Unmanned aerial vehicles (UAVs) acting as aerial base stations are regarded as an effective solution for emergency communication, due to their high mobility and low cost. In this paper, we consider the differentiated communication requirements in disaster relief scenarios, where the uplink demands high throughput and the downlink requires low latency and high reliability. To simultaneously capture the timeliness and reliability requirements of downlink transmissions, we propose a novel QoS evaluation metric based on finite blocklength theory. Building on this, we formulate a system utility maximization problem and solve it by jointly optimizing UAV trajectory and ground node access control. To address this problem, we propose a novel algorithm named PW-QMIX, which integrates a priority-based heuristic access control policy with a QMIX-based trajectory optimization scheme, effectively tackling the challenges posed by the high-dimensional joint action space. Furthermore, to tackle the challenge of dynamic observation caused by UAV movement and sensing limitations, we design a weight generation network (WGN) and incorporate it into the input layer of QMIX. The WGN dynamically generates weight matrices based on observed nodes, enhancing UAV’s adaptability to local observation variations. Simulation results validate the significance of jointly optimizing uplink and downlink communications. Moreover, compared with state-of-the-art algorithms, the proposed PW-QMIX demonstrates significant advantages in convergence and scalability.

Adopting federated learning (FL) in unmanned aerial vehicle (UAV) networks is a promising paradigm, which can empower UAV networks with enhanced intelligence to support complex applications. Considering the imbalanced data properties, limited energy and unstable wireless connection of UAVs, an effective client scheduling scheme is critical for the design of efficient FL. In this paper, in order to properly consider the priority criteria, we first propose two importance metrics from the perspectives of data attributes and local updates, namely data importance measurement (DIM) and gradient importance measurement (GIM). Then, take into account DIM and GIM, an optimization problem is formulated to jointly optimize the client scheduling, computation and communication of UAVs. Due to the non-convex nature of this problem, we decompose it into two sub-problems and derive their optimal closed-form solutions. Simulations demonstrate that, compared to benchmark schemes, our proposal ensures better performance on test accuracy, convergence and energy saving.

Unmanned aerial vehicles (UAVs) are recognized as effective data collectors for wireless sensor networks. The Age of Information (AoI), a metric indicating data freshness, is crucial for decision making in time-sensitive applications. It can be significantly reduced by jointly optimizing UAV trajectories and communication scheduling of sensor nodes (SNs). However, rapid changes in the environment make it challenging to predesign UAV trajectories and communication scheduling decisions using traditional methods, especially when central controllers are absent and the numbers of UAVs and SNs vary. In this article, we propose hypernetwork-based QMIX (HyperQMIX), a population-invariant multiagent deep reinforcement learning (MADRL) algorithm capable of transferring policies across tasks with varying population sizes. First, we design neural network modules adaptable to varying input and output dimensions, facilitated by parameter generation through a hypernetwork. Then, HyperQMIX leverages these modules to process fluctuations in state and action dimensions. This approach ensures that the network structure remains consistent regardless of population sizes, thereby enhancing the algorithm’s scalability. Extensive simulations demonstrate that HyperQMIX significantly outperforms state-of-the-art algorithms in terms of learning efficiency and converged performance. Moreover, agents pretrained with HyperQMIX perform well in tasks of different population sizes without additional training. Fine-tuning these models achieves performance comparable to training from scratch.

With the development of wireless communication and Internet of Things (IoT), there are massive wireless devices that need to share the limited spectrum resources. Dynamic spectrum access (DSA) is a promising paradigm to remedy the problem of inefficient spectrum utilization brought upon by the historical command-and-control approach to spectrum allocation. In this article, we investigate the distributed DSA problem for multiusers in a typical multichannel cognitive radio network. The problem is formulated as a decentralized partially observable Markov decision process (Dec-POMDP), and we propose a centralized off-line training and distributed online execution framework based on cooperative multi-agent reinforcement learning (MARL). We employ the deep recurrent$Q$-network (DRQN) to address the partial observability of the state for each cognitive user. The ultimate goal is to learn a cooperative strategy which maximizes the sum throughput of a cognitive radio network in a distributed fashion without information exchange between cognitive users. Finally, we validate the proposed algorithm in various settings through extensive experiments. The experimental results show that the proposed CoMARL-DSA algorithm outperforms the state-of-the-art deep$Q$-learning for spectrum access (DQSA) in terms of successful access rate and collision rate by at least 14% and 12%, respectively.

With innovative applications of unmanned aerial vehicle (UAV) ad hoc networks in various areas, their demands on broad bandwidth, large capacity and low latency become prominent. The combination of millimeter wave, directional antenna and time division multiple access techniques, which enables concurrent transmissions, is promising to deal with it. In this paper, we study the resource allocation problem in UAV ad hoc networks. Specifically, the slot assignment, antenna boresight and transmit power are jointly optimized to promote the network capacity. First, we formulate the optimization problem as the maximization of the fairness-weighted network capacity, subject to the constraint on priority guarantee. Then, because the formulated problem is a mixed integer non-linear programming problem (MINLP), which is NP-hard, two algorithms called dual-based iterative search algorithm (DISA) and sequential exhausted allocation algorithm (SEAA) are respectively proposed to efficiently solve it with acceptable complexity. DISA slacks the MINLP into a continuous-variable optimization problem and solves it with the Lagrangian dual method in an iterative manner. As a heuristic method, SEAA schedules links sequentially, i.e., from high-priority to low-priority ones. Numerical results demonstrate that both DISA and SEAA can efficiently allocate resources for UAVs, while guaranteeing the fairness and priority of links.

The heterogeneous cloud radio access network (H‐CRAN) is considered a promising solution to expand the coverage and capacity required by fifth‐generation (5G) networks. UAV, also known as wireless aerial platforms, can be employed to improve both the network coverage and capacity. In this paper, we integrate small drone cells into a H‐CRAN. However, new complications and challenges, including 3D drone deployment, user association, admission control, and power allocation, emerge. In order to address these issues, we formulate the problem by maximizing the network throughput through jointly optimizing UAV 3D positions, user association, admission control, and power allocation in H‐CRAN networks. However, the formulated problem is a mixed integer nonlinear problem (MINLP), which is NP‐hard. In this regard, we propose an algorithm that combines the genetic convex optimization algorithm (GCOA) and particle swarm optimization (PSO) approach to obtain an accurate solution. Simulation results validate the feasibility of our proposed algorithm, and it outperforms the traditional genetic and K‐means algorithms.

Due to the flexibility in 3-D space and high probability of line-of-sight (LoS) in air-to-ground communications, unmanned aerial vehicles (UAVs) have been considered as means to support energy-efficient data collection. However, in emergency applications, the mission completion time should be main concerns. In this article, we propose a UAV-aided data collection design to gather data from a number of ground users (GUs). The objective is to optimize the UAV’s trajectory, altitude, velocity, and data links with GUs to minimize the total mission time. However, the difficulty lies in that the formulated time minimization problem has mutual effect with trajectory variables. To tackle this issue, we first transform the original problem equivalently to the trajectory length problem and then decompose the problem into three subproblems: 1) altitude optimization; 2) trajectory optimization; and 3) velocity and link scheduling optimization. In the altitude optimization, the aim is to maximize the transmission region of GUs which can benefit trajectory designing; then, in the trajectory optimization, we propose a segment-based trajectory optimization algorithm (STOA) to avoid repeat travel; besides, we also propose a group-based trajectory optimization algorithm (GTOA) in large-scale high-density GU deployment to relieve massive computation introduced by STOA. Then, the velocity and link scheduling optimization is modeled as a mixed-integer nonlinear programming (MINLP) and block coordinate descent (BCD) is employed to solve it. Simulations show that both STOA and GTOA achieve shorter trajectory compared with the existing algorithm and GTOA has less computational complexity; besides, the proposed time minimization design is valid by comparing to the benchmark scheme.

Exploiting unmanned aerial vehicles (UAVs) as flying base stations (BSs) to assist the terrestrial cellular networks is promising in 5G and beyond. Despite the inherent potentials, one challenging problem is how to optimally deploy multiple UAVs to achieve on-demand coverage for ground user equipment (UE). In this article, we model the deployment problem as minimizing the number of UAVs and maximizing the load balance among them, which is subject to two main constraints, i.e., UAVs should form a robust backbone network and they should keep connected with the fixed BSs. To solve this optimization problem with low complexity, we decompose the problem into two subproblems and propose a hybrid algorithm to solve them stepwise. First, a centralized greedy search algorithm is used to heuristically obtain the minimum number of UAVs and their suboptimal positions in a discontinuous space. Then, a distributed motion algorithm is adopted which enables each UAV to autonomously control its motion toward the optimal position in a continuous space. The proposed algorithm is applicable to various scenarios where UAVs are deployed alone or with fixed BSs regardless of the UE distribution. Extensive simulations validate the proposed algorithm.

Due to the flying nature of unmanned aerial vehicles (UAVs), it is very attractive to deploy UAVs as aerial base stations and construct airborne networks to provide service for on-ground users at temporary events (such as disaster relief, military operation, and so on). In the constructing of UAV airborne networks, a challenging problem is how to deploy multiple UAVs for on-demand coverage while at the same time maintaining the connectivity among UAVs. To solve this problem, we propose two algorithms: a centralized deployment algorithm and a distributed motion control algorithm. The first algorithm requires the positions of user equipments (UEs) on the ground and provides the optimal deployment result (i.e., the minimal number of UAVs and their respective positions) after a global computation. This algorithm is applicable to the scenario that requires a minimum number of UAVs to provide desirable service for already known on-ground UEs. Differently, the second algorithm requires no global information or computation, instead, it enables each UAV to autonomously control its motion, find the UEs and converge to on-demand coverage. This distributed algorithm is applicable to the scenario where using a given number of UAVs to cover UEs without UEs' specific position information. In both algorithms, the connectivity of the UAV network is maintained. Extensive simulations validate our proposed algorithms.

Cognitive radio (CR) has emerged as a promising technology to increase the utilization of spectrum resource. A pivotal challenge in CR lies on secondary users' (SU) finding each other on the frequency band, i.e., the spectrum locating. In this demo, we implement two kinds of multi-channel rendezvous technology to solve the problem of spectrum locating: (i) the common control channel (CCC) based rendezvous scheme, which is simple and effective when a control channel is always available; and (ii) the channel-hopping (CH) based blind rendezvous, which could also obtain guaranteed rendezvous on all commonly available channels of pairwise SUs in a short time without a CCC. Furthermore, the cognitive nodes in the demonstration could adjust their communication channels autonomously according to the dynamic spectrum environment for continuous data transmission.

This paper describes our demonstration of a self-adaptive network architecture reconfiguration technology in Cognitive Radio Networks (CRNs) under complex and vicious environment. The technology enables CRN to switch between three kinds of typical network architectures, i.e., centralized, ad hoc and cooperative relay in an autonomous and flexible way. This self-adaptive switch can solve the problem of physical breakdown and invalid communication link in vicious network environment, and hence enhance the network robustness. We implement the technology on a CRN testbed consisting of GNU Radio and USRPs and verify its performance including switching time and throughput.

In order to improve the connectivity and survivability of Cognitive Radio Networks (CRNs) under complex and vicious communication environment, we propose a network architecture self-adaption technology. The technology enables CRN to switch between three kinds of architectures, i.e., centralized, single-hop ad hoc and cooperative relay in an autonomous and flexible way. This self-adaptive switching can deal with physical breakdown and invalid communication link due to great distance or spectrum heterogeneity, and enhance the network robustness thereby. The work patterns of each architecture and the switching scheme between them were demonstrated, and theoretical switching time was also calculated. Moreover, a testbed based on GNU Radio and USRPs was set up to test its performance including switching time and throughput. Testing results prove the effectiveness of the technology.