In this paper, emerging 5G mobile services are investigated and categorized from the perspective of not service providers, but end-users. The development of 5G mobile services is based on an intensive analysis of the global trends in mobile services. Additionally, several indispensable service requirements, essential for realizing service scenarios presented, are described. To illustrate the changes in societies and in daily life in the 5G era, five megatrends, including the explosion of mobile data traffic, the rapid increase in connected devices, everything on the cloud, hyper-realistic media for convergence services and knowledge as a service enabled by big-data analysis, are examined. Based on such trends, we classify the new 5G services into five categories in terms of the end-users’ experience as follows: immersive 5G services, intelligent 5G services, omnipresent 5G services, autonomous 5G services and public 5G services. Moreover, several 5G service scenarios in each service category are presented, and essential technical requirements for realizing the aforementioned 5G services are suggested, along with a competitiveness analysis on 5G services/devices/network industries and the current condition of 5G technologies.

Sixth-generation (6G) networks pose substantial security risks because confidential information is transmitted over wireless channels with a broadcast nature, and various attack vectors emerge. Physical layer security (PLS) exploits the dynamic characteristics of wireless environments to provide secure communications, while reconfigurable intelligent surfaces (RISs) can facilitate PLS by controlling wireless transmissions. With RIS-aided PLS, a lightweight security solution can be designed for low-end Internet of Things (IoT) devices, depending on the design scenario and communication objective. This article discusses RIS-aided PLS designs for 6G-IoT networks against eavesdropping and jamming attacks. The theoretical background and literature review of RIS-aided PLS are discussed, and design solutions related to resource allocation, beamforming, artificial noise, and cooperative communication are presented. We provide simulation results to show the effectiveness of RIS in terms of PLS. In addition, we examine the research issues and possible solutions for RIS modeling, channel modeling and estimation, optimization, and machine learning. Finally, we discuss recent advances, including simultaneous transmitting and reflecting-RIS and malicious RIS.

In air-to-ground communications, the network lifetime depends on the operation time of unmanned aerial vehicle-base stations (UAV-BSs) owing to the restricted battery capacity. Therefore, the maximization of energy efficiency and the minimization of outage ground users are important metrics of network performance. To achieve these two objectives, the location and transmit power of the UAV-BSs in the network must be optimized. This optimization problem may not be tractable in the conventional optimization framework because multiple UAV-BSs interact in a complicated manner. Hence, we formulate the problem as a Markov decision process and develop an algorithm to obtain a solution in a reinforcement learning framework. To avoid a central controller and high computational complexity, we employ a multiagent distributed ${Q}$ -learning algorithm to obtain a solution. Specifically, we propose a multiagent ${Q}$ -learning-based UAV-BS deployment and power control strategy to maximize energy efficiency and minimize the number of outage users in multi-UAV wireless networks. Through intensive simulations, it is demonstrated that the proposed algorithm can outperform benchmark algorithms in terms of average energy efficiency and number of average outage users in multi-UAV wireless networks.

An unmanned aerial vehicle-mounted base station (UAV-BS) is a promising technology for the forthcoming sixth-generation wireless networks, owing to its flexibility and cost effectiveness. Besides, the limited network operation time of UAV-BS networks can be overcome with the concept of tethered unmanned aerial vehicles (TUAVs), which are powered from an energy source in the ground. Along with this trend, the optimal deployment (i.e., trajectory control) of TUAVs to maximize throughput in multicell environments has been studied. As the problem is modeled by a Markov decision process, a multiagent $Q$ -learning (QL) algorithm was developed to obtain a solution. When considering the limited inter-UAV link capacity and computing power of each UAV, the proposed multiagent QL algorithm can be a practical approach. Intensive simulations were conducted to evaluate the performance of the proposed algorithm with respect to various metrics, such as sum or individual rates, fairness, and computational complexity in multicell air-to-ground (A2G) networks. Our proposed algorithm achieves superior performance compared to conventional algorithms, such as random action, QL-based altitude control algorithms (QAC), and centralized QL algorithm.

In ultra-dense small cell networks (UDSCNs), where a significant number of small cell base stations (SBSs) coexist, the amount of power consumed at the SBSs can be extremely high, rendering the efficient management of power consumption for the SBSs particularly important. Herein, we propose a deep-learning-based resource allocation strategy to maximize network-wide energy efficiency in the UDSCN by optimally controlling the transmit power and user association. In this regard, a novel deep neural network (DNN) structure comprising three separate DNN units, each of which determines the activation of the SBSs, user association, and transmit power, as well as an unsupervised-learning-based training methodology are designed. Simulation results verify that the proposed scheme achieves a near-optimal performance while requiring a short computation time.

The high rotation speeds and mega-constellations of low earth orbit satellites (LEO SATs) cause the inter-satellite frequent handovers (HOs) problem which can lead to substantial performance degradation. This paper proposes a novel distributed multi-agent deep Q-network based SAT HO strategy for the LEO SAT networks to simultaneously minimize the number of HOs and maximize the throughputs and the visible times of UEs while satisfying the quality-of-service (QoS) constraints of all UEs. The proposed HO scheme allows UEs to independently and simultaneously perform the HO decision makings based on their own local information, which enables to immediately adapt to the dynamic changes of the LEO SAT network environments. The numerical results demonstrated that our proposed HO strategy achieves the lowest average HO rate and the highest achievable throughputs compared to other conventional HO strategies, while ensuring a higher QoS guarantee time ratio.