الفهرس | Only 14 pages are availabe for public view |
Abstract One of the primary interests of the optical communication community is the achievement of greater data transmission capacity, which has led to the modification of the different physical light properties including amplitude, phase, wavelength, and polarization for data encoding and channel addressing. Multiplexing of multiple independent data channels is a common method for enhancing the transmission capacity of optical communication systems. The implementation of orthogonal spatially overlapping and co-propagating spatial modes, known as Mode-Division Multiplexing (MDM), is a particular case of Space-Division Multiplexing (SDM). Each mode can hold an independent data channel in such a scenario, and orthogonality allow multiple modes for efficient de/multiplexing and low inter-modal crosstalk. One of the most potential candidates for MDM systems is the Orbital Angular Momentum (OAM) due to its ability to improve the performance of optical communication systems. Advantages arise from the circular symmetry of the OAM modes relative to other MDM methods, making OAM modes well suited for several optical technologies. In this thesis, a novel chaotic-interleaver is used with Low-Density ParityCheck (LDPC) coded OAM-shift keying through Atmospheric Turbulence (AT) channel. Moreover, a Convolution Neural Network (CNN) is used as an adaptive demodulator to enhance the performance of the wireless optical system. The viability of the proposed system is verified by convoying a digital image in the presence of distinctive turbulence conditions with different codes. The proposed CNN is chosen with the optimal parameter and hyperparameter values that yield the highest accuracy, the utmost Mean Average Precision (MAP), and the largest value of Area Under Curve (AUC) for the different optimizers. The simulation results affirm that the proposed system can achieve better peak Signal-to-Noise-Ratios (SNRs) and lower Mean Square Error (MSE) values in the presence of different atmospheric turbulence conditions, when the CNN classification capability is restricted. By computing accuracy, MAP, and AUC of the proposed system, we realize that the Stochastic Gradient Descent with Momentum (SGDM) and the Adaptive Moment Estimation (ADAM) optimizers have better performance compared to the Root Mean Square Propagation (RMSProp) optimizer in terms of accuracy by about 3:8% .After that, we suggest 3D chaotic interleaving for coded 3D video frames with dissimilar spatial and temporal features transmitted via a variety of Nary Orbital Angular Momentum Shift-Keying Free-Space Optical (N-OAMSK-FSO) communication system. The LDPC-coded encrypted video frames have the highest Peak Signal-to-Noise Ratio (PSNR) and the lowest Bit Error Rate (BER) through N-OAM-SK-FSO model. Due to the defects of conventional OAM-SK detection mechanism, two efficient Deep Learning (DL) techniques, namely Recurrent Neural Network (RNN) and 3D-CNN are used to decode the OAM modes with a lower error rate in the presence of extreme atmospheric turbulence. The simulation results imply that both techniques have nearly the same classification and prediction performance through NOAM-SK-FSO model, but this performance is deteriorated in case of larger dataset classes. Moreover, Graphics Processing Unit (GPU) accelerates the classification performance by almost 67.64% and 36.93% using RNN and 3D CNN techniques, respectively. The two applied DL techniques are approximately more efficient than other conventional classification techniques by almost 18%. Furthermore, this thesis presents a hybrid multi-state Orbital Angular Momentum-Multi-Pulse-Position Modulation (NOAM-MPPM) technique over gamma-gamma Free-Space Optical (ΓΓ-FSO) channel and analyzes its performance. Both atmospheric and Pointing Error (PE) effects are taken into account in our analysis. In addition, approximate-tight upper bounds on the BERs of both NOAM and NOAM-MPPM techniques are developed, considering the influences of beam divergence and PE. The ΓΓ-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 conditions and OAM modes. The results demonstrate that under variable turbulence conditions, the NOAM-MPPM technique outperforms both ordinary NOAM and MPPM techniques. Finally, different DL techniques, namely Random-Forest (RF), CNN, and Auto-Encoder (AE), are employed to get the optimum classification accuracies on different datasets with NOAM-MPPM-ΓΓ-PE model. Our results indicate that AE has the best performance metrics of DL compared to other models on different datasets. The thesis also presents a novel bit-level OAM video frame encryption algorithm that is dependent on the Piecewise Linear Chaotic Maps (PWLCM) for transmission through different turbulence conditions. Firstly, the mathematical model for the BER of OAM is derived employing the ΓΓ turbulencechannel. After that, a comparison between the theoretical results from Mathematica and the simulation results from MATLAB under different turbulence strengths, SNRs, and propagation distances is presented to assure that there is a perfect match between both models. The proposed OAM video cryptosystem is checked via various security key indicators such as entropy analysis, histogram testing, attack analysis, time analysis, correlation testing, differential analysis, and other quality and security evaluation metrics. The simulation results and the performance analysis confirm that the proposed algorithm is reliable and secure for OAM video frame encryption and communication under different turbulence conditions in the FSO communication systems. |