Due to the high mobility of vehicles, the frequent path failures caused by dynamic network topology, and a variety of obstructions, efficient data dissemination with delay constraint in vehicular ad hoc networks (VANETs) is a challenging issue. To address these problems, a novel mobile relaying technique by employing unmanned aerial vehicles (UAVs) is considered to facilitate data dissemination in vehicular environments where the communication infrastructures are not available or the network connectivity is poor. This paper studies and formulates the throughput maximization problem in UAV-assisted VANETs, which aims to achieve high throughput while guarantee the delay constraint of data flows to the vehicles in the area. To maximize the network throughput, the maximization problem tries to find an optimal delivery strategy for data dissemination by optimizing the transmission rate. To solve the problem, the knapsack problem can be reduced to the maximization problem, which is proved NP-hard. A polynomial time approximation scheme is proposed to achieve an approximate solution. Detailed theoretical analysis including time complexity and approximation ratio of the proposed algorithm is presented. Simulation results demonstrate the effectiveness of the proposed algorithm.

Non-orthogonal multiple access (NOMA) is indeed an important technique of 5 G and beyond 5 G (B5G) communication systems, as it can increase the spectrum efficiency and system capacity under the same bandwidth. Long-distance unmanned aerial vehicle (UAV) networks, comprising a base station and multiple UAVs, are commonly used for reconnaissance purposes due to the extended flight range of the UAVs, which can provide broad coverage and satisfy the requirements for real-time communication. To alleviate spectrum underutilization of single channel orthogonal multiple access, we investigate resource allocation in UAV networks by utilizing NOMA and then formulate an optimization problem that maximizes the network capacity. The formulated problem can be decomposed into two sub-problems, namely UAV grouping and power allocation optimization. Accordingly, we present a UAV grouping method with low complexity according to the difference of channel gain between UAVs and base station, in which the UAVs with larger difference will be in the same cluster such that they can transmit signals over the same channel. Then, a power allocation optimization algorithm based on Lagrange multiplier method is then used to optimize the transmit power of UAVs utilizing the same channel. This algorithm considers the quality of service requirements for each UAV, and assigns power levels that maximize the overall network capacity. Simulation results have shown that the presented algorithms improve network capacity by approximately 19% compared with that of orthogonal multiple access, which demonstrates the effectiveness of utilizing NOMA in long-distance UAV networks.

Reconfigurable intelligent surface (RIS) is playing a growing and ever-more significant role in constructing the six-generation (6G) wireless networks due to its properties of low cost and easy integration. Current studies about RIS-assisted communication generally assume the RISs are deployed at fixed position or devices, however, with the rapid development of unmanned aerial vehicle (UAV) technology, this readily flying wireless access platform is increasingly used to realize reliable communication. If the RISs are mounted on the random 3-D (three-dimension) mobile UAVs, how to investigate the uplink transmission of the RIS-assisted UAV communication systems would be a great challenge. To resolve this open issue, we establish a novel theoretical model to analyze the uplink performance of the RIS-assisted UAV communication systems with the random 3-D mobile pattern. In the modeling process, we first provide a random 3-D mobile model for UAVs, where the random waypoint and uniform mobility (UM) models are simultaneously used. Next, we build an end-to-end (E2E) transmission model for the RIS-assisted UAV communication system, where the impacts of channel fading type, RIS configuration, UAV’s mobility, and association policies are comprehensively considered. Combining the above two models, we derive the analytical expressions of uplink transmission metrics, and make a bound performance analysis on this base. Finally, we evaluate the uplink performance of the RIS-assisted UAV communication system with the random 3-D mobile pattern, and verify the proposed theoretical model.

Vehicular ad hoc networks (VANETs) are characterized by dynamic behavior and unique characteristics that contribute to the complexity of network connectivity and reachability, thereby posing substantial challenges to achieving efficient data transmission. While one avenue for understanding these network properties involves the application of time-varying graphs, comprehending such methods may be hindered by absence of well-defined analogies to established properties of static graphs. Thereby, this study addresses this gap by exploring the properties of time-varying graphs as a means to depict network characteristics, aiming to enhance data dissemination efficiency within vehicular networks assisted by unmanned aerial vehicles (UAVs). Considering delay-sensitive data dissemination, we integrate dissemination delay into the time-varying graph, introducing the concept of a delay-constrained time-varying graph (DTVG). In this context, a data dissemination problem is formulated to optimize data transmission utility. To address this problem, we propose a novel data dissemination algorithm leveraging insights from $k$ -token dissemination problem. Theoretical analysis is employed to derive both upper and lower bounds of our proposed algorithm. Simulations are conducted for validating the feasibility of characterizing dynamic networks based on time-varying graphs. Finally, the effectiveness of our proposed approach is demonstrated through the evaluation of metrics such as dissemination delay, delivery ratio, and network throughput.