Flying ad hoc networks (FANETs) provide high flexibility and real-time wireless communication solutions for multiunmanned aerial vehicle (UAV) systems by utilizing UAVs as routers. However, conventional routing protocols are inadequate for FANETs due to high mobility and dynamic topology of UAV networks. To address these challenges, this paper proposes an enhanced on-demand predictive (EDP) routing protocol for UAV networks. The EDP protocol incorporates a neighbor-coverage-based predictive flooding mechanism and an adaptive link-quality-based route maintenance method. The flooding mechanism utilizes Kalman filter theory to predict mobility and the route maintenance method selects optimal route by evaluating multiple factors. Simulation results demonstrate that the EDP protocol significantly improves the packet delivery rate while reducing network delay and overhead under varying environments, outperforming benchmark routing protocols for FANETs.

Emerging applications are placing increasing demands on wireless networks, particularly in terms of ensuring reliable communication for control-related information. However, the complexity of network architectures and the growing number of user devices present significant challenges in achieving reliable multiple access. In this paper, we present a framework that utilizes machine learning (ML) to meet the need for reliable access in unmanned aerial vehicle (UAV) networks. The K-means algorithm is employed to cluster users according to their communication reliability requirements, grouping together users with similar demands within each cluster. Each cluster adopts a different access strategy: clusters with lower reliability requirements utilize non-orthogonal multiple access to enhance spectrum efficiency, while clusters with higher reliability requirements employ orthogonal multiple access to ensure reliability. Taking into account the impact of UAV altitude and power allocation schemes on reliability, we propose an iterative algorithm to optimize the UAV altitude and power allocation factors, aiming to maximize UAV coverage while meeting the users’ reliability requirements. The simulation results validate the effectiveness of the proposed ML-based reliable access scheme, highlighting its potential to enhance the design and deployment of reliable communication in future UAV networks.

Under the advancement of emerging communication technologies, the utilization of Internet of Things (IoT) is progressively expanding across diverse domains. The age of information (AoI) stands as a crucial measurement in evaluating the efficiency of IoT networks. For efficient and reliable data collection, Unmanned aerial vehicle (UAV) have been extensively applied in IoT networks. However, the escalating number of sensor nodes and random data sampling mode within IoT networks have made it challenging for UAV trajectory planning with the constraint of energy consumption. In response to this challenge, our solution entails an attention-based actor-critic algorithm for multi-UAV path planning in a pre-clustered IoT network, which takes into account both the average AoI of clusters and the energy consumption of each UAV. The simulation outcomes validate that our algorithm achieves a trade-off between the information freshness and energy consumption in the multi-UAV data gathering scenario.

The development of wireless communication technology is reshaping the landscape of intelligent transportation systems, particularly in the realm of internet of vehicles (IoV). Among these, integrated sensing and communications (ISAC) has garnered widespread attention by leveraging shared hardware resources or even spectrum between sensing and communication to achieve integrated benefits. For IoV, parameters such as target position and speed estimated by ISAC can be used as prior information for resource allocation and beamforming to improve communication performance. In this paper, we focus on ISAC-enabled IoV, where radar sensing signals and communication signals are transmitted within different slots of a subframe to avoid interference. We propose a resource allocation scheme to maximize system throughput while meeting the differentiated needs of all users, where spatial division multiple access is dynamically employed based on network load. Simulation outcomes verify the efficiency of the suggested algorithm.

Efficient data processing is crucial for industrial Internet of Things (IIoT) applications, but the limited energy and computing resources in IIoT devices (IIoT-Ds) pose constraints. This article utilizes a unmanned aerial vehicle (UAV) as a computing server for enhanced IIoT mission execution. Specifically, the energy consumption of IIoT-Ds and the UAV, as well as the weighted cost of the communication and computing scheduling strategy in the UAV-aided IIoT, are jointly taken into account. An optimization problem based on the system energy consumption is built under the constraints of UAV motion, computing offloading, and transmitting power allocation. A problem decoupling-based alternating optimization method is proposed to solve the minimization problem by decomposing it into three subproblems: 1) UAV motion optimization; 2) computing offloading configuration; and 3) transmitting power allocation. Through comparing the proposed communication and computing scheduling strategy with existing methods, simulation results illustrate its attainment of quasi-optimal performance, thereby validating the effectiveness of the alternating optimization method.

Non-orthogonal multiple access (NOMA) is regarded as a promising technology in achieving high capacity and massive connectivity. In this paper, the reliable transmission scheme of downlink NOMA systems is investigated. In particular, we divide the disc covered by the base station into several annular areas, where the receivers are randomly located following a uniform distribution. In this way, NOMA pairing is performed by randomly selecting receivers from two different areas. Firstly, we derive the closed-form expressions of bit error rate (BER) with quadrature phase-shift keying (QPSK) modulation, where the channel is modeled as small-scale Rayleigh fading and large-scale path loss. To achieve reliable communications, then, the BER performance of the receiver with the worst channel gain in each area is studied. Finally, an optimal power allocation algorithm is proposed, which obtains the minimum transmission power and optimal power allocation factor with a given BER constraint of all receivers. Extensive simulations demonstrate the accuracy of obtained BER expressions and the effectiveness of the proposed algorithm. These results provide valuable insight into realizing on reliable transmission of NOMA with randomly deployed receivers.

Mobile edge computing (MEC) has been recognized as an effective technology to augment computing capabilities to user equipments (UEs). Although some existing works have been done on MEC, but only few works focus on the area of full duplex (FD) enabled Heterogeneous Networks (HetNets), which is an important scenario in future 5G networks. In this paper, we integrate FD technology in a MEC enabled HetNets. Firstly, a maximization optimization problem of users' revenues is formulated, in which user association, computation offloading strategy policy, uplink FD transmission power allocation, and computation resource scheduling are all considered. Secondly, due to the original optimization problem is NP-hard and non-convex, we transform it into two sub problems, named user allocation optimization problem (UAOP) and resource allocation optimization problem (RAOP). Moreover, RAOP is proved as a convex problem and its optimal resource allocation solution is found by Lagrangian multiplier method. Finally, a greedy-based optimization algorithm (GBOA) is proposed to solve the original problem. The effectiveness of the proposed scheme is demonstrated by simulation results with different system parameters.