## 1 Introduction
For instance, in [15] the alleviation of FSO tapping has been studied using free space spatial diversity and encoder/decoder techniques. Moreover, the effect of jamming on standard Gaussian beam is analytically investigated in [16; 17]. In [16], the jamming effect on the bit error rate (BER) performance of FSO system is studied using numerical simulation. In addition, in [17] the FSO channel outage probability is evaluated under jamming effect, where jamming mitigation is investigated by proposing a multi-input-single-output (MISO) FSO system. Moreover, a closed-form expression of the average BER has been obtained in [18] for a 2 \(\times\) 1 MISO optical space shift keying system under jamming effect and in a turbulent FSO channel. So far, these methods are based on numerical analysis to mitigate and/or study FSO system under jamming; however, ML-based techniques are not yet reported in literature for extrinsic free space challenges. Table 1 shows the recent progress of ML-based identification for M-ary pattern coding under various channel conditions, where intrinsic channel conditions are considered more. It is worth noting that, to the best of authors knowledge, no work has been yet reported in literature that investigates the jamming effect on complex structured light beam mode families using ML-based techniques. Therefore, in this paper we experimentally investigate the effect of jamming signal on LG, superposition-LG, and HG mode families. As shown in Figure 1, structured light patterns can be used to transfer information bits between two buildings; however, a simple standard Gaussian jammer can be used to corrupt the pattern at the receiver side. In particular, the following is considered in this work:  
1. An FSO pattern coding system is experimentally built to generate 8-ary LG, 8-ary superposition-LG (we call it Mux-LG), 16-ary LG and Mux-LG, 16-ary HG, and 32-ary formed by all considered modes. The identification accuracy of different mode families is assessed, in a direct detection FSO system, using CNN-based classifier and under signal-to-jammer ratio (SJR) ranging from \(-5\) to \(3\) dB. The direction-of-arrival (DoA) of the jamming signal is determined using CNN- and linear discriminant analysis (LDA)-based classifiers. The rest of paper is organized as follows. Section 2 presents the structured representation of LG and HG mode families. The experimental setup and data set collection are discussed in Section 3. CNN- and LDA-based classifiers are introduced in Section 4. The experimental results and their analysis are reported in in Section 5. Section 6 discusses the limitations and the practicability of the proposed system. Finally, Section 7 provides concluding remarks. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & **Mode** & **Mode** & **Challenge** & **ML** & **Link** & **Sim./Exp.** \\  & **Family** & **Order** & **Type** & **Method** & **Distance** & **Sim./Exp.** \\ \hline
[7] & LG & 16-ary & AT & ANN & 3 km & Exp. \\ \hline
[12] & LG & 16-ary & AT & CNN & 1 km & Sim. \\ \hline
[13] & LG and & 8-, 16-, & AD & KNN, SVM, and CNN & 1 m & Exp. \\ \hline
[14] & LG & 16-ary, & AT & CNN & 1 km & Sim. \\ \hline
[3] & LG & 16-ary & TW, BW, and TIW & CNN & 1.5 m & Exp. \\ \hline This & LG and & 8-, 16-, & 32-ary & Jamming & CNN and LDA & 1 m & Exp. \\ \hline \hline \end{tabular}  
* Sim.: Simulation, Exp.: Experimental, AI: Atmospheric turbulence, AD: Atmospheric dust, TW: Turbid water, BW: Bubbly water, TIW: Temperature inhomogeneity water, LG: Laguerre-Gaussian, HG: Hermite-Gaussian, ANN: Artificial neural network, CNN: Convolutional neural network, kNN: k-nearest neighbour, SVM: Support vector machine, and LDA: Linear discriminant analysis.  
\end{table} 
Table 1: ML-based identification of M-ary light pattern coding systems under different transmission challenges. Figure 1: Structured light communication patterns between buildings with extrinsic Gaussian profile jammer.