Handover (HO) is one of the key aspects of next-generation (NG) cellular communication networks that need to be properly managed since it poses multiple threats to quality-of-service (QoS) such as the reduction in the average throughput as well as service interruptions. With the introduction of new enablers for fifth-generation (5G) networks, such as millimetre wave (mm-wave) communications, network densification, Internet of things (IoT), etc., HO management is provisioned to be more challenging as the number of base stations (BSs) per unit area, and the number of connections has been dramatically rising. Considering the stringent requirements that have been newly released in the standards of 5G networks, the level of the challenge is multiplied. To this end, intelligent HO management schemes have been proposed and tested in the literature, paving the way for tackling these challenges more efficiently and effectively. In this survey, we aim at revealing the current status of cellular networks and discussing mobility and HO management in 5G alongside the general characteristics of 5G networks. We provide an extensive tutorial on HO management in 5G networks accompanied by a discussion on machine learning (ML) applications to HO management. A novel taxonomy in terms of the source of data to be utilized in training ML algorithms is produced, where two broad categories are considered; namely, visual data and network data. The state-of-the-art on ML-aided HO management in cellular networks under each category is extensively reviewed with the most recent studies, and the challenges, as well as future research directions, are detailed.

Wireless communication networks have been witnessing unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Although there are many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance capacity due to their easy implementation, pop-up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity where it is needed. However, UAVs mostly have limited energy storage, hence, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed—conventional and machine learning (ML). Such classification helps understand the state-of-the-art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above-mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trends in the literature.

We propose a reinforcement learning-based cell switching algorithm to minimize the energy consumption in ultra-dense deployments without compromising the quality of service (QoS) experienced by the users. In this regard, the proposed method can intelligently learn which small cells (SCs) to turn off at any given time based on the traffic load of the SCs and the macro cell. To validate the idea, we used the open call detail record (CDR) data set from the city of Milan, Italy, and tested our algorithm against typical operational benchmark solutions. With the obtained results, we demonstrate exactly when and how the proposed method can provide energy savings, and moreover how this happens without reducing QoS of users. Most importantly, we show that our solution has a very similar performance to the exhaustive search, with the advantage of being scalable and less complex.

There is no doubt that the world is currently experiencing a global pandemic that is reshaping our daily lives as well as the way business activities are being conducted. With the emphasis on social distancing as an effective means of curbing the rapid spread of the infection, many individuals, institutions, and industries have had to rely on telecommunications as a means of ensuring service continuity in order to prevent complete shutdown of their operations. This has put enormous pressure on both fixed and mobile networks. Though fifth generation mobile networks (5G) is at its infancy in terms of deployment, it possesses a broad category of services including enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC), that can help in tackling pandemic-related challenges. Therefore, in this paper, we identify the challenges facing existing networks due to the surge in traffic demand as a result of the COVID-19 pandemic and emphasize the role of 5G empowered by artificial intelligence in tackling these problems. In addition, we also provide a brief insight on the use of artificial intelligence driven 5G networks in predicting future pandemic outbreaks, and the development a pandemic-resilient society in case of future outbreaks.

The Open Radio Access Network (O-RAN) architecture has been identified as a promising technology for enhanced network deployment, innovation, improved competition, and reduction of capital and operating expenses (CAPEX/OPEX) of 5G and beyond networks because of its open interfaces, disaggregated network entities and functions, virtualization of network hardware and software, and intelligent control. However, the effect of this improved technology on the energy consumption of the RAN needs to be carefully investigated, so that the many advantages that can be obtained from the O-RAN are not overwhelmed by increased energy consumption. Hence, in this paper, we investigate the O-RAN from an Energy efficiency (EE) perspective by reviewing the state-of-the-art power consumption models, and EE techniques that have been proposed to minimize the energy consumption of O-RAN. In addition, the challenges associated with the optimization of the EE of O-RAN and opportunities for further research are highlighted.