Five Facets of 6G: Research Challenges and Opportunities

Whilst the fifth-generation (5G) systems are being rolled out across the globe, researchers have turned their attention to the exploration of radical next-generation solutions. At this early evolutionary stage we survey five main research facets of this field, namely {\em Facet~1: next-generation architectures, spectrum and services, Facet~2: next-generation networking, Facet~3: Internet of Things (IoT), Facet~4: wireless positioning and sensing, as well as Facet~5: applications of deep learning in 6G networks.} In this paper, we have provided a critical appraisal of the literature of promising techniques ranging from the associated architectures, networking, applications as well as designs. We have portrayed a plethora of heterogeneous architectures relying on cooperative hybrid networks supported by diverse access and transmission mechanisms. The vulnerabilities of these techniques are also addressed and carefully considered for highlighting the most of promising future research directions. Additionally, we have listed a rich suite of learning-driven optimization techniques. We conclude by observing the evolutionary paradigm-shift that has taken place from pure single-component bandwidth-efficiency, power-efficiency or delay-optimization towards multi-component designs, as exemplified by the twin-component ultra-reliable low-latency mode of the 5G system. We advocate a further evolutionary step towards multi-component Pareto optimization, which requires the exploration of the entire Pareto front of all optiomal solutions, where none of the components of the objective function may be improved without degrading at least one of the other components

Hybrid Device-to-Device and Device-to-Vehicle Networks for Energy-Efficient Emergency Communications

Recovering postdisaster communications has become a major challenge for search and rescue. Device-to-device (D2D) and device-to-vehicle (D2V) networks have drawn attention. However, due to the limited D2D coverage and onboard energy, establishing a hybrid D2D and D2V network is promising. In this article, we jointly establish, optimize, and fuse D2D and D2V networks to support energy-efficient emergency communications. First, we establish a D2D network by optimally dividing ground devices (GDs) into multiple clusters and identifying temporary data caching centers (TDCCs) from GDs in clusters. Accordingly, emergency data returned from GDs is cached in TDCCs. Second, given the distribution of TDCCs, unmanned aerial vehicles (UAVs) are dispatched to fetch data from TDCCs. Therefore, we establish a UAV-assisted D2V network through path planning and network configuration optimization. Specifically, optimal path planning is implemented using cascaded waypoint and motion planning and optimal network configurations are determined by multiobjective optimization. Consequently, the best tradeoff between emergency response time and energy consumption is achieved, subject to a given set of constraints on signal-to-interference-plus-noise ratios, the number of UAVs, transmit power, and energy. Simulation results show that our proposed approach outperforms benchmark schemes in terms of energy efficiency, contributing to large-scale postdisaster emergency response.Comment: 12 page

Trajectory Optimization for UAV Emergency Communication with Limited User Equipment Energy: A safe-DQN Approach

In post-disaster scenarios, it is challenging to provide reliable and flexible emergency communications, especially when the mobile infrastructure is seriously damaged. This article investigates the unmanned aerial vehicle (UAV)-based emergency communication networks, in which UAV is used as the mobile aerial base station for collecting information from ground users in affected areas. Due to the breakdown of ground power system after disasters, the available energy of affected user equipment (UE) is limited. Meanwhile, with the complex geographical conditions after disasters, there are obstacles affecting the flight of UAV. Aiming at maximizing the uplink throughput of UAV networks during the flying time, we formulate the UAV trajectory optimization problem considering UE energy limitation and location of obstacles on the ground. Since the constraint on UE energy is dynamic and long-term cumulative, it is hard to be solved directly. We transform the problem into a constrained Markov decision-making process (CMDP) with UAV as agent. To tackle the CMDP, we propose a safe-deep-Q-network (safe-DQN) based UAV trajectory design algorithm, where the UAV learns to selects the optimal action in reasonable policy sets. Simulation results reveal that: i) the uplink throughput of the proposed algorithm converges within multiple iterations; and ii) compared with the benchmark algorithms, the proposed algorithm performs better in terms of uplink throughput and UE energy efficiency, achieving a good trade-off between UE energy consumption and uplink throughput

An overview of VANET vehicular networks

Today, with the development of intercity and metropolitan roadways and with various cars moving in various directions, there is a greater need than ever for a network to coordinate commutes. Nowadays, people spend a lot of time in their vehicles. Smart automobiles have developed to make that time safer, more effective, more fun, pollution-free, and affordable. However, maintaining the optimum use of resources and addressing rising needs continues to be a challenge given the popularity of vehicle users and the growing diversity of requests for various services. As a result, VANET will require modernized working practices in the future. Modern intelligent transportation management and driver assistance systems are created using cutting-edge communication technology. Vehicular Ad-hoc networks promise to increase transportation effectiveness, accident prevention, and pedestrian comfort by allowing automobiles and road infrastructure to communicate entertainment and traffic information. By constructing thorough frameworks, workflow patterns, and update procedures, including block-chain, artificial intelligence, and SDN (Software Defined Networking), this paper addresses VANET-related technologies, future advances, and related challenges. An overview of the VANET upgrade solution is given in this document in order to handle potential future problems

Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking

The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out

Energy-Efficient Resource Allocation for 6G Backscatter-Enabled NOMA IoV Networks

The integration of Ambient Backscatter Communication (AmBC) with Non-Orthogonal Multiple Access (NOMA) is expected to support connectivity of low-powered Internet-of-Vehicles (IoVs) in the upcoming Sixth-Generation (6G) transportation systems. This paper proposes an energy-efficient resource allocation framework for the AmBC-enabled NOMA IoV network under imperfect Successive Interference Cancellation (SIC) decoding. In particular, multiple Road-Side Units (RSUs) transmit superimposed signals to their associated IoVs utilizing downlink NOMA transmission. Meanwhile, the Backscatter Tags (BackTags) also transmit data symbols towards nearby IoVs by reflecting the superimposed signals of RSUs. Thus, the objective is to maximize the total energy efficiency of the NOMA IoV network subject to the minimum data rate of all IoVs. A joint problem that simultaneously optimizes the total power budget of each RSU, power allocation coefficient of IoVs and reflection power of BackTags under imperfect SIC decoding is formulated. A Dinkelbach approach is first adopted to transform the optimization problem and then the transformed problem is decoupled into two subproblems for optimal transmit power at RSUs and efficient reflection power at BackTags, respectively. To solve the problems efficiently, dual theory and Karush-Kuhn-Tucker conditions are exploited, where the Lagrangian dual variables are iteratively calculated using the subgradient method. To check the performance of the proposed framework, a benchmark optimization without AmBC is also provided. Numerical results demonstrate the superiority of the proposed AmBC-enabled NOMA IoV framework over the benchmark conventional IoV framework