Owing to the explosive growth of requirements of rapid emergency communication response and accurate observation services, airborne communication networks (ACNs) have received much attention from both industry and academia. ACNs are subject to heterogeneous networks that are engineered to utilize satellites, high-altitude platforms (HAPs), and low-altitude platforms (LAPs) to build communication access platforms. Compared to terrestrial wireless networks, ACNs are characterized by frequently changed network topologies and more vulnerable communication connections. Furthermore, ACNs have the demand of the seamless integration of heterogeneous networks such that the network quality-of-service (QoS) can be improved. Thus, designing mechanisms and protocols for ACNs poses many challenges. To solve these challenges, extensive research has been conducted. The objective of this special issue is to disseminate the contributions in the field of ACNs. To present this special issue with the necessary background and offer an overall view on this field, three key areas of ACNs are covered. Specifically, this paper covers LAP-based communication networks, HAPbased communication networks, and integrated ACNs. For each area, this paper addresses the particular issues and reviews major mechanisms. This paper also points out future research directions and challenges.

Resource allocation for mobile edge computing (MEC) in unmanned aerial vehicle (UAV) networks has been a popular research issue. Different from existing works, this paper considers a multi-UAV-aided uplink communication scenario and investigates a resource allocation problem of minimizing the total system latency and the energy consumption, subject to constraints on transmit power of mobile users (MUs), system latency caused by transmission and computation. The problem is confirmed to be a challenging time-series mixed-integer non-convex programming problem, and we propose a joint UAV Movement control, MU Association and MU Power control (UMAP) algorithm to solve it effectively, where three sub-problems are optimized iteratively. Specifically, UAV movement and MU association are optimized utilizing deep reinforcement learning (DRL) to decrease the energy consumption and system latency. Next, a closed-form solution of the MU transmit power is derived. Finally, simulation results show that the UMAP algorithm can significantly decrease the system latency and energy consumption and increase the coverage rate compared with benchmark algorithms.

The unmanned aerial vehicle (UAV) network that is convinced as a significant component of 5G and emerging 6G wireless networks is desired to accommodate multiple types of service requirements simultaneously. However, how to converge different types of services onto a common UAV network without deploying an individual network solution for each type of service is challenging. We tackle this challenge in this paper through slicing the UAV network, i.e., creating logical UAV networks customized for specific requirements. To this end, we formulate the UAV network slicing problem as a sequential decision problem to provide mobile broadband (MBB) services for ground mobile users while satisfying ultra-reliable and low-latency requirements of UAV control and non-payload signal delivery. This problem, however, is difficult to be directly solved mainly due to the sequence-dependent characteristic and the lack of accurate location information of mobile users and accurate and tractable channel gain models in practice. To overcome these difficulties, we propose a novel solution approach based on learning and optimization methods. Particularly, we develop a distributed learning method to predict mobile users’ locations, where partial user location information stored on each UAV is utilized to train user location prediction networks. To achieve accurate channel gain models, we design deep neural networks (DNNs) that are trained by signal measurements at each UAV. To cope with the challenging sequence-dependent characteristic of the problem, we develop a Lyapunov-based optimization framework with provable performance guarantees to decompose the original problem into a sequence of separate optimization subproblems based on the learned results. Finally, an iterative optimization scheme joint with a successive convex approximation technique is exploited to solve these subproblems. Simulation results demonstrate the accuracy of the learning methods as well as the effectiveness of the Lyapunov-based optimization framework.

This letter is concerned with investigating a joint user association and unmanned aerial vehicle (UAV) location optimization problem for UAV-aided communications. Different from the majority of existing studies, this letter aims at deriving solutions to the joint optimization problem that is modeled as a mixed-integer non-convex optimization problem (MINCOP) with a goal of maximizing users' total achievable data rates. A novel iterative optimization scheme is proposed to handle the MINCOP. Specifically, the MINCOP is first decomposed into an integer optimization subproblem and a non-convex optimization subproblem. An iterative algorithm is then developed to optimize these two separated subproblems alternatively. Besides, the non-convexity of the non-convex subproblem is handled via successive convex approximation. Based on approximate results, Karush-Kuhn-Tucker (KKT) conditions are leveraged to obtain the evolutionary-form solution to the joint optimization problem. The comparison with the state-of-the-art verifies that the developed algorithm is convergent and is able to achieve good performance.

Particle swarm optimization (PSO) aims at finding the optimum point in a high-dimension solution space by simulating the swarming and flocking behaviors in nature. Recent empirical studies of reconstructing the hidden interaction networks in flocking birds and schooling fish found that individuals play different roles in group decision making. An outstanding question is whether the performance of PSO can be improved by incorporating these empirical findings. Here, we systematically explore the impact of the heterogeneity of interaction network and individual’s learning strategies to find that the corresponding network-based algorithm, network-based heterogeneous particle swarm optimization (NHPSO), significantly outperforms other PSO based and non-PSO-based comparative algorithms on our experiments with 18 test functions. Our further analysis of the information exchange among the particles reveals that learning from low-degree particles in the middle period of evolution is crucial for the swarm to achieve the global optimum. These results offer a new framework to improve the performance of swarm optimization and demonstrate the applicability of network science in designing optimization algorithms. Finally, the universality of NHPSO is demonstrated on an emerging application, the unmanned aerial vehicle communication coverage.

This paper is concerned with the efficient movement control of multiple drone-cells for communication coverage. Although many works have been developed to cope with this problem, few of them have investigated the three-dimensional (3-D) continuous movement control of multiple drone-cells. In this paper, a problem of 3-D continuous movement control of multiple drone-cells is formulated with an objective of maximizing the energy-efficient communication coverage of drone-cell networks, while preserving the network connectivity. To mitigate this challenging problem, an energy-efficiency and continuous-movement-control algorithm (E $^2$ CMC), which is based on an emerging deep reinforcement learning (DRL) method, is proposed. In E $^2$ CMC, an energy efficiency reward function considering the energy consumption, the {sum of quality-of-service (QoS) requirements of users} as well as the coverage fairness is first designed. Next, E $^2$ CMC learns to adjust drone-cells' locations continuously by interacting with an environment in a sequence of observations, actions, and rewards. Further, E $^2$ CMC will reduce the reward drastically as long as the networks are disconnected. Simulation results verify the superiority of the proposed learning algorithm on deploying multiple drone-cells to provide the communication coverage.