Long-Term Energy Consumption Minimization Based on UAV Joint Content Fetching and Trajectory Design†

This paper studies the multiple unmanned aerial vehicles (UAVs) trajectory design for post-disaster cooperative video inspection and information uploading. The locations to be inspected are modeled as points of interest (PoIs) with heterogeneous resolution requirements. The three-dimensional (3D) trajectories for multiple cooperative UAVs are designed to minimize the time for successfully inspecting all the PoIs and maximize the communication time for the video uploading. An UAV inspection model is built to clarify the criteria for successful inspection, which is related to the UAV's 3D positions and velocity. The proposed UAV trajectory design involves two steps. First, the PoIs in proximity are grouped using convex optimization. Then, the multiple UAV trajectories are jointly designed by solving a multiple traveling salesman problem (MTSP). Numerical results show that the proposed design can significantly reduce the mission completion time.

Due to flexible scheduling, and better transmission channel, unmanned aerial vehicle (UAV) can improve transmit performance of Internet of Things (IoT). In this paper, we propose an UAV-enabled multichannel IoT based on intelligent fog computing, where UAV as a relay forwards IoT's information to the data center under the control of fog computing base station in the case of terrestrial channel fading. The IoT's throughput are maximized by jointly optimizing subcarrier, power of IoT, and UAV, and UAV trajectory, subject to the constraints of information causality, maximum transmit power, and maximum UAV speed. The subcarriers are dynamically allocated according to their channel gains, the water filling algorithm is adopted to optimize the power for UAV, and IoT by fixing UAV trajectory, and the optimal UAV trajectory is achieved with successive convex approximation under the fixed power allocation. Then a jointly iterative optimization on subcarrier, power, and trajectory is presented to get the optimal solution. In addition, we propose a fairness optimization scheme to maximize the minimum transmit rate of IoT nodes. The simulations indicate the IoT with mobile UAV, and dynamic subcarrier allocation may achieve better transmit performance, and the fairness optimization can decrease the rate difference of nodes effectively.

UAV-enabled reliable mobile relaying under the time-varying Rician fading channel

Join trajectory optimization and communication design for UAV-enabled OFDM networks

Unmanned aerial vehicle (UAV)-enabled vehicular communications in the sixth generation (6G) are characterized by line-of-sight (LoS) and dynamically varying channel conditions. However, the presence of obstacles in the LoS path leads to shadowed fading environments. In UAV-assisted cellular vehicle-to-everything (C-V2X) communication, vehicle and UAV mobility and shadowing adversely impact latency and throughput. Moreover, 6G vehicular communications comprise data-intensive applications such as augmented reality, mixed reality, virtual reality, intelligent transportation, and autonomous vehicles. Since vehicles’ sensors generate immense amount of data, the latency in processing these applications also increases, particularly when the data are not independently identically distributed (non-i.i.d.). Furthermore, when the sensors’ data are heterogeneous in size and distribution, the incoming packets demand substantial computing resources, energy efficiency at the UAV servers and intelligent mechanisms to queue the incoming packets. Due to the limited battery power and coverage range of UAV, the quality of service (QoS) requirements such as coverage rate, UAV flying time, and fairness of vehicle selection are adversely impacted. Controlling the UAV trajectory so that it serves a maximum number of vehicles while maximizing battery power usage is a potential solution to enhance QoS. This paper investigates the system performance and communication disruption between vehicles and UAV due to Doppler effect in the orthogonal time–frequency space (OTFS) modulated channel. Moreover, a low-complexity UAV trajectory prediction and vehicle selection method is proposed using federated learning, which exploits related information from past trajectories. The weighted total energy consumption of a UAV is minimized by jointly optimizing the transmission window (), transmit power and UAV trajectory considering Doppler spread. The simulation results reveal that the weighted total energy consumption of the OTFS-based system decreases up to 10% when combined with federated learning to locally process the sensor data at the vehicles and communicate the processed local models to the UAV. The weighted total energy consumption of the proposed federated learning algorithm decreases by 10–15% compared with convex optimization, heuristic, and meta-heuristic algorithms.

With the rapid development of mobile edge computing (MEC) and unmanned aerial vehicle (UAV) communication networks, UAV-assisted edge computing has emerged as a promising paradigm for low-latency and energy-efficient computation. However, the time-varying nature of air-to-ground channels and the coupling between UAV trajectories and computation offloading decisions significantly increase system complexity. To address these challenges, this paper proposes an Adaptive UAV Edge Intelligence Framework (AUEIF) for joint UAV computation offloading and trajectory optimization under dynamic channels. Specifically, a dynamic graph-based system model is constructed to characterize the spatio-temporal correlation between UAV motion and channel variations. A hierarchical reinforcement learning-based optimization framework is developed, in which a high-level actor–critic module is responsible for generating coarse-grained UAV flight trajectories, while a low-level deep Q-network performs fine-grained optimization of task offloading ratios and computational resource allocation in real time. In addition, an adaptive channel prediction module leveraging long short-term memory (LSTM) networks is integrated to model temporal channel state transitions and to assist policy learning and updates. Extensive simulation results demonstrate that the proposed AUEIF achieves significant improvements in end-to-end latency, energy efficiency, and overall system stability compared with conventional deep reinforcement learning approaches and heuristic-based schemes while exhibiting strong robustness against dynamic and fluctuating wireless channel conditions.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent strategy where a UAV equipped with an MEC server is deployed to serve terminal devices. This paper considers a multi-UAV assisted network in which multiple UAVs and a terrestrial base station (BS) are deployed to provide MEC services to mobile users. The objective is to minimize an energy and latency-based cost function by jointly optimizing task offloading and MEC server selection decision, transmission power, UAV trajectory, and CPU frequency allocation. An alternating iterative approach based on the block descent method is proposed to solve this problem. In the first layer, task offloading and server selection decision subproblem is solved using a game theoretic approach. The second layer handles offloading and downloading transmission power allocations by utilizing a simplistic geometric waterfilling (GWF) technique, and the UAV trajectory by successive convex approximation (SCA). Whereas, the third layer solves the computation resource subproblem by performing CPU frequency allocation using a gradient descent method. The proposed method uses a segment-by-segment approach, which divides the entire UAV flight trajectory into shorter timeframe segments to reduce the computation time. Simulation results are presented to show that the proposed approach outperforms various benchmark schemes.

The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) is a promising paradigm for providing extensive coverage and additional computation services to the Internet of Things (IoT) devices with smart applications, which are normally computation-intensive and delay-critical, especially in some remote areas or emerging scenarios without available infrastructure. However, considering the restricted computation capabilities of UAVs, even multiple UAVs may not satisfy the quality of service (QoS) requirement of IoT users with heavy-computation applications. In this paper, a heterogenous-multiple-UAVs enabled collaborative aerial-ground MEC framework is proposed. The total computation bits maximization problem under the time-division multiple access (TDMA) scheme and partial offloading mode is formulated by jointly optimizing the user association, the CPU-cycle frequencies of users and computing UAVs, the transmit power of users and UAVs, the offloading time of users and UAVs, as well as the UAVs' trajectories. Due to the non-convexity of the original problem with the nonlinear coupling of different variables, a two-layered alternative optimization (TLAO) algorithm is proposed, where the outer layer optimizes the user association and resource allocation, while the inner layer optimizes the UAV trajectory scheduling. Simulation results demonstrate that our proposed TLAO scheme can improve the total computation bits compared with other benchmark schemes, and achieve fast convergence. Moreover, comparative results also verify that our proposed strategy is flexible to apply to homogeneous multi-UAVs and single-UAV scenarios.

With line-of-sight mode deployment and fast response, unmanned aerial vehicle (UAV), equipped with the cutting-edge integrated sensing and communication (ISAC) technique, is poised to deliver high-quality communication and sensing services in maritime emergency scenarios. In practice, however, the real-time transmission of ISAC signals at the UAV side cannot be realized unless the reliable wireless fronthaul link between the terrestrial base station and UAV are available. This paper proposes a multicarrier-division duplex based joint fronthaul-access scheme, where mutually orthogonal subcarrier sets are leveraged to simultaneously support four types of fronthaul/access transmissions. In order to maximize the end-to-end communication rate while maintaining an adequate sensing quality-of-service (QoS) in such a complex scheme, the UAV trajectory, subcarrier assignment and power allocation are jointly optimized. The overall optimization process is designed in two stages. As the emergency area is usually far away from the coast, the optimal initial operating position for the UAV is first found. Once the UAV passes the initial operating position, the UAV’s trajectory and resource allocation are optimized during the mission period to maximize the end-to-end communication rate under the constraint of minimum sensing QoS. Simulation results demonstrate the effectiveness of the proposed scheme in dealing with the joint fronthaul-access optimization problem in maritime ISAC networks, offering the advantages over benchmark schemes.

Wireless communication is an essential technology to unlock the full potential of unmanned aerial vehicles (UAVs) in numerous applications and has thus received unprecedented attention recently. Although technologies such as direct link, WiFi, and satellite communications are still useful in some remote scenarios where cellular services are unavailable, it is believed that exploiting the thriving 5G and beyond cellular networks to support UAV communications is the most promising and cost-effective approach, especially when the number of UAVs grows dramatically. On the one hand, to guarantee safe and efficient flight operations of multiple UAVs, it is of paramount importance to provide secure and ultra-reliable communication links between the UAVs and their ground pilots or control stations for conveying command and control signals, especially in beyond-visual-line-of-sight (BVLOS) scenarios. On the other hand, because of advances in communication equipment miniaturization as well as UAV manufacturing, mounting compact and lightweight base stations (BSs) or relays on UAVs becomes increasingly feasible. This has led to two promising research paradigms for UAV communications, namely, UAV-assisted cellular communications and cellular-connected UAVs, where UAVs are integrated into cellular networks as aerial communication platforms and aerial users, respectively. As such, integrating UAVs into cellular networks is believed to be a win-win technology for both UAV-related industries and cellular network operators, which not only creates plenty of new business opportunities but also benefits the communication performance of 3-D wireless networks. In addition, UAV related sensing and computing are also helpful for achieving efficient and reliable communication (e.g., in avoiding coverage holes) as well as smart UAV coordination, positioning, and trajectory design. However, 5G and beyond wireless networks with UAVs significantly differs from traditional communication systems, because of the high altitude and high maneuverability of UAVs, the unique UAV-ground channels, the diversified quality of service (QoS) requirements for downlink command and control (C&C) and uplink mission-related data transmission, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the new design degrees of freedom enabled by joint UAV mobility control and communication resource allocation.

In this paper, we study the resource allocation and trajectory design for secure unmanned aerial vehicle (UAV)-enabled communication systems, where multiple multi-purpose UAV base stations are dispatched to provide secure communications to multiple legitimate ground users (GUs) in the existence of multiple eavesdroppers (Eves). Specifically, by leveraging orthogonal frequency division multiple access (OFDMA), active UAV base stations can communicate to their desired ground users via the assigned subcarriers while idle UAV base stations can serve as jammer simultaneously for communication security provisioning. To achieve fairness in secure communication, we maximize the average minimum secrecy rate per user by jointly optimizing the communication/jamming subcarrier allocation policy and the trajectory of UAVs, while taking into account the constraints on the minimum safety distance among multiple UAVs, the maximum cruising speed, the initial/final locations, and the existence of cylindrical no-fly zones (NFZs). The design is formulated as a mixed integer non-convex optimization problem which is generally intractable. Subsequently, a computationally-efficient iterative algorithm is proposed to obtain a suboptimal solution. Simulation results illustrate that the performance of the proposed iterative algorithm can significantly improve the average minimum secrecy rate compared to various baseline schemes.

Leveraging unmanned aerial vehicles (UAVs) for access and high altitude platform stations (HAPSs) for data backhaul to construct the Air-Ground Integrated Network (AGIN), is a feasible solution to achieve seamless network coverage for remote IoT devices in future 6 G era. However, since the number of terminals increases exponentially, it is essential to improve spectrum efficiency and throughput of the system. In addition, the limited on-board energy storage of UAVs and finite battery of IoT terminals make the system energy efficiency (EE) a new concern. To cope with the above mentioned challenges, in this study we first put forward a clustered-NOMA (C-NOMA)-enabled heterogeneous AGIN model for remote areas including one HAPS for backhaul and multiple UAVs for access, where C-NOMA is used to obtain both improved system throughput and terminal complexity. Then, we study the joint UAV trajectory plan and resource allocation problem in order to maximize the system EE. Since it is a mixed integer nonlinear programming (MINLP) issue coupled with transmission power, subchannel allocation, UAV trajectory and speed control, this problem is decoupled into two subproblems and solved iteratively. For the first one, the optimal channel and power strategy is obtained by our subchannel allocation and power control for AGIN EE (SAPAE) maximum algorithm according to Lagrange dual decomposition method. For the second, for transforming the nonconvex problem into a convex one, we provide the successive convex approximation-based UAV trajectory and speed optimization for AGIN EE (SUTSAE) maximum algorithm, and then the near-optimal UAV trajectory and flight speed are obtained. We conduct extensive experiments to compare with other benchmark methods. It is shown that the proposed approach is better in EE and spectrum efficiency.

The ultra-reliable and low-latency communication (uRLLC) plays a vital role in road safety and traffic management of intelligent transportation systems (ITS). Integrating unmanned aerial vehicles (UAVs) and non-orthogonal multiple access (NOMA) has become a prospective strategy for uRLLC by separately establishing line-of-sight (LoS) link and simultaneous transmissions. The confidentiality and security of information transmission in the context of uRLLC faces serious challenges, due to the openness nature of the wireless medium. Different from traditional encryption methods, featured by high computing complexity and serious processing delay, physical layer security (PLS) exploits the inherent properties of the wireless medium to prevent transmitted messages from being eavesdropped. In addition, NOMA-UAV systems pose unique challenges for secure transmission due to potential stronger LoS channels between the source and the eavesdroppers. Therefore, in this paper, we investigate the confidentiality and security of a NOMA-UAV uRLLC system, under which an UAV serves two types of users with different requirements, i.e., secret users (both security and reliability) and public users (only reliability). Based on the tools of stochastic geometry, we address the challenges of secure communications caused by the mobility of UAV, and derive the closed-form expression of secrecy rate maximization, a key metric for observing the key system parameters on the security and reliability. Furthermore, joint design of UAV trajectory and transmit power is proposed to enhance the system security. Finally, compared with popular PLS scheme empowered by UAV trajectory design, the proposed scheme is evaluated by numerical simulations for theoretical analyses and effectiveness in practice.

International audience

This paper investigates a new scenario of spectrum sharing between unmanned\naerial vehicle (UAV) and terrestrial wireless communication, in which a\ncognitive/secondary UAV transmitter communicates with a ground secondary\nreceiver (SR), in the presence of a number of primary terrestrial communication\nlinks that operate over the same frequency band. We exploit the UAV's mobility\nin three-dimensional (3D) space to improve its cognitive communication\nperformance while controlling the co-channel interference at the primary\nreceivers (PRs), such that the received interference power at each PR is below\na prescribed threshold termed as interference temperature (IT). First, we\nconsider the quasi-stationary UAV scenario, where the UAV is placed at a static\nlocation during each communication period of interest. In this case, we jointly\noptimize the UAV's 3D placement and power control to maximize the SR's\nachievable rate, subject to the UAV's altitude and transmit power constraints,\nas well as a set of IT constraints at the PRs to protect their communications.\nNext, we consider the mobile UAV scenario, in which the UAV is dispatched to\nfly from an initial location to a final location within a given task period. We\npropose an efficient algorithm to maximize the SR's average achievable rate\nover this period by jointly optimizing the UAV's 3D trajectory and power\ncontrol, subject to the additional constraints on UAV's maximum flying speed\nand initial/final locations. Finally, numerical results are provided to\nevaluate the performance of the proposed designs for different scenarios, as\ncompared to various benchmark schemes. It is shown that in the quasi-stationary\nscenario the UAV should be placed at its minimum altitude while in the mobile\nscenario the UAV should adjust its altitude along with horizontal trajectory,\nso as to maximize the SR's achievable rate in both scenarios.\n