# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 6 Contemporary Developments with UOC Technology
### AI in UOC
The CNN and gradient descent optimizer (GDO) were used at the feedback and correction path, respectively, and they were jointly called a feedback network. Atmospheric turbulence was simulated using the Kolmogorov phase with Von Karman spectrum effects and its strength was quantified in terms of refractive index Cn2 as  
\[\varphi(X,Y)=Real\left\{F^{-1}\left(C_{NN}\sqrt{\varphi_{NN}(k)}\right)\right\}, \tag{48}\]  
where \(\varphi_{NN}(k)=0.023r0-5/3(k2+k02)-11/6\ exp(-k^{2}/\text{km}^{2})\), r0 = (0.423k^{2}C_{n}{}^{2}Z)3/5\), \(X\), \(Y\) are Cartesian coordinates, F-1 is the inverse fast Fourier transform (FFT), CNN is a complex random normal number with zero mean and unit variance, and NN is the sampling grid size. A single turbulence phase screen, to mimic the given atmospheric effects/conditions in the communication channel at a given time, was used. Hoon Lee et al. developed a deep learning (DL)-based WOC system to mitigate channel effects. The developed DL technique was applied to multi-colored VLC and image sensor communication (ISC). Its performance was tested in terms of identifying the efficient transceiver pair in different link ranges and data rates. In the proposed DL technique, a convolution auto-encoder and decoder pair were used at the Tx and receiver arrays, respectively. These two coders were constructed using an ANN structure and used to generate the codeword at the Tx and reconstruct the data from that codeword at the receiver so that the output is like the input [462]. R. J. Dickenson and Z. Ghassemlooy proposed an ANN-based high-data-rate detection mechanism for indoor infrared optical communications. The ANN was combined with the continuous wavelet transform (CWT) and used at the receiver for feature extraction (using CWT) and pattern recognition (using ANN). The detection accuracy of the CWT-ANN was compared with a matched filter and MMSE equalizer. The simulation and experimental results were analyzed in terms of SNR, delay spread, bit duration, and BER. The results showed the outperformance of the CWT-ANN technique [463]. RJ Dickenson and Z. Ghassemlooy also proposed an AI-assisted signal detection for VOCs. A multipath IR channel was modeled, and the respective attenuation values and delay-spread for different modulation schemes were given to the AI algorithm. Wavelet coefficients were derived based on the pulse period to fit within a time window of apt duration and to appropriately sample at the receiver. The absolute and variable threshold values of the correlation coefficients were used at a sampling interval to detect data at a rate of 40 Mbps. The translated wavelet functions were correlated with the received time domain signal and to reconstruct the data with fixed and variable decision thresholds [464; 465]. B. Frackerton et al. presented a novel design using neural networks (NNs) to improve the detection performance of an optical detector. A concept of angle-diversity image receivers with a neural network was implemented. The receiver consisted of a multipath detector array, an FOV lens, a pixilated detector array, and an NN. The output of the NN layer was sent to a decision device [466]. AI-assisted FSO systems are smarter, friendlier, and more highly sensitive to the changes in their environment. AI techniques with suitable learning algorithms can suggest the most suitable Tx and Rx subsystems parameters, just by inputting atmospheric parameter variations, geographical location, and modulation technique, yielding an optimized system installation at the place of interest. Therefore, AI is expected to play a vital role in the design of optimal FSO communication systems/networks nowadays. Furthermore, a promising research challenge is the design of on-chip AI networks that will be very small in size/volume and will perform efficient computations within less time and with less power consumption. Multiple hardware platforms/chips are being developed for the real-time implementation of AI algorithms. Google has launched an AI chip calleda \"tensor processing unit (TPU)\".