## 2 Related Works  
Empirical methods try to build a model by using curve fitting techniques based on the measurement data collected in a representative path loss environment. They usually include carrier frequency, the distance between the transmitter (Tx) and receiver (Rx) (i.e., the TR-separation), and the path loss element as their main parameters. Although the simplicity of empirical methods has made them popular, their accuracy of prediction is often limited and not adequate for cell planning and optimization purposes [16]. A CNN-based millimeter wave path loss model for suburban areas, where heights of buildings are generally low, was proposed in [10]. Additionally, they proposed Enhanced Local Area Multi-Line Scan (E-LAMS) algorithms to extract topographic information within the path loss environment between Tx and Rx as an image, and to use it as input for 2D CNN. The proposed model has four subnetworks and feature-sharing layers, predicting path loss values for four directional antennae mounted on the receiver. The feature sharing layer used in this model shares common knowledge among subnetworks, and it allows generalization. As a result of comparing the path loss model proposed in [10] with the state-of-the-art empirical path loss model, the path loss model proposed in [10] proved to have better performance. However, since this was only considered in a suburban area, it is not clear that performance in urban areas, with varying heights of buildings, will be guaranteed. A 3D CNN-based path loss model for urban areas, which convert and use tabular data converted into image was proposed in [17]. In this paper, various feature data vectors of tabular data were spread to various pixels, and in the process, the importance of specific features was calculated and arranged to correspond. The generated image was fused to be a compound pseudo image to be used at the input of the CNN. Although this method of generating 3D CNN input images has been shown to achieve performance gain, there was a lack of explanation on how to extract features, such as tabular vectors. FadeNet, which can predict large-scale fading from the base station to each location within the coverage area based on CNN, was proposed in [18]. FadeNet mainly aims to plan and optimize 5G mmWave cellular networks. They collected and used measured data of mmWave cells in multiple sites. They were able to utilize parallel processing units of graphics processing units to reduce prediction time by 40\(\times\)-1000\(\times\), compared to ray tracing methods. In terms of accuracy, the prediction accuracy of the proposed FadeNet was higher than distance dependent prediction and conditional least squares prediction. The 3D CNN-based model proposed in [12] aimed to achieve a similar level of performance as in suburban and indoor environment scenarios even in dense urban scenarios, with various morphological characteristics representing many high-rise buildings of various heights and complex street networks. As the input of the 3D CNN model, a 3D image, including building and terrain information, was used, and the RSRP data measured at seven base stations were used as the output. The use of 3D CNN model technique proved that 3D is more advantageous than 2D for CNN-based path loss prediction in complex urban environments. CNN with Meta-learning is based on meta-learning, which performs well in few-shot learning scenarios with multiple tasks constituting meta-tasks [19; 20; 21]. The CNN-based indoor and outdoor path loss model composed of multi-beam meta-tasks outperforms the CNN model, not only in the empirical model but also in the conventional training algorithm. Meta-learning, also known as \"learning to learn\", means designing a model to learn new skills or quickly adapt to a new environment through several training tasks. The meta-learning techniques that motivated [19; 20; 21] are Model-Agnostic Meta-Learning (MAML) and Reptile [22; 23].