The explosive popularity of small-cell and Internet of Everything devices has tremendously increased traffic loads. This increase has revolutionised the current network into 5G technology, which demands increased capacity, high data rate and ultra-low latency. Two of the research focus areas for meeting these demands are exploring the spectrum resource and maximising the utilisation of its bands. However, the scarcity of the spectrum resource creates a serious challenge in achieving an efficient management scheme. This work aims to conduct an in-depth survey on recent spectrum sharing (SS) technologies towards 5G development and recent 5G-enabling technologies. SS techniques are classified, and SS surveys and related studies on SS techniques relevant to 5G networks are reviewed. The surveys and studies are categorised into one of the main SS techniques on the basis of network architecture, spectrum allocation behaviour and spectrum access method. Moreover, a detailed survey on cognitive radio (CR) technology in SS related to 5G implementation is performed. For a complete survey, discussions are conducted on the issues and challenges in the current implementation of SS and CR, and the means to support efficient 5G advancement are provided.

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In the future, as populations grow and more end-user applications become available, the current traditional electrical distribution substation will not be able to fully accommodate new applications that may arise. Consequently, there will be numerous difficulties, including network congestion, latency, jitter, and, in the worst-case scenario, network failure, among other things. Thus, the purpose of this study is to assist decision makers in selecting the most appropriate communication technologies for an electrical distribution substation through an examination of the criteria’s in-fluence on the selection process. In this study, nine technical criteria were selected and processed using machine learning (ML) software, RapidMiner, to find the most optimal technical criteria. Several ML techniques were studied, and Naïve Bayes was chosen, as it showed the highest performance among the rest. From this study, the criteria were ranked in order of importance from most important to least important based on the average value obtained from the output. Seven technical criteria were identified as being important and should be evaluated in order to determine the most appropriate communication technology solution for electrical distribution substation as a result of this study.

Resource optimisation is critical because 5G is intended to be a major enabler and a leading infrastructure provider in the information and communication technology sector by supporting a wide range of upcoming services with varying requirements. Therefore, system improvisation techniques, such as machine learning (ML) and deep learning, must be applied to make the model customisable. Moreover, improvisation allows the prediction system to generate the most accurate outcomes and valuable insights from data whilst enabling effective decisions. In this study, we first provide a literature study on the applications of ML and a summary of the hyperparameters influencing the prediction capabilities of the ML models for the communication system. We demonstrate the behaviour of four ML models: k nearest neighbour, classification and regression trees, random forest and support vector machine. Then, we observe and elaborate on the suitable hyperparameter values for each model based on the accuracy in prediction performance. Based on our observation, the optimal hyperparameter setting for ML models is essential because it directly impacts the model’s performance. Therefore, understanding how the ML models are expected to respond to the system utilised is critical.

The Internet of Everything is currently in demand and has burdened the network tremendously. Accommodating this exponential increase in demand will require improved Radio Resource Management technology. This problem can be curbed with higher spectrum bands, reevaluation of Time Division Duplex, deployment of Software Defined Network, and Network Function Virtualization into 5G New Radio (NR) network. Therefore, this work aims to provide an in-depth survey on the recent resource management schemes that can be proposed for 5G NR enhancement by exploiting both rulebased algorithms and machine learning methods. Radio resource management consists of managing user allocation, the antenna transmission power, bandwidth, and modulation scheme. Therefore, in this paper, three categories of radio resource management technologies are introduced: resource allocation, energy efficiency, and interference management. The discussion revolves around their potentials and contributions as well as challenges faced to produce efficient 5G resource management schemes.