Terrestrial communication networks mainly focus on users in urban areas but have poor coverage performance in harsh environments, such as mountains, deserts, and oceans. Satellites can be exploited to extend the coverage of terrestrial fifth-generation networks. However, satellites are restricted by their high latency and relatively low data rate. Consequently, the integration of terrestrial and satellite components has been widely studied to take advantage of both sides and enable the seamless broadband coverage. Due to the significant differences between satellite communications (SatComs) and terrestrial communications (TerComs) in terms of channel fading, transmission delay, mobility, and coverage performance, the establishment of an efficient hybrid satellite–terrestrial network (HSTN) still faces many challenges. In general, it is difficult to decompose an HSTN into a sum of separate satellite and terrestrial links due to the complicated coupling relationships therein. To uncover the complete picture of HSTNs, we regard the HSTN as a combination of basic cooperative models that contain the main traits of satellite–terrestrial integration but are much simpler and thus more tractable than the large-scale heterogeneous HSTNs. In particular, we present three basic cooperative models, i.e., model ${X}$ , model ${L}$ , and model ${V}$ , and provide a survey of the state-of-the-art technologies for each of them. We discuss future research directions toward establishing a cell-free, hierarchical, decoupled HSTN. We also outline open issues to envision an agile, smart, and secure HSTN for the sixth-generation ubiquitous Internet of Things.

/pdf/5g-embraces-satellites-for-6g-ubiquitous-iot-basic-models-4oyge76ps2.pdf

5G Embraces Satellites for 6G Ubiquitous IoT: Basic Models for Integrated Satellite Terrestrial Networks

Terrestrial communication networks mainly focus on users in urban areas but have poor coverage performance in harsh environments, such as mountains, deserts, and oceans. Satellites can be exploited to extend the coverage of terrestrial fifth-generation (5G) networks. However, satellites are restricted by their high latency and relatively low data rate. Consequently, the integration of terrestrial and satellite components has been widely studied, to take advantage of both sides and enable seamless broadband coverage. Due to the significant differences between satellite communications (SatComs) and terrestrial communications (TerComs) in terms of channel fading, transmission delay, mobility, and coverage performance, the establishment of an efficient hybrid satellite-terrestrial network (HSTN) still faces many challenges. In general, it is difficult to decompose a HSTN into a sum of separate satellite and terrestrial links due to the complicated coupling relationships therein. To uncover the complete picture of HSTNs, we regard the HSTN as a combination of basic cooperative models that contain the main traits of satellite-terrestrial integration but are much simpler and thus more tractable than the large-scale heterogeneous HSTNs. In particular, we present three basic cooperative models, i.e., model X, model L, and model V, and provide a survey of the state-of-the-art technologies for each of them. We discuss future research directions towards establishing a cell-free, hierarchical, decoupled HSTN. We also outline open issues to envision an agile, smart, and secure HSTN for the sixth-generation (6G) ubiquitous Internet of Things (IoT).

/pdf/5g-embraces-satellites-for-6g-ubiquitous-iot-basic-models-1hxxthf5o4.pdf

Topology control algorithms in multi-unmanned aerial vehicle networks: An extensive survey

A flying ad hoc network (FANETs), also known as a swarm of unmanned aerial vehicles (UAVs), can be deployed in a wide range of applications including surveillance, monitoring, and emergency communications. UAVs must perform real-time communication among themselves and the base station via an efficient routing protocol. However, designing an efficient multihop routing protocol for FANETs is challenging due to high mobility, dynamic topology, limited energy, and short transmission range. Recently, owing to the advantages of multi-objective optimization, Q-learning (QL)-based position-aware routing protocols have improved the performance of routing in FANETs. In his article, we provide a comprehensive review of existing QL-based position-aware routing protocols for FANETs. We rigorously address dynamic topology, mobility models, and the relationship between QL and routing in FANETs, and extensively review the existing QL-based position-aware routing protocols along with their advantages and limitations. Then, we compare the reviewed protocols qualitatively in terms of operational features, characteristics, and performance metrics. We also discuss important open issues and research challenges with potential research directions.

/pdf/survey-on-q-learning-based-position-aware-routing-protocols-2q55sr9w.pdf

Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks

Unmanned aerial vehicles (UAVs) are inevitable to meet the requirements of future wireless networks. Recently, researchers have investigated diverse issues related to UAV‐assisted networks (including placement of UAVs, resource management, and spectrum sharing) for a broad range of applications, including disaster management, data collection from the ground sensor network, surveillance, logistic support, etc. This article presents a comprehensive survey of recent advances in UAV‐assisted networks. We mainly emphasize the optimization perspective of UAV‐assisted wireless networks with different objectives, including coverage area, throughput, energy efficiency, quality of service, delay, and outage probability. We provide a detailed discussion for each objective with their constraints, optimization problem, solution approach, and performance metrics. We also provide relationships among different objectives and parameters considered in the literature. Finally, we list open research issues and future research directions to improve UAV‐assisted wireless networks in the context of optimization.

Resource optimization in UAV‐assisted wireless networks—A comprehensive survey

Industrial Internet of Things (IIoT) has found wider research, and satellite-terrestrial network (STN) can provide large-scale seamless connections for IIoT. With virtualization, we design resource cube to describe the integration and state of multi-dimensional virtual resources. To achieve higher resource utilization and smarter connections, we design a matching considered preferences (MCPR) algorithm to match IIoT nodes with service sides. The matching design considers the resource cube (MCRC) algorithm based on MCPR algorithm to lower the total system delay. In addition, in order to simplify the analysis of resource management, we adopt a layered architecture and multiple M/M/1 queuing models. We analyze the resource utilization and the total system delay for three different combinations of arrival rate and service rate of each resource cube. With MCRC algorithm, the utilization of resources is slightly reduced, while the total system delay is greatly reduced compared with MCPR algorithm.

/pdf/resource-cube-multi-virtual-resource-management-for-2tklpnl130.pdf

Resource Cube: Multi-Virtual Resource Management for Integrated Satellite-Terrestrial Industrial IoT Networks

Unmanned aerial vehicle (UAV) multi-hop communication networks are foreseen to be widely employed in both military and civilian scenarios. However, in ultra-dense scenarios with swarm UAVs, nodes are highly dynamic mobile, ultra-dense deployment and non-centralized distribution. These characteristics make the centralized resource management policy not apply. Meanwhile, existing routing protocols can't meet the performance challenges of high dynamic, topology and link frequency changes of ultra-dense scenarios with swarm UAVs. To solve the above challenges of resource management and routing protocol, a cross-layer optimization method is presented with a novel mean field game (MFG) in this paper. It is based on the cross-layer design method of the MFG theory and jointly considers the power resources in the physical layer, frequency resources in the medium access control (MAC) layer, and routing resources in the network layer. By dividing into subproblems, the original problem is solved. Meanwhile, the optimal data transmission path can be selected through the management and allocation of frequency resources and power resources. A crosslayer resource management dynamic source routing (CLRMDSR) protocol is designed based on that which adds link quality measurement. The simulation results show that the presented CLRM-DSR with the proposed resource management scheme can improve the data packet transmission rate, reduce end-toend delay, and lower routing overhead for the multi-hop swarm UAV communication network.

A mean field game-theoretic cross-layer optimization for multi-hop swarm UAV communications

As an extensive prospect in communication technology, multi-hop unmanned aerial vehicle (UAV) faces some challenges as well. How to ensure the availability of time slots in high dynamic scenarios and the rationality and robustness of power allocation in massive scenarios remains to be resolved. In this paper, we propose a robust mean field game (MFG) framework to manage time slot resources and power resources. On the one hand, this framework can predict the potential conflicts of time slots and avoid them in time. On the other hand, it can well adapt to the massive scenarios with complex interference, in order to realize power control. The simulation results show that the robust MFG-based resource management scheme can not only reduce the probability of packet collision and packet loss, but also improve communication energy efficiency (EE) with better robustness.

Joint Power Control and Scheduling for High-Dynamic Multi-Hop UAV Communication: A Robust Mean Field Game

SnS material is considered as an attractive anode material to substitute commercial graphite anodes of lithium-ion batteries (LIBs) due to its large capacity, low cost, and environment-friendliness. However, the side reactions induced during charge/discharge processes significantly expand the volume of SnS material, limiting its development. Herein, a novel deep eutectic solvent is synthesized with polyethylene glycol and SnCl2·2H2O, and [email protected] composites are prepared using a one-step pyrolytic vulcanization method. The composite material is sandwich-shaped with graphene sheet layers interspersed with SnS particles. Tests after making them into button cells have shown that the composite electrodes have excellent electrochemical properties: an initial discharge capacity of 1346 mAh g−1 at the current density of 100 mA g−1; outstanding long-period performance under high current. In the case of 1 A g−1, it still has 421.8 mAh g−1 capacity after 1000 cycles. It also shows superior rate characteristics: under the current density of 5 A g−1, its specific discharge capacity is 343 mAh g−1; the specific discharge capacity increases rapidly to 798 mAh g−1 when the current density returns to 1 A g−1. 

Electrochemical performance of SnS@graphene composites prepared by deep eutectic solvent method

The invention discloses an unmanned aerial vehicle multi-dimensional resource management method with high energy efficiency and belongs to the technical field of wireless communication. The method specifically comprises the following steps: determining a user type; selecting an unmanned aerial vehicle or a ground base station to serve an edge or center user according to the user type; because of the high propulsion energy consumption of the unmanned aerial vehicle, establishing an optimized energy efficiency model combining multi-dimensional resource management of the height, speed, track radius and transmitting power of the unmanned aerial vehicle; decomposing a model objective function which is a non-convex function into four sub-problems , and separately solving the four sub-problems; for solving the power, using a Dinkelbach algorithm to convert a fractional optimization problem into a subtractive optimization problem, and then using a Lagrange method and a subgradient method for solving an optimization variable; solving other optimization variables by utilizing a relationship between a function derivative and an extreme value; repeating the above steps, determining whether theobjective function is converged or not, if so, stopping iteration, and if not, repeating the above steps. On the premise of ensuring the communication quality of the user, the system energy efficiency of the unmanned aerial vehicle network auxiliary edge user is improved.

Lizhong Chang

Papers

Resource Cube: Multi-Virtual Resource Management for Integrated Satellite-Terrestrial Industrial IoT Networks

A mean field game-theoretic cross-layer optimization for multi-hop swarm UAV communications

Joint Power Control and Scheduling for High-Dynamic Multi-Hop UAV Communication: A Robust Mean Field Game

Electrochemical performance of SnS@graphene composites prepared by deep eutectic solvent method

Unmanned aerial vehicle multi-dimensional resource management method with high energy efficiency