## 1 Introduction  
As the demands on the now deployed modern fifth-generation (5G) systems are becoming overwhelming, researchers are beginning to question their effectiveness for future technological evolution and development to meet user demands [1, 2]. It is eminent that each generation of cellular networks differs from the preceding generation or the one preceding it as improvements are made in their specifications and supporting applications [3]. The growing numbers of both users and the enormous number of connected intelligent devices have also put intense pressure on the existing 5G networks [2]. Technical issues and network demands beyond which the specification of 5G was initially designed for have overtaken its capacity [2, 4, 5], thereby paving the way for beyond 5G and the incoming sixth-generation (6G) systems [1, 6]. With the current 5G systems still awaiting full and widespread deployment in most parts of the world, stakeholders are starting to envisage the next technological advancement in 6G. Like its predecessors, 6G is anticipated to make new demands, requirements, and architectural adjustments in its bid to meet the technological evolutions of today's developing applications and services [2, 7]. The introduction of the 6G network is projected to take effect from 2030 [7; 8]. Consequently, a substantial number of 6G proposals have been investigated to define the 6G network from many aspects, ranging from its system requirements, network architecture, foundational/enabling technologies, envisioned services, privacy and security, novel application scenarios, and technical and open challenges [7; 9; 10]. These studies have covered fundamental aspects of the anticipated 6G system [1; 10; 11]. The discussions have ranged from requirements for 6G connectivity in intelligent healthcare systems [12], underwater access [9], SAGSI networks [13], positioning systems [14], emerging enabling technologies, and machine learning integrations [4; 15; 16]. Machine learning (ML) technology has proved to be an efficient integral part of wireless networks [17; 18]. ML approaches provide powerful problem-solving tools in wireless communication and are expected to dominate future research in wireless networks [16; 19]. ML is a branch of artificial intelligence (AI) that has made a tremendous technological breakthrough in wireless communication networks. It is expected that the 6G network can effectively be optimized by integrating several enabling technologies [20]. Within 6G communications, ML will play a critical role. Currently, virtually all new technologies that are developed are ML-enabled, and it is expected to grow even further [21] in the years to come. A table of abbreviations with their full meaning is presented in Table 1. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Abbreviation** & **Full Meaning** \\ \hline AHC & Agglomerative Hierarchical Clustering \\ \hline AI & Artificial Intelligence \\ \hline AMPS & Advanced Mobile Phone System \\ \hline ANN & Artificial Neural Network \\ \hline AR & Augmented Reality \\ \hline BER & Bit Error Rate \\ \hline BS & Base Station \\ \hline CAVs & Connected Autonomous Vehicles (CAVs) \\ \hline CDMA & Code-division Multiple Access \\ \hline CDNN & Causal-Deep Neural Network \\ \hline CSI & channel state information \\ \hline cGAN & Conditional Generative Adversarial Network \\ \hline DDPG & Deep Deterministic Policy Gradient \\ \hline DEETO & DRL-based Energy Efficient Task Offloading \\ \hline DL & Deep Learning \\ \hline DML & Distributed Machine Learning \\ \hline DNN & Deep Neural Network \\ \hline DQN & Deep Q-Learning \\ \hline DRL & Deep Reinforcement Learning \\ \hline DT & Digital Twin \\ \hline D2D & Device to Device \\ \hline eMBB & Enhanced Mobile Broadband \\ \hline EMs & Electromagnetic Waves \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Abbreviations and their full meaning.