## 1 Introduction  
It is anticipated in the year 2050 that more than two-thirds of the world population will live in urban areas, where advanced technologies should play a significant role to optimize the life-style in future smart cities. Free space optics (FSO) is one promising technology with an expected market size investment of $300 million in 2029 [1]. FSO has been extensively considered by various communication sectors including wireless communication networks, optical interconnect in data centers, underwater communications, and next generation Internet of things systems [2, 3, 4, 5]. This is owing to the unique features of FSO technology such as ease and low installation cost, high-throughput, long reach distance, and low link latency. In this regard, different light beam structures were used in data multiplexing and \(M\)-ary pattern coding applications [6, 7]. These include the traditional Gaussian beam and complex light structures such as Laguerre, Hermit, and Bessel Gaussian (LG, HG, and BG) mode families. Nonetheless, the wireless optical link is subject to propagation challenges that limit the FSO efficiency. These include intrinsic atmospheric conditions and extrinsic human-made risks (i.e., jamming and interception threats). In the former, the effect of atmospheric turbulence, rain, fog, and dust has been comprehensively investigated theoretically and by experimental demonstrations [8]. Additionally, traditional digital signal processing (DSP), adaptive optics (AO), and machine learning (ML) methods were used alternatively to mitigate atmospheric effects. For instance, the work in [9, 10] studied the mitigation of crosstalk-based turbulence using DSP for multiplexed LG modes propagatingin an emulated weak-turbulance channel. A 15-tap 4 \(\times\) 4 multi-input multi-output (MIMO) equalizer is used for four multiplexed LG channels each carrying 20 Gbps quadrature phase shift keying (QPSK) signal. In addition, the authors in [11] exploited AO components to alleviate atmospheric turbulence on the data multiplexed LG mode family, where pre- and post-compensation of weak and moderated turbulence is achieved using an AO feedback closed loop. Besides, ML-based methods have been used, broadly, as a classifier to identify structured light signals, in \(M\)-ary pattern coding systems, and as a regressor to predict various atmospheric conditions. In this regard, artificial neural network (ANN) has been utilized to identify 16-ary superposition LG modes transmitted over a strong turbulence channel, of 3 km link, in Vienna city [7]. Moreover, convolutional neural network (CNN) has been used as a classifier and regressor in [12] to identify 16-ary superposition LG modes and jointly predict the turbulence level, respectively. In [13], the k-nearest neighbour (kNN), support vector machine (SVM), and CNN have been compared to identify 8-, 16-, and 32-ary structured light beams generated using LG and HG mode families, in a dusty environment. Moreover, the CNN-based regressor was used to predict the visibility range of the communication channel. In [14], a chaotic interleaving processing step has been applied to 16-ary orbital angular momentum (OAM) states to mitigate FSO turbulence after coding the original transmitted bits using low-density parity-check codes and Turbo codes. The CNN-based classifier was used to identify various OAM-states with an achieved accuracy of 99% after optimizing CNN hyperparameters. In addition to the free space conditions, the underwater environment effects, such as water bubbles, temperature inhomogeneity, and water turbid, have been studied using CNN algorithm on single and superposition LG modes that form 16-ary OAM states [3]. It is worth noting that the immunity of ML-based approaches to environment (atmospheric or underwater) conditions has stimulated the recent interest in ML-based methods as an alternative option to traditional DSP and/or AO-based receivers [7]. On the other side, the investigation of extrinsic threats such as FSO beam tapping and jamming are still in its fancy.