最新刊期
卷 8 ， 期 3 ， 2024
- 摘要：To exploite reusing a single unmanned aerial vehicle (UAV) to simultaneously act as an aerial base station and anchor point to assist the terrestrial emergency network, a novel integrated air-ground emergency support network was built with joint communication and localization service. The UAV position deployment optimization problem in the integrated network was studied, with a focus on resolving the fundamental and conflicting impacts of UAV deployment to the communication and localization performance. Firstly, a D-optimality based localization accuracy metric was adopted to replace the conventional Cramer-Rao lower bound (CRLB), and a UAV deployment optimization problem that maximizes the network throughput was formulated under individual localization accuracy and communication rate constraints. The closed-form expression of feasible deployment region of a UAV was derived that could satisfy the localization accuracy requirements of ground user, which was shown to be a second-order cone with the target user being the vertex, and proposed efficient deployment algorithm accordingly. Then, a 3D city map assisted deployment scheme was proposed to address the inaccurate air-ground channel modeling problems. The proposed deployment method is analytically tractable for deriving the optimal deployment solution, and has intuitive geometric structure to facilitate fast and performance-guaranteed deployment of integrated air-ground networks to satisfy the communication and localization performance requirements of ground users.关键词：unmanned aerial vehicle;integrated communication and localization;emergency network;deployment optimization766|1009|0更新时间：2025-03-13
- 摘要：With the advancement of the low-altitude economy and the widespread application of unmanned aerial vehicle (UAV), regulatory challenges for UAV flights have significantly increased. Integrated sensing and communication (ISAC) technology is expected to solve the above problems. For this reason, the 3rd Generation Partnership Project (3GPP) had identified low-altitude UAV as one of the important application scenarios of ISAC technology. However, the fast flight speed, small size and high altitude of UAV could adversely affect the strength and quality of the received sensing signals. Consequently, there was an urgent need to investigate the path loss characteristics of the UAV sensing channel and develop effective model. Mono-static sensing channel measurements for UAV at frequencies of 15 GHz and 28 GHz were conducted. The impact of horizontal distance, vertical altitude differences, and the radar cross section (RCS) of the target on path loss were examined. The findings revealed low-coupling characteristics between the target and background channels. Subsequently, the path loss and distance model for the UAV sensing target channel was established, contributing to the ongoing progress of 3GPP standardization.关键词：unmanned aerial vehicle;integrated sensing and communication;path loss;3GPP standadization834|603|0更新时间：2025-03-13
- 摘要：In cell-free massive multiple-input multiple-output (CF-mMIMO) system, unmanned aerial vehicles (UAV), serving as mobile access point (AP), are increasingly playing a significant role in both communication and task execution. To enhance the endurance of UAV during complex mission executions, the energy transmission and trajectory design for UAV within a cell-free mMIMO system were explored, utilizing wireless power transmission (WPT) as the foundation. With a focus on maintaining communication fairness for users experiencing outages, energy consumption constraints as UAV receive power replenishments from access points throughout their missions were considered. Simultaneously, a joint optimization encompassing the UAV's flight trajectory, charging/discharging time slots, and beamforming configurations were conducted. To address this complex problem, an angle search-based communication-assisted deep Q-network (DQN) algorithm was proposed, facilitating a targeted spatial exploration. Simulation results demonstrate that, while balancing endurance and communication requirements, this algorithm significantly elevates the utilization rate of UAV and enhances communication fairness for interrupted user equipment (UE), ultimately achieving dynamic regional coverage.关键词：cell-free massive MIMO system;dynamic coverage;unmanned aerial vehicle;trajectory design;wireless power transmission187|167|0更新时间：2025-03-13
- 摘要：Active search with unmanned aerial vehicle (UAV) swarms in cluttered and unpredictable environments poses a critical challenge in search and rescue missions, where the rapid localizations of survivors are of paramount importance, as the majority of urban disaster victims are surface casualties. However, the altitude-dependent sensor performance of UAV introduces a crucial trade-off between coverage and accuracy, significantly influencing the coordination and decision-making of UAV swarms. The optimal strategy has to strike a balance between exploring larger areas at higher altitudes and exploiting regions of high target probability at lower altitudes. To address these challenges, collaborative altitude-adaptive reinforcement learning (CARL) was proposed which incorporated an altitude-aware sensor model, a confidence-informed assessment module, and an altitude-adaptive planner based on proximal policy optimization (PPO) algorithms. CARL enabled UAV to dynamically adjust their sensing location and made informed decisions. Furthermore, a tailored reward shaping strategy was introduced, which maximized search efficiency in extensive environments. Comprehensive simulations under diverse conditions demonstrate that CARL surpasses baseline methods, achieves a 12% improvement in full recovery rate, and showcase its potential for enhancing the effectiveness of UAV swarms in active search missions.关键词：reinforcement learning;Bayesian learning;collaborative UAV swarms;active search framework441|291|0更新时间：2025-03-13
- 摘要：Driven by technologies such as the Internet of things (IoT), edge computing, and autonomous positioning and navigation, UAV swarms have been widely applied in scenarios such as emergency communication, data collection, environmental mapping, and intelligent logistics. To achieve optimal system performance under limited communication resources, a remote control framework for UAV swarms was designed. The framework considered the differences in external environments where UAV operate and integrates state estimation errors and control errors to formulate a joint optimization problem for control commands and scheduling decisions. This problem was decomposed into two subproblems: robust controller design and update scheduling, which were solved using quadratic programming and Lyapunov optimization methods, respectively. Simulation results verify the importance of prior contextual information and control errors in UAV swarm scheduling. Compared with traditional strategies that only consider state estimation errors, the new method reduces tracking errors by up to 13.74% and demonstrates better performance under various state transition noise conditions.关键词：UAV swarms control;mobile edge computing;resource allocation353|379|0更新时间：2025-03-13
- 摘要：The deep integration of UAV and Internet of things (IoT) transmits a large amount of sensitive data in the air-to-ground intelligent network, posing a serious risk of privacy leakage. The proposal of federated learning (FL) provides a privacy-preserving solution for low-altitude IoT applications, allowing multiple participants to jointly train models without sharing sensitive data. However, the federated learning performance is unstable because of various application scenarios, heterogeneous nodes and dynamic environments. An federated fearning based on proxy Raft election and weight calculation (FedREP-W) method was proposed, which combined classical Raft election and weight calculation, significantly improving the stability and efficiency of federated training. To be more specific, the use of Raft to choose new agent devices keeped federated learning stable. By incorporating the concept of weight elections, the effectiveness of federated learning could be enhenced by designating the most powerful node as an agent. The experimental results publicly available datasets show that the proposed strategy and algorithm perform well in lowering the number of communication rounds, speeding up model convergence, and making the system stable. This provides a feasible solution for efficient, secure, and stable federated learning in low-altitude IoT networks.关键词：low-altitude IoT;federated learning;device election strategy;stability;training efficiency406|253|0更新时间：2025-03-13
- 摘要：With the development of autonomous driving technology, the low-altitude Internet of things has gradually become a key focus of national industrial innovation. High-precision and highly adaptable simultaneous localization and mapping are crucial for the intelligent operation of unmanned systems. However, the limited perception capabilities of a single agent and the susceptibility of visual sensors to interference make it challenging for traditional methods to meet the demands of real-time localization and mapping in complex and dynamic environments. To enhance the accuracy and adaptability of localization and mapping, an innovative millimeter-wave radar-assisted multi-agent collaborative localization and mapping scheme was presented. This scheme deeply explored the multi-dimensional features of millimeter-wave radar in the time, space, and Doppler domains, effectively filtering and collecting reliable point cloud information to achieve local odometry and local map construction. With shared local map information, a collaborative global optimization mechanism was designed to achieve precise localization and mapping. Experimental results demonstrate that this scheme effectively ensures the robustness and accuracy of the system.关键词：localization and mapping;millimeter-wave radar;multi-agent collaboration;low-altitude Internet of things201|261|0更新时间：2025-03-13
- 摘要：The low-altitude Internet of things (IoT), based on an air-ground integrated network, combines communication and computing functions. This allows it to efficiently collect, transmit, and analyze data in low-altitude scenarios, continuously empowering the development of the low-altitude economy. In this network, aerial platforms such as unmanned aerial vehicle (UAV) uses onboard sensors to gather multimodal perception data and perform AI-based data processing to support various low-altitude applications, such as agricultural monitoring and environmental modeling. Executing multimodal data inference and content generation tasks requires large AI models. To meet these demands, UAV needs powerful computing resources and vast data support, making efficient model training and optimization essential. However, this poses significant challenges to the current low-altitude IoT network. To address this, an integrated air-ground edge-cloud collaborative framework was proposed, where UAV function as edge nodes, collecting data and performing small-scale computations. Through wireless channels, cloud servers provide large-scale computations and update models for the UAV, enabling efficient collaborations. Given limited wireless communication bandwidth, the framework faces challenges in scheduling information exchange between the UAV and the cloud servers. To solve this, joint optimizations for task allocation, transmission resource management, data quantization, and edge model updates were presented, to improve inference accuracy by maximizing the mean average precision (mAP) of the proposed framework. A closed-form lower bound for the mAP based on the performance of the edge and cloud models were derived and a solution to mAP maximization was proposed. Simulations, based on visual classification experiments, show that the mAP of proposed framework under IoLoUA consistently outperforms centralized and distributed frameworks across various bandwidth and data conditions.关键词：large model;edge intelligence;unmanned aerial vehicle776|449|0更新时间：2025-03-13
- 摘要：The integrated sensing and communication(ISAC) system based on orthogonal time frequency space(OTFS) as the transmission waveform is recognized for its higher efficiency of resources, making it one of the key technologies for addressing the shortage of spectrum resources. As the number of sensing targets increases, the difference in signal power received by the base station from the superposition of multiple sensing echo signals becomes less significant. Traditional multi-target channel sensing and target detection algorithms result in error transmission and accumulation, thereby degrading the performance of the system's channel sensing and target detection. A maximum likelihood estimator based multi-objective channel parameter sensing and target detection algorithm was proposed to improve the estimation accuracy of the sensed channel and target parameters. Specifically, the parallel interference cancellation(PIC) algorithm was adopted to the received superimposed signals. The signals were reconstructed using the results obtained from the previous iteration and were subtracted from the received signals. The signal-to-interference-plus-noise ratio of the echo signals in the estimation of the sensed channel and target parameters was improved, so the performance of the maximum likelihood estimator was improved. Simulation results show that the proposed algorithm outperforms the traditional ones in terms of channel estimation accuracy. Additionally, the convergence of the proposed algorithm is also validated to be overhead saving.关键词：integrated sensing and communication;orthogonal time frequency space;channel sensing and target detection;parallel interference cancellation479|455|0更新时间：2025-03-13
- 摘要：Regarding the presence of multiple malicious eavesdropping device (ED) in the marine Internet of things (M-IoT), to ensure the secure computation offloading of unmanned surface vehicle (USV) to a high altitude platform (HAP), a non-orthogonal multiple access (NOMA) assisted transmission policy was employed, where a set of idle USV acted as jamming USV (JU) that interfered with the eavesdropping of the ED and formed NOMA clusters with the transmitting USV (TU). Considering the requirements such as energy constraints and security transmission, the offloading ratio, transmitting power, computation resource allocation and NOMA cluster selection were jointly optimized with the objective of minimizing the maximum task processing latency. To solve the proposed mixed-integer non-convex optimization problem, an algorithm combining deep deterministic policy gradient (DDPG) and cross-entropy was proposed. Simulation results show that the proposed algorithm can effectively reduce maximum task processing latency and ensure the security of the system.关键词：M-IoT;mobile edge computing;NOMA;physical layer security178|306|0更新时间：2025-03-13
- 摘要：In the mobile edge computing scenario, the distributed architecture of federated learning allows the edge server and mobile terminals to cooperatively train the deep model, without necessitating sharing of local data across the mobile terminals. While the training process generally consists of multiple rounds between the server and several clients, which can incur high communication costs and training overhead. To address this issue, a communication-efficient model pruning for federated learning (CEMP-FL) framework, which employed the single-shot layer balance network pruning (SBNP) algorithm, combined with unstructured sparse weight compression, was proposed to significantly reduce the size of the global model, and to effectively alleviate the biased pruning due to training samples discrepancy between clients. Meanwhile, layer balance policy (LBP) was adopted to ensure a balance of the model parameters between layers, which could effectively circumvent the problem of layer-collapse in the case of high sparsity. Finally, the performance of CEMP-FL in wireless scenarios was discussed on two benchmark datasets. The experimental results show that the proposed CEMP-FL method achieves the highest compression ratio of communication costs while maintaining performance, and provides efficient communication in the distributed architecture of federated learning.关键词：federated learning;pruning algorithm;communication-efficiency;layer balance321|209|0更新时间：2025-03-13
- 摘要：An access control strategy was designed for the cognitive radio-inspired rate splitting multiple access (CR-RSMA) network with multiple secondary users. This strategy aimed to enable more secondary users to access the primary user′s frequency band while ensuring that the primary user achieved the same outage performance as orthogonal multiple access (OMA) and satisfying the quality of service (QoS) requirements for communications of secondary users. The sum throughput of secondary users were maximized on this basis. Considering the QoS constraints of secondary users, the strategy jointly optimized the secondary users′ transmit power allocation factor and the set of accessible secondary users, and derived the closed-form expression of the optimal power allocation factor. Then, the maximum communication rate and minimum communication time of secondary users were obtained. Building upon this, the greedy search algorithm was employed to determine the final set of accessed secondary users. Finally, the strategy optimized time resources to maximize the sum throughput of all admitted secondary users. Simulation results demonstrate that the proposed strategy outperforms the cognitive radio-inspired non-orthogonal multiple access (CR-NOMA) network in terms of the number of accessible users and throughput.关键词：cognitive radio;rate splitting multiple access;access control strategy;resource allocation133|241|0更新时间：2025-03-13
- 摘要：IPv6 address space is very large. IPv6 unicast address is divided into two parts: network prefix and interface identifier. The network prefix is assigned by Internet service provider (ISP), and the interface identifier can be determined by manual configuration, random generation and the EUI-64 format. The IPv6 addresses determined by manual configured and statically generation in EUI-64 format bring a risk of personal privacy leakage. However, randomly generated IPv6 addresses sometimes do not meet the network access control requirements based on IP addresses. Therefore, a ZUC-based dynamic addressing (ZBDA) algorithm was proposed. The MAC address of a network host was encrypted using the ZUC stream cipher algorithm to generate a dynamic IPv6 address, which could be decrypted at the receiving server to obtain the MAC address of host, and it can be verified the host's access permissions from decrypted MAC address. The ZBDA algorithm not only solves the problem of personal privacy leakage caused by improper IPv6 addressing, but also meets the network access requirements based on IP address control. Moreover, IPv6 address generation speed and verification speed are fast. Therefore, the algorithm has the value of practical application.关键词：IPv6;addressing algorithm;interface identifier;privacy leakage;EUI-64;ZUC111|367|0更新时间：2025-03-13
- 摘要：Aiming at the problem of insufficient feature information contained in small targets under unmanned aerial vehicle (UAV) images that led to insufficient detection accuracy of the model, a small target detection algorithm for UAV sea rescue images that integrated multi-scale and contextual information was proposed. Firstly, context enhancement module was designed for small target feature information, which effectively enhanced the ability of the model to process small targets by enhancing the contextual information of the feature layer. Secondly, to improve the robustness of the model, spatial attention module was designed to enhance the learning of important features. Finally, balance L1 loss was used to optimize the loss function of the baseline algorithm and enhance the stability of the model during the process of detection. Based on the Tiny-Person dataset, through extensive experimental comparison with the benchmark algorithm, the proposed algorithm improves the detection performance of small targets on the sea surface by 2.06% on AP50_tiny, which has a positive impact on rescue operations.关键词：sea rescue;unmanned aerial vehicle image;small object detection;attention mechanism;feature fusion376|210|0更新时间：2025-03-13
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