As of today, the fifth generation (5G) mobile communication system has been rolled out in many countries and the number of 5G subscribers already reaches a very large scale. It is time for academia and industry to shift their attention towards the next generation. At this crossroad, an overview of the current state of the art and a vision of future communications are definitely of interest. This article thus aims to provide a comprehensive survey to draw a picture of the sixth generation (6G) system in terms of drivers, use cases, usage scenarios, requirements, key performance indicators (KPIs), architecture, and enabling technologies. First, we attempt to answer the question of “Is there any need for 6G?” by shedding light on its key driving factors, in which we predict the explosive growth of mobile traffic until 2030, and envision potential use cases and usage scenarios. Second, the technical requirements of 6G are discussed and compared with those of 5G with respect to a set of KPIs in a quantitative manner. Third, the state-of-the-art 6G research efforts and activities from representative institutions and countries are summarized, and a tentative roadmap of definition, specification, standardization, and regulation is projected. Then, we identify a dozen of potential technologies and introduce their principles, advantages, challenges, and open research issues. Finally, the conclusions are drawn to paint a picture of “What 6G may look like?.” This survey is intended to serve as an enlightening guideline to spur interests and further investigations for subsequent research and development of 6G communications systems.

Next-generation cellular technologies, commonly referred to as the sixth generation (6G), are envisioned to support a higher system capacity, better performance, and network sensing capabilities. The terahertz (THz) band is one potential enabler to this end due to the large unused frequency bands and the high spatial resolution enabled by the short signal wavelength and large bandwidth. Different from earlier surveys, this paper presents a comprehensive treatment and technology survey on THz communications and sensing in terms of advantages, applications, propagation characterization, channel modeling, measurement campaigns, antennas, transceiver devices, beamforming, networking, the integration of communications and sensing, and experimental testbeds. Starting from the motivation and use cases, we survey the development and historical perspective of THz communications and sensing with the anticipated 6G requirements. We explore the radio propagation, channel modeling, and measurement for the THz band. The transceiver requirements, architectures, technological challenges, and state-of-the-art approaches to compensate for the high propagation losses, including appropriate antenna design and beamforming solutions. We overview several related technologies that either are required by or are beneficial for THz systems and networks. The synergistic design of sensing and communications is explored in depth. Practical trials, demonstrations, and experiments are also summarized. The paper gives a holistic view of the current state of the art and highlights the open research challenges towards 6G and beyond.

The Internet of Things (IoT) edge network has connected lots of heterogeneous smart devices, thanks to unmanned aerial vehicles (UAVs) and their groundbreaking emerging applications. Limited computational capacity and energy availability have been major factors hindering the performance of edge user equipment (UE) and IoT devices in IoT edge networks. Besides, the edge base station (BS) with the computation server is allowed massive traffic and is vulnerable to disasters. The UAV is a promising technology that provides aerial base stations (ABSs) to assist the edge network in enhancing the ground network performance, extending network coverage, and offloading computationally intensive tasks from UEs or IoT devices. In this paper, we deploy a clustered multi-UAV to provide computing task offloading and resource allocation services to IoT devices. We propose a multi-agent deep reinforcement learning (MADRL)-based approach to minimize the overall network computation cost while ensuring the quality of service (QoS) requirements of IoT devices or UEs in the IoT network. We formulate our problem as a natural extension of the Markov decision process (MDP) concerning stochastic game, to minimize the long-term computation cost in terms of energy and delay. We consider the stochastic time-varying UAVs’ channel strength and dynamic resource requests to obtain optimal resource allocation policies and computation offloading in aerial to ground (A2G) network infrastructure. Simulation results show that our proposed MADRL method reduces the average costs by 38.643%, and 55.621% and increases the reward by 58.289% and 85.289% compared with the different single agent DRL and heuristic schemes, respectively.

Channel state information (CSI), which enables wireless systems to adapt their transmission parameters to instantaneous channel conditions and consequently achieve great performance boost, plays an increasingly vital role in mobile communications. However, getting accurate CSI is challenging due mainly to rapid channel variation caused by multi-path fading. The inaccuracy of CSI imposes a severe impact on the performance of a wide range of adaptive wireless systems, highlighting the significance of channel prediction that can combat outdated CSI effectively. The aim of this article is to shed light on the state of the art in this field and then go beyond by proposing a novel predictor that leverages the strong time-series prediction capability of deep recurrent neural networks incorporating long short-term memory or gated recurrent unit. In addition to an analytical comparison of computational complexity, performance evaluation in terms of prediction accuracy is carried out upon multi-antenna fading channels. Numerical results reveal that deep learning brings a notable performance gain compared with the conventional predictors built on shallow recurrent neural networks.

By adapting transmission parameters such as the constellation size, coding rate, and transmit power to instantaneous channel conditions, adaptive wireless communications can potentially achieve great performance. To realize this potential, accurate channel state information (CSI) is required at the transmitter. However, unless the mobile speed is very low, the obtained CSI quickly becomes outdated due to the rapid channel variation caused by multi-path fading. Since outdated CSI has a severely negative impact on a wide variety of adaptive transmission systems, prediction of future channel samples is of great importance. The traditional stochastic methods, modeling a time-varying channel as an autoregressive process or as a set of propagation parameters, suffer from marginal prediction accuracy or unaffordable complexity. Taking advantage of its capability on time-series prediction, applying a recurrent neural network (RNN) to conduct channel prediction gained much attention from both academia and industry recently. The aim of this article is to provide a comprehensive overview so as to shed light on the state of the art in this field. Starting from a review on two model-based approaches, the basic structure of a recurrent neural network, its training method, RNN-based predictors, and a prediction-aided system, are presented. Moreover, the complexity and performance of predictors are comparatively illustrated by numerical results.

The definition of the fundamental concepts and the design of the architectural framework for network slicing in fifth-generation communication systems have been successfully concluded; the standardization activities are almost over; and the commercial deployment has already commenced worldwide. To compete for digital supremacy and to be seen as front-runners in the international technological race, researchers from various regions and countries have begun exploring the technical requirements, envisioning potential applications, identifying innovative enablers, developing testbeds for the preliminary validation of several terrestrial and non-terrestrial technologies, and conceptualizing the architectural design for the next generation of mobile communication systems -the sixth-generation (6G) -aiming to connect the human, physical, and digital worlds with a high level of intelligence and openness for the 2030s. In support of such an ambitious vision, this article extends the end-to-end network slicing concepts, methods, solutions, and functioning architectures towards 6G. To this intent, the study first presents several decisive motivating trends behind such an extension of network slicing in order to make forthcoming mobile networks fully slicing-aware. Following that, the paper attempts to highlight the intelligentization of a number of key enabling technologies that will bring a renaissance to network slicing in the next decade. It then proposes a unified architectural framework and its principal building blocks in several layers, paving the way for the implementation of an open and intelligent network and network slicing in 6G. The proposed architectural solution harmonizes the most recent specifications of the relevant de jure and de facto standards development organizations in their applicable layers with the aim of architecting a pre-standard-compliant and preliminary framework for slicing the 6G network. Finally, the article is intended to spur interest and lay the groundwork for further investigations and subsequent research and development by highlighting a number of open research challenges and directions in this flourishing field.