# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 6 Contemporary Developments with UOC Technology
### AI in UOC
In uncertain conditions, ML relies on (i) supervised learning--learning from mapping input with output; (ii) unsupervised learning--learning from the input patterns; and (iii) reinforcement learning--learning from continuous rewards and punishments [449; 450]. In advanced modulation schemes, such as 16-QAM, 64-QAM, and beyond, the synchronization of modulation carrier frequency and phase information is very critical. Bayesian filtering, adaptive control algorithms, simulated annealing, and genetic algorithms are widely used to provide autonomous control to the modulator in accordance with environmental changes. The results prove that the AI-assisted schemes outperform conventional approaches [451; 452; 453]. In Ref. [454], the author proposed a novel approach for autonomous amplifier gain adjustment utilizing a multilayer perceptron neural network. Artificial neural network (ANN)- and Kalman-filter-assisted optical performance monitoring, such as polarization tracking and carrier-phase tracking using parameters of the received bits' eye-diagram and eye-histogram, are presented in [455; 456]. An ANN was adopted and utilized to estimate signal fades in FSO communication in [445]. In Refs. [445; 446; 447; 448], an ANN was used to evaluate the statistics of temporal link attenuation changes, i.e., temporal fades. The neural network used in [445; 457] had multiple layers and a nonlinear sigmoidal activation function. In [445], data corresponding to meteorological parameter variations and their corresponding FSO data link attenuations were collected over a long period, normalized into the interval <0,1>, and then used as input to train and test the developed ANN. The cancellation of system noise, A/D conversion noise, and voltmeter noise were accomplished while collecting atmospheric and FSO attenuation data [444; 445; 446]. Two types of delays, i.e., delaying atmospheric and attenuation data for different amounts of time, were applied at the input of an ANN to test the performance of ANNs as regular, nonlinear, and auto-regressive networks. Experimental results were presented which show that the developed ANN predicted the receiver power well in accordance with the atmospheric changes, and the prediction results maintain a good correlation with the measured signal strength [458]. Timothy Doster and Abbie T. Watnik proposed a machine-learning-based approach to demultiplex the OAM-carrying FSO beams at the receiver. The beam intensity pattern was captured as an image and then used to train a convolution neural network (CNN) that acted as a classifier. The CNN-based OAM demultiplexing technique simplifies the operational requirements, reduces alignment accuracy, and relaxes the orthogonality constraints [459]. A 256 \(\times\) 256 pixel collection window camera was used to capture an image in different atmospheric conditions, generated using the Kolmogorov turbulence model [460]. Experiments were conducted at Tulane University in two configurations, namely configuration 1--SLMs and conjugate demultiplexed images (total 32 sub-images) for every code of 5 bits, and configuration 2--only the proposed CNN structure for all the codes. The experimental results proved that the proposed CNN-based technique demultiplexed the transmitted codes with a >99% accuracy, even in high turbulence condition. To mitigate the impacts of atmospheric turbulence, Sanjaya Lohani and Ryan T. Glasser developed a feedback network using machine learning techniques [461]. In their work, the optical mode profile of the received image was compared with the desired images and the deviation profile was inputted to the feedback machine learning network to deform the transmitted image, such that the corrected image was obtained at the receiver in the subsequent iterations, as shown in Figure 39b. An ANN was designed and used to measure the intensity profile of the distorted FSO beam, which made the measurement simple and robust.