## 4 Resource Management in Heterogeneous Network (RM HetNet)  
The demand for wireless communications services has increased due to the recent research of having mobile device connectivity at any time and everywhere, which has sped up the deployment of wireless networks with various service capabilities. Massive connectivity will be a key component of 6G systems in the future between devices via the cellular network, equipment via HetNet, and D2D features [78]. Effective resource management strategies are crucial to achieving these demands, especially in HetNets. Resource management, which dynamically allots available resources with different constraints, such as EE, SE, and QoS, is a popular strategy in resource management schemes. Artificial intelligence (AI) is a crucial enabler for the future generations of 6G mobile networks, and assuring effective resource management is a significant aspect of realizing the 6G vision [79]. The 6G networks powered by AI can provide robust and intelligent resource management solutions [80]. It is anticipated that 6G networks would efficiently handle high-quality services, new-and-emerging applications (such as virtual reality and augmented reality, holographic technology, and remote surgery), and infinite connection for the vast numbers of smart devices [81]. In this section, we go through the mechanisms used in recent RM methods, and we classify those methods further based on different concepts. The resource management aspects are illustrated in Figure 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Survey** & **Year** & & **Summary** \\ \hline \multirow{6}{*}{[74]} & \multirow{6}{*}{2020} & \multirow{6}{*}{*} & Provided a detailed optimization taxonomy for resource allocation in a C-RAN. \\  & & & Spectrum management, user assignment, remote radio head selection, throughput maximization, network utility, and power allocation are the key elements of efficient resource management. \\  & & & HetNet, mmWave, virtualized, NOMA, and full-duplex use cases were shown to improve performance. \\  & & & Several open issues and future directions are discussed. \\ \hline \multirow{6}{*}{[75]} & \multirow{6}{*}{2021} & \multirow{6}{*}{*} & \multirow{6}{*}{The 5G vehicular network’s resource allocation is shown using traditional optimization and advanced machine learning methods, especially deep reinforcement learning. \\  & & & Discussing 5G and 6G vehicular network problems, open issues, and future directions. \\  & & & Network slicing and distributed federated learning are used to analyze multiaccess edge computing. \\ \hline \multirow{6}{*}{[76]} & \multirow{6}{*}{2020} & \multirow{6}{*}{*} & \multirow{6}{*}{To achieve objectives, presented new methods and technologies such as CR, HetNet, mmWave, carrier aggregation, and massive MIMO. \\  & & & Provides a detailed review of cooperative communication among all spectrum management techniques, and \\  & & & potential problems are addressed. \\  & & & Research challenges are also discussed to achieve 6G wireless network design goals. \\ \hline \multirow{6}{*}{[77]} & \multirow{6}{*}{2022} & \multirow{6}{*}{A survey of HetNets’ machine learning (ML) and generic problem-based intelligent load balancing models. \\  & & & Provides a guideline for developing cost-effective and \\  & & & intelligent load-balancing methods in future HetNets. \\  & & & HetNet load balancing models and future key features are presented. \\ \hline \multirow{6}{*}{This survey} & \multirow{6}{*}{2022} & \multirow{6}{*}{This article comprehensively analyzes the current work in resource management aspects such as power allocation, user association, mode selection, and \\  & & & spectrum allocation. \\ \cline{1-1}  & & & Highlight the most significant issues with the current resource management approaches and present the best solution to overcome the current issues. \\ \cline{1-1}  & & & Presents future directions and research challenges that have yet to be \\ \cline{1-1}  & & & adequately addressed. \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._