different Deep Learning Techniques

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.