## 3 Scope of This Survey
The two paradigms, half-duplex and full-duplex, were introduced, with an emphasis on the operating modes in the full-duplex. After that, they discussed the utilization of learning techniques to improve the performance of SS while considering both cooperative and local sensing strategies. In addition, they investigated the utilization of CR for 5G networks and beyond and its potential role in radio resource allocation. They also illustrated important future research in SS for CR based on wireless communications techniques. An optimization taxonomy on different aspects of RM is provided, and a comprehensive survey of RM methods in a C-RAN is presented [74]. Spectrum management, user assignment, power allocation, remote radio heads (RRH) selection, and capacity maximization were the critical elements for efficient RM in C-RAN. In order to further demonstrate the benefits of C-RAN technology, they showed emerging use-cases technology: HetNets, mmWave, full-duplex, and Non-Orthogonal Multiple Access (NOMA). At last, the paper presents several suggestions for open issues and future research. The 5G vehicular network's resource allocation is outlined with the help of conventional optimization and advanced machine learning (ML) techniques, in particular, a deep reinforcement learning (DRL) approach [75]. Additionally, they proposed a vehicle-to-vehicle communication system based on federated deep reinforcement learning (FDRL). They discussed the problems and opportunities for future research into 5G and beyond into 6G in vehicular networks. Network slicing and a distributed federated learning method are investigated as multiaccess edge computing. HetNet, carrier aggregation (CA), CR, mmWave, and massive MIMO are new technologies that must be used to achieve the desired results [76]. These methods are already used in 5G cellular networks, but their coexistence has created several spectrum management problems. This research provides a summary of these developing technologies. The study reviews cooperative communication across all spectrum management methods and potential issues with the proposed solutions. The future direction is also discussed to achieve the design of 6G networks. In [77], they presented different ML-based load-balancing approaches implemented in HetNets. This survey offers a roadmap for utilizing cost-effective and intelligent load-balancing strategies for future 6G networks. The available methods and solutions for the load balancing issue are investigated. The key performance indicators (KPIs) utilized in assessing the load balancing methods in HetNets are presented. Finally, the operational aspects of future load balancing are discussed, along with the existing challenges in implementing models. The summary of relevant survey articles is presented in Table 1, which also focuses on the research gaps relative to the proposed survey. The current studies on resource management have concentrated their attention on more recent strategies and methods. This review contributes to the existing research by providing an overview of the latest improvements in the resource management aspects of HetNets. Moreover, up-to-date prospective research issues are discussed in addition to the fundamental principle and theoretical analysis. In this overview, we present a summary to investigate existing RM approaches.