## 1 Introduction
The extraction of elevation and azimuth angles from CIR to distinguish the NLoS and LoS scenarios in urban places was performed in [11]. Also, an accurate classification performance was achieved using convolutional networks in the problem of fingerprint feature extraction and classification [12]. The authors of [13] demonstrated via their ML approaches that supervised classification algorithms and unsupervised learning clustering algorithms can be effective classification strategies for scenario identification. Figure 1: ML concern in physical, middle, and end-user layers. In a previous work [14], we introduced an enhanced feature-selection process based on the regularization concept to enhance the classification performance of [14] and reduced the computational complexity of the ML algorithms with high generalization ability. The authors of [15] initiated the problem formulation of classification of typical terrestrial scenarios. However, the latency of transition and classification was not their concern. Therefore, to iterate upon their results, the time consumption of the preprocessing workflow and ML classification time are crucial issues to address. Minimizing the preprocessing procedure computations and the classification time of the algorithm are required to have quick scenario identification when a transition between multiple scenarios occurs. The ROC curve is important as an evaluation parameter as it contains both true positive rate (TPR) and false positive rate (FPR). The main motivation for this work is enhancing recent work [14; 15] in terms of preprocessing time and performance of the wireless communication scenario identification. The proposed method has considered the latency of the classification scheme. Hence, this paper contributes the following:  
1. Reduction of the model response and latency for the preprocessing workflow of each regularization technique instruction used in the previous model [14]. The previous model adopted ElasticNet without studying the time consumption. In this work, the performance and time efficiency results prove that adopting the LASSO is more suitable than ElasticNet. It achieves the same feature-selection performance of ElasticNet but in much less time. 2. Calculation of the classification time of KNN, SVM, k-Means, and GMM. The training phase and testing phase runtimes are computed and compared for both supervised algorithms (KNN and SVM). The formulation of clusters and the \"fit and predict\" runtime for the unsupervised learning k-Means and GMM are revealed. 3. Calculation and study of the ROC curves and AUC scores for each class in each model. Both ROC curves and AUC scores of the classes are calculated as one over all, where the evaluation is taken as a binary such that every class is distinguished from the others (e.g., the RMa LoS represented as '1' versus the other classes presented as '0'). The rest of this paper is organized as follows. Section 2 provides information about the dataset used in this research, the model specification, and preprocessing and processing procedures. Section 3 shows the results and discussion of the preprocessing and classification phases, including time and ROC curves. Section 4 is devoted to the main conclusions.