The trade-off between more user bandwidth and quality of service requirements introduces unprecedented challenges to the next generation smart optical networks. In this regard, the use of optical performance monitoring (OPM) and modulation format identification (MFI) techniques becomes a common need to enable the development of next-generation autonomous optical networks, with ultra-low latency and selfadaptability. Recently, machine learning (ML)-based techniques have emerged as a vital solution to many challenging aspects of OPM and MFI in terms of reliability, quality, and implementation efficiency. This paper surveys ML-based OPM and MFI techniques proposed in the literature. First, we address the key advantages of employing ML algorithms in optical networks. Then, we review the main optical impairments and modulation formats being monitored and classified, respectively, using ML algorithms. Additionally, we discuss the current status of optical networks in terms of MFI and OPM. This includes standards, monitoring parameters, and the available commercial products with their limitations. Second, we provide a comprehensive review of the available ML-based techniques for MFI, OPM, and joint MFI/OPM, describing their performance, advantages, and limitations. Third, we give an overview of the exiting ML-based OPM and MFI techniques for the emerging optical networks such as the new fiber-based networks that use future space division multiplexing techniques (e.g. few-mode fiber), the hybrid radioover-fiber networks, and the free space optical networks. Finally, we discuss the open issues, potential future research directions, and recommendations for the potential implementation of MLbased OPM and MFI techniques. Some lessons learned are presented after each section throughout the paper to help the reader identifying the gaps, weaknesses, and strengths in this field.

5G wireless networks promise to provide massive bandwidth for various types of connections. In such networks, the backhaul/fronthaul sections should be easy to deploy and support the required high bandwidth. To improve the free space optic (FSO) link bandwidth so that it can replace fiber cables and support 5G networks, all-optical FSO systems were proposed which exploit advanced modulation formats in the transmitter side and coherent detection in the receiver side. However, such links will suffer much under harsh outdoor environment, especially under fog and dust conditions than traditional FSO links that have limited bandwidth. Effect of fog on such links has been investigated in literature. However, dust effect is not covered. In this study, we first experimentally analyzed the effects of dust storms on the performance of an all-optical FSO link carrying a 1-Gbaud/16-quadrature amplitude modulation 5G signal. The results demonstrate that the all-optical FSO link is significantly affected by low visibility range, with severe bit-error-rate (BER), and error vector magnitude (EVM) limits appearing at a 50-m visibility range for a 2.7-m channel length. For a visibility range greater than 200-m, the BER and EVM were improved to 10-9 and 5.5% of the root mean square, respectively. Furthermore, the analysis showed that the dust storm condition introduces flat fading over the frequency range under study, i.e. 21 GHz–29 GHz. Second, a comparison between FSO and radio frequency (RF) channels under the same dusty conditions was performed. The results showed that the effects of the dust storm are negligible for the RF link which makes it suitable as a backup for FSO link under sever dust conditions. Finally, a hybrid cascaded FSO/RF link was installed and analyzed in terms of visibility range, BER, and EVM.

Free space optic (FSO) is a type of optical communication where the signal is transmitted in free space instead of fiber cables. Because of this, the signal is subject to different types of impairments that affect its quality. Predicting these impairments help in automatic system diagnosis and building adaptive optical networks. Using machine learning for predicting the signal impairments in optical networks has been extensively covered during the past few years. However, for FSO links, the work is still in its infancy. In this paper, we consider predicting three channel parameters in FSO links that are related to amplified spontaneous emission (ASE) noise, turbulence, and pointing errors. To the best of authors knowledge, this work is the first to consider predicting FSO channel parameters under the effect of more than one impairment. First, we report the performance of predicting the FSO parameters using asynchronous amplitude histogram (AAH) and asynchronous delay-tap sampling (ADTS) histogram features. The results show that ADTS histogram features provide better prediction accuracy. Second, we compare the performance of support vector machine (SVM) regressor and convolutional neural network (CNN) regressor using ADTS histogram features. The results show that CNN regressor outperforms SVM regressor for some cases, while for other cases they have similar performance. Finally, we investigate the capability of CNN regressor for predicting the channel parameters for three different transmission speeds. The results show that the CNN regressor has good performance for predicting the OSNR parameter regardless of the value of transmission speed. However, for the turbulence and pointing errors, the prediction under low speed transmission is more accurate than under high speed transmission.

Progress in optical wireless communication (OWC) has unleashed the potential to transmit data in an ultra-fast manner without incurring large investments and bulk infrastructure. OWC includes wireless data transmissions in three optical sub-bands; ultraviolet, visible, and infrared. This paper discusses installing infrared OWC, known as free space optics (FSO), systems on top of installed radio frequency (RF) networks for outdoor applications to benefit from the reliability of RF links and the unlicensed broad optical spectrum, and the large data rates carried by laser beams propagating in free space. We equally review commercially available solutions and the hardware requirements for RF and FSO technology co-existence. The potential of hybrid RF/FSO for space communication is further discussed. Finally, open problems and future research directions are presented.

Transmission systems that use advanced complex modulation schemes and digital coherent receivers, have been driving the growth of optical communication networks for nearly a decade. In this fact, new strategies for network diagnosis and management are developed. In this work, we propose and experimentally demonstrate a cost-effective technique that not only exhibits optical performance monitoring (OPM) functionalities but also enables simultaneous symbol rate and modulation format identification. It is implemented for both dual-polarized quadrature phase shift keying (DP-QPSK) and 16-quadrature amplitude modulation (DP-16QAM) at various symbol rates. Our approach is based on artificial neural networks (ANNs) in conjunction with asynchronous amplitude histogram (AAH) evaluation. Our ANN-based pattern recognition algorithm, with all of the features of AAHs, facilitates both symbol rate and modulation format classification at high data rates. Experimental investigation demonstrates accurate classification of advanced modulation formats, in the presence of various link impairments. The latter include chromatic dispersion (CD) varying from 200 to 1600 ps/nm, differential group delay (DGD) between 10 and 70 ps, phase noise (PN) varying from 10 kHz to 10 MHz and amplified spontaneous emission (ASE) noise. The results of the above method demonstrate multi-impairment monitoring over wide monitoring ranges. At high symbol rates, after a precise extraction of monitoring parameters (i.e., FCD and FOSN R), our approach achieved a minimum dispersion parameter close to 0.2 dB and a maximum performance parameter of 6 dB. Our technique is robust and transparent for both the symbol rates and the modulation formats.

The unique orthogonal shapes of structured light beams have attracted researchers to use as information carriers. Structured light-based free space optical communication is subject to atmospheric propagation effects such as rain, fog, and rain, which complicate the mode demultiplexing process using conventional technology. In this context, we experimentally investigate the detection of Laguerre Gaussian and Hermite Gaussian beams under dust storm conditions using machine learning algorithms. Different algorithms are employed to detect various structured light encoding schemes including the use of a convolutional neural network (CNN), support vector machine, and k-nearest neighbor. We report an identification accuracy of 99% under a visibility level of 9 m. The CNN approach is further used to estimate the visibility range of a dusty communication channel.