# Beyond REM: A New Approach to the Use of Image Classifiers for the Management of 6G Networks
## 1 Introduction  
The rapid progression of mobile communication technologies has induced several advancements that fundamentally transform methods of communication, work, and lifestyle. The transition from 5G to 6G networks is anticipated to bring transformative changes, introducing ultra-reliable low-latency communication, enhanced mobile broadband, and massive machine-type communication capabilities [1]. Within this evolving landscape, the concept of zero-touch networks has emerged as a critical paradigm designed to enhance the efficiency, flexibility, and scalability of complex systems while minimizing operational costs and human intervention [2, 3]. The zero-touch (ZT) network paradigm symbolizes the automation of network operations, management, and optimization processes. Powered by intelligent and autonomous systems, these are crucial for managing the scale and complexity of 5G and b5G networks, thereby ensuring optimal performance, resource utilization, and user experience. A critical element in the realization of zero-touch networks is real-time decision support tools that enable data-driven decision-making and swift adaptation to the dynamic and heterogeneous radio environment. Traditionally, tools such as coverage maps and Radio Frequency (RF) maps have been relied upon for network deployment, playing key roles in site selection, capacity planning,and interference management. These tools often fail to represent the dynamic and diverse radio environment adequately. To address this, Radio Environment Maps (REMs) were introduced [4]. Yet, despite their evolution, REMs face limitations in supporting zero-touch network operations due to their limited capacity to accurately reflect the real-time complexity of the wireless environment [5]. In parallel with the advancement of 5G standards, data processing systems and Machine Learning (ML) algorithms have significantly evolved [6; 7]. The integration of mature positioning technologies has opened up a new path. In this context, localization of User Equipment (UEs) is critical for identifying, locating, and classifying network failures. Ubiquitous UE localization, both outdoors and indoors, is anticipated, facilitated by information from global navigation satellite systems (GNSS) and the use of cellular-based precise location systems [8]. This localization data is critical to the context-aware paradigm, where variables impacting cellular performance, such as user distributions, are considered for network management. The integration of localized information into cellular management, particularly fault diagnosis, presents a series of challenges [9]. The collection and formatting of heterogeneous data generated by various network elements can be complex, and the analysis of such data in live systems introduces further difficulties. Understanding the interconnectivity of faults originating from multiple points in the network and deciphering causality in such extensive, complex systems can be daunting. Meanwhile, advancements in Deep Learning (DL) mechanisms, particularly Convolutional Neural Networks (CNNs), have been driven by their applications in fields such as automated driving, security, and health [10]. These fields heavily depend on image and video recognition and classification tasks. While DL algorithms have begun to penetrate autonomic network literature, there is a need for more comprehensive approaches that utilize image classifiers for this purpose [11]. Image classifiers, proficient in capturing, processing, and analyzing visual data, hold the potential to model the complexities and temporal variations of wireless networks effectively. They can reveal hidden cross-relations among variables, thereby reducing the need for ultra-precise location methods and opening up new opportunities for advanced image processing algorithms in mobile network management. Although the methodologies for creating REMs are rapidly evolving, they face significant challenges.