# Editorial for the Special Issue on 5G Enabling Technologies and Wireless Networking
###### Abstract  
The ongoing deployment of 5G networks is seen as a key enabler for realizing upcoming interconnected services at scale, including the massive deployment of the Internet of Things, providing V2X communications to support autonomous vehicles, and the increase in smart homes, smart cities, and Industry 4.0. The Special Issue of _Future Internet_ titled \"5G Enabling Technologies and Wireless Networking\" focuses on 5G-enabling technologies to meet these access, efficiency, and performance requirements and facilitates coordinating wireless networks to support future deployments. We published a total of five papers that covered a range of topics from practical 5G deployment experiences in non-public and vehicular networks to applying technologies such as service chaining and self-organising Networks (SONs) to network operations. As such, we have grouped the published work into two broad categories: the first discusses practical issues related to 5G deployments while the second looks at enabling technologies and how they can supplement or extend current existing 5G networks. In the first group, we have three papers focusing on a range of issues related to current 5G wireless networks. In the first paper [1], the authors present the results of their work to model and predict the performance of millimeter wave (mmWave) backhaul links that were deployed as part of the Liverpool 5G network. Based on the properties of the 802.11 ad protocol and the physical characteristics of the environment, they simulated how each link performed with different signal-to-noise ratios (SNR) and packet error rate (PER) values and verified them against real-world deployed links. The results showed a good convergence between the simulated and real results and provide a solid foundation for further network planning and optimization. This type of practical deployment experience, particularly around non-public networks also provides useful insights into how 5G deployments can be optimised going forward. In the second paper, [PERSON] et al. [2] took a different approach and proposed a data-driven strategy for predicting customer service technical ticket reopening for 5G fiber telecommunications companies. Their main aim was to ensure that the service level agreement between the end user and service provider was satisfied in terms of the perceived quality of service. The authors made a detailed comparison of different approaches to classification--ranging from decision trees to artificial neural networks and support vector machines--and found that a Bayesian network classifier is the most accurate at predicting whether a monitored ticket will be reopened or not. This work again provides useful insights into how 5G networks can be managed as user numbers continue to grow and become denser and the potential for congestion or other issues increases. Finally, from a practical perspective, [PERSON] et al. [3] reviewed the applications, characteristics, and challenges faced in the design of MAC protocols in 5G vehicular environments. They presented a classification of MAC protocols based on the metrics of contention mechanisms and channel access. In the first case, contention was listed as contention-based, contention-free, and hybrid, whereas channel access was categorized as being distributed, centralized, cluster-based, cooperative, token-based, or random access. The paper gives an analysis of the objectives, mechanisms, advantages/disadvantages, and simulators used in each protocol and provides a discussion on the future scope andopen challenges for improving MAC protocol's design. This is important because vehicular networks continue to be an important use case for 5G so new insights into MAC protocol's design may lead to performance improvements in the future as these networks expand. In the second group, we consider more theoretical supporting technologies that can be utilised to enhance the performance of existing 5G networks. The paper by [PERSON] et al.