## 4 Review and Survey Articles
The first section of this article provides an overview of the connected services managed by the 5G and 6G networks. After that, we'll talk about existing architectures. The essential concepts of self-organizing networks were reviewed and explored in four publications [19; 20; 21; 22]. In addition, provide a taxonomy for self-organizing networks use cases and describe their underlying ML techniques, as well as service management by demonstrating how combining ML algorithms with SDN and NFV on a variety of use cases and scenarios can yield insights, detect meaningful events and conditions, and enable the management system to respond appropriately. NMA was discussed in one publication [23] as a way to improve the level of intelligence in network elements. Two papers [24; 25] investigated resource allocation in 5G communications utilising RL, as well as a dynamic programming framework for solving RA problems optimally across different network states. Three publications [26; 27; 28] examined big data in the context of 5G and machine learning. The vast data can be divided into two categories: raw data and correct data. If the right data is extracted efficiently from such a large amount of raw data, 5G can be optimised. Big data analytics has evaluated the main taxonomy of machine learning and the fresh trends that could enable this exploitation of data to get network insight a reality. One study [29] examined open data choices as well as alternate ways to obtain data from networks that would otherwise be unavailable to academics. On the IoT side, data storage and synchronisation, cloud storage, the base control system, third-party apps, and the management interface are all available. Two articles [30; 31] introduced the potential of AI in the next-generation wireless networks, ranging from basic learning algorithms such as ML, DL, and others, to help meet the varying requirements of the 5G standards, such as operating in a fully automated manner, meeting increased capacity demand, and providing superior QoE to users. Only one article [32] looked at the application of machine learning to fault management in cellular networks from an operational standpoint. Based on the building components of a typical fault management system, describe the applicable ML approaches through to DL, and assess the progress that has been made in their implementation. The goal is to combine 5G technology with machine learning to make these technologies more accessible to a wider variety of consumers. Three articles [33; 34; 35; 36] used an empirical analysis of resource management efficiency in network slicing to investigate this trade-off. The results are based on extensive measurement data acquired in a live mobile network and provide insight into the efficiency of network slicing architectures, their dimensioning, and their interaction with resource management algorithms. By presenting several case examples, discussing the problems, and shedding fresh light on future research paths for using AI. in 5G wireless communications, four articles [37; 38; 39; 40] offered an in-depth assessment of AI and ML for 5G wireless communication systems and networking. One publication [41] looked into the potential features of B5G, as well as future research directions for how machine learning might help realise B5G. Others covered a variety of strategies and platforms that make it easier to install DL on mobile devices. Then, using DL to organise mobile and wireless networking research into different categories, present an encyclopaedic review of the field [42]. Three articles [43; 44; 45] presented a systematic overview of 6G that focuses on prospects and development, core techniques, applicable scenarios, and challenges, and proposes a framework for the 6G network, as well as an overview of the first five generations of wireless systems, followed by a survey on the 6G wireless network, as well as a discussion on the possible requirements and challenges. Specifications for 6G antennas for various applications are highlighted in [46].