Integrated sensing and communication (ISAC)-enabled multi-UAV networks can jointly detect non-cooperative UAVs and serve ground user equipment (UEs) in dense urban environments. However, conventional full-coverage service causes redundant transmissions, power inefficiency, and severe inter-/intra-UAV interference, especially under limited UAV power budgets. This paper improves the coverage mode by jointly optimizing UE/target task assignment and ISAC beamforming, which leads to a non-convex mixed-integer nonlinear programming (MINLP) problem with coupled discrete-continuous variables and complex interference. To address this challenge, we propose a hierarchical closed-loop optimization framework that integrates fractional programming with stochastic gradient descent–ascent (FP-SGDA). The framework decomposes the MINLP into an outer-layer task assignment problem and an inner-layer beamforming problem, and establishes a bidirectional feedback loop between them. In the inner layer, FP-SGDA efficiently optimizes the non-convex ISAC beamforming problem and feeds back the optimized beamformers, per-UAV communication rate, sensing CRLB, SINR, and actual intra-/inter-UAV interference under the current assignment. Guided by this feedback, the outer layer updates the binary UE/target assignment matrices and reallocates high-interference or high-difficulty tasks among UAVs, thereby reducing coverage overlap and mitigating co-channel ISAC interference at the source. Numerical results demonstrate that the proposed framework outperforms the SCA and MI-WMMSE baselines, delivering a higher communication sum rate, a lower sensing Cramér–Rao lower bound (CRLB), and enhanced power efficiency in dense urban multi-UAV ISAC networks.

Integrated Sensing and Communication (ISAC)-enabled Roadside Units (RSUs) encounter significant performance trade-offs between target sensing and multi-user communication in complex urban environments, where conventional optimization methods are prone to converging to local optima and joint optimization methods often yield sub-optimal results due to conflicting objectives. To address the challenge of trade-off between sensing and communication performance, this paper proposes a hierarchical beamforming optimization solution designed to tackle joint sensing–communication problems in such scenarios. The overall optimization problem is decomposed into a two-level “leader-follower” structure. In the leader layer, we introduce a max–min strategy based on the bisection method to transform the non-convex Signal-to-Interference-plus-Noise Ratio (SINR) optimization problem into a second-order cone constraint problem and solve the communication beamforming vector. In the follower layer, the Signal-to-Clutter-plus-Noise Ratio (SCNR) maximization problem is converted into a Semi-Definite Programming (SDP) problem solved via the CVX toolbox. Additionally, we introduce a “spatiotemporal resource isolation” mechanism to project the sensing beam onto the null space of the communication channel. The hierarchical optimization solution jointly optimizes communication SINR and sensing SCNR, enabling an effective balance between sensing accuracy and communication reliability. Simulation results demonstrate the proposed method’s effectiveness in simultaneously improving sensing accuracy and communication reliability.

In this paper, we utilize multi-hop UAV relay networks to provide data relay forwarding services for ground heterogeneous users who are hard to build direct communication links. This paper studies the resource allocation and deployment optimization problem of multi-hop UAV relay network in order to satisfy the various needs of heterogeneous users and lower the outage probability of multi-hop transmission link. First, based on the characteristics of air-to-ground channels, we establish an information transmission model between UAV and ground users. Given heterogeneous users with various data volume requirements, we propose a method for allocating time and power resources so that UAV can serve as many heterogeneous users as it can while still satisfying user service requirements needs. Second, the calculation for the outage probability in a multi-hop relay network is constructed based on the properties of the air-to-air channel, and the optimal placement of the multi-hop relay network with the lowest outage probability is solved. Finally, an experimental comparative analysis was used to confirm the effectiveness of the proposed method when applied to the multi-hop relay model serving heterogeneous users.

This paper investigates the feasibility of the integrated sensing and communication (ISAC) technology in the future airborne network (AN), a large heterogeneous network of various aircraft to realize situation awareness and sharing. In order to achieve better transmissions with few overheads, an effective beam rendezvous scheme is fundamental. However, due to the nodes’ oblivious movements in AN, the conventional beam training methods may lead to significant overheads and latencies. Motivated by this, we develop a novel fast beam prediction scheme for communication in AN with sensing assistance to improve the sum-rates while satisfying the sensing requirements simultaneously. This paper introduces our vision of the ISAC-enabled AN and provides the signal models in the considered AN based on ISAC technology. Then, we propose a 3D beam prediction scheme that helps both sides to formulate the aligned beams utilizing the angular prediction results, without the heavy beam training processes to reduce the overheads. Numerical results demonstrate that the proposed scheme can achieve high-data-rate transmission while guaranteeing the sensing performance with lower complexity and link overheads.

IntroductionSlow transit constipation (STC) is a type of functional constipation. The detailed mechanism of STC, for which there is currently no effective treatment, is unknown as of yet. Tongbian decoction (TBD), a traditional Chinese medicinal formula, is commonly used to treat STC in clinical settings. However, the potential impact of TBD on the management of STC via modulation of the gut microbiota remains unclear.MethodsPseudo-germ-free rats were constructed after 6 days of treatment with bacitracin, neomycin, and streptomycin (abbreviated as ABX forthwith). Based on the successful construction of pseudo-germ-free rats, the STC model (ABX + STC) was induced using loperamide hydrochloride. After successful modeling, based on the different sources of donor rat microbiota, the ABX + STC rats were randomly divided into three groups: Control → ABX + STC, STC → ABX + STC, and STC + TBD → ABX + STC for fecal microbiota transplant (FMT). Body weight, fecal water content, and charcoal power propelling rate of the rats were recorded. Intestinal microbiota was detected by 16S rRNA sequencing, and the 5-hydroxytryptamine (5-HT) signaling pathway was examined by western blots, immunofluorescence, and immunohistochemical analysis.ResultsAfter treatment with fecal bacterial solutions derived from rats treated with Tongbian decoction (TBD), there was an increase in body weight, fecal water content, and the rate of charcoal propulsion in the rats. Additionally, activation of the 5-hydroxytryptamine (5-HT) signaling pathway was observed. The 16S rRNA sequencing results showed that the fecal bacterial solution from TBD-treated rats affected the intestinal microbiota of STC rats by increasing the proliferation of beneficial bacteria and suppressing the expansion of harmful bacteria.ConclusionOur study showed that TBD alleviated constipation in STC rats by modulating the structure of the intestinal microbiota.

This paper investigates the directional transmission scheme for airborne networks (ANs) with orthogonal time frequency space (OTFS) modulation, to cope with the degradations of communication performance due to the aircraft’s uncertain and high-speed mobilities. Besides showing better communication performance in high mobility scenarios such as AN, OTFS has also been verified for realizing the integrated sensing and communication (ISAC) systems. In this case, this paper proposes a sensing-assisted beam prediction method by exploiting echoes to predict the next locations of moving aircraft, solving the beam rendezvous problem at the transmitter. Besides, for the data detection problem at the receiver, this paper proposes a novel pilot placement scheme relying on the predicted delays and Dopplers, realizing accurate channel estimation with lower overheads. Simulation results show that the proposed OTFS-based directional transmission scheme can achieve reliable communication performance with a low bit error rate.

Neighbor discovery problem is of essential importance in airborne network (AN) to support the capabilities of real-time situation awareness and sharing among aircraft. With the exploding demand for high-data-rate transmission and communication security, it is a predictable trend that adopt the multi-antenna in AN. However, due to the uncertainty of AN (e.g., oblivious relative positions, asynchronous clock, heterogeneous multi-antenna configurations, and anonymous information, the neighbor discovery algorithm must be carefully designed, with the aim at achieving faster beam rendezvous within a bounded latency. In this paper, the oblivious multi-antenna neighbor discovery problem is investigated, with the multi-antenna neighbor discovery algorithms (MAND) proposed based on the Chinese Remainder Theorem (CRT). Through the theoretical analysis, we derive the conditions for successful neighbor discovery within a bounded latency, as well as the worst case discovery latency bound with multi-antenna. The simulation results show that our algorithms can approximate the better average performance of the randomized mechanism in oblivious scenarios, besides achieving complete neighbor discovery within a bounded latency.

Multi-UAV relay network can provide information relay and forwarding services for ground users who cannot communicate directly with the help of building reliable connectivity links. Software Defined Networking (SDN), a new network architecture, provides a promising solution for addressing problems such as load balancing, topology changes, and information sharing in multi-UAV relay network. Aiming at the load balancing problem of multi-UAV relay based on SDN, a dynamic migration algorithm based on load balancing and delay (DMA-LBD) is proposed by using the idea of dynamic migration. This ensures that users within the coverage area of UAVs with larger loads are migrated to other UAVs, thereby establishing new connection relationships that balance the load between all UAVs within the relay network. Experimental results demonstrated that, in comparison to other methods, the proposed algorithm makes a suitable compromise between the relay load balancing and the average delay of relay link. It can avoid the single optimization situation of an imbalanced UAV relay load or an excessive average delay, providing a more effective solution to the problem of relay load balancing of multi-UAV relay network.

Due of their flexibility and on-demand deployment capabilities, UAVs are frequently utilized in wireless communications applications. UAV can be utilized as an airborne communication relay to connect geographically separated ground users and ensure dependable connectivity coverage. In this paper, multiple UAVs are deployed for reliable relay coverage for numerous users who are unable to communicate directly. By continuously optimizing the deployment location of UAVs with load balancing and energy consumption as performance metrics, the relay coverage algorithm we propose ensures the users’ dependable connectivity. The algorithm then determines the projection point of UAVs on the ground and the corresponding cell division. The results of the experiments demonstrate that the proposed method can improve the balance of the UAV relay load and lower the energy requirements for relay communication.

针对集成僵化的传统航空网络难以在节点故障后高效调度网络资源从而恢复执行任务的问题，提出无线网络虚拟化环境下节点可靠感知的差异保护虚拟航空网络映射算法。首先，节点映射采用新的节点重要度评价方法，综合感知故障可能、无线干扰和网络资源，为虚拟节点映射可靠物理节点；其次，链路映射根据节点重要度，采用P圈保护技术对映射路径节点实行差异保护。仿真结果表明，相比于传统的节点保护映射算法，所提算法在保持较低恢复时延的同时，提高了映射成功率。

Signal de-noising process is the process of signal characteristic components extraction. In view of the singular value decomposition de-noising method is difficult to separate under low SNR, through observing the singular value distribution under different decomposition order, select the appropriate number of decomposition, and combining the wavelet packet de-noising ability and good frequency resolution, put forward a kind of effective signal de-noising method. Firstly, based on different SNR to choose the appropriate number of singular value decomposition, select bigger singular value reconstructing the signal, preliminary to remove noise, and then make three layer wavelet packet decomposition, compare the energy distribution of all nodes, and make selection of energy concentration of several nodes, reconstruct the signal. Simulation results verify the validity of this method.

The Unmanned Aerial Vehicle (UAV) swarm collaborative operations is one of the disruptive technologies in future information warfare. Considering the characteristics of UAV swarm collaborative operation, we propose two UAV swarm cooperative situation awareness (SA) theory models based on the classical ones and the composition of UAV swarm. Firstly, the improved Endsley three-level SA model is proposed, in which the traditional mental model is changed into human machine intelligent model on the individual level. Then the homogeneous swarm and heterogeneous swarm cooperative SA model are separately built based on the improved team SA model and Distributed SA (DSA) model. Meanwhile, a comparison between the multi-UAV and UAV swarm is carried. Finally, a scenario analysis of swarm cooperative SA in combat situation is presented.

In collaborative combat operations, the challenges posed by communication adversities in aviation data links have become increasingly conspicuous. Ensuring the reliable connectivity of data links has thus emerged as a critical issue, particularly in light of growing interference effects on communication link outage. To address this challenge, this paper proposes a cooperative non-orthogonal multiple access (NOMA) technology that aims to enhance interference tolerance and strengthen stable communication capabilities in data links. To achieve these goals, an anti-interference collaborative NOMA system model applicable to aviation data links is established, and the anti-interference performance of collaborative NOMA technology is analyzed under Rice fading channel conditions. The analysis centers on the system's outage probability, for which a closed-form expression is derived. Numerical analysis methods are employed to calculate and solve the expression, while simulation experiments are conducted to analyze the effect of factors such as transmission power, cooperative forwarding power, interference power, communication distance, and allocation coefficients on the system's outage probability. The results show that the aviation data link system using a collaborative NOMA scheme has better anti-interference performance than a non-collaborative scheme, and adjusting the above parameters can effectively improve the system's stable communication performance.

By relaying messages between users, unmanned aerial vehicles (UAVs), which are frequently utilized in the communication industry, can be used as airborne communication relays to resolve connectivity problems. In order to effectively connect a large number of heterogeneous users dispersed on the ground, we deploy a large number of UAVs for reliable relay coverage and message transmission between users. We initially put forth a relay coverage algorithm that continuously optimizes the position of UAVs and the cell division of ground users in order to provide users with full coverage. Second, we study several relay message forwarding methods between UAVs while taking into account the features of heterogeneous users. A relay selection method, which takes the relay link throughput as the optimization goal, is proposed to ensure full connectivity between UAVs. Finally, we contrast and analyze how various relay forwarding methods affect the effectiveness of the relay network. The experimental results demonstrate that the proposed relay coverage algorithm can balance the UAV relay load better and reduce transmission delay, while the suggested relay selection strategy can minimize the number of isolated UAVs and maximize the throughput of UAV relay links.

Airborne tactical networks (ATNs) built on network virtualization (NV) can enable efficient information sharing for network-centric warfare by breaking the tight coupling between applications and network infrastructure and thus solving the network ossification problem. With dynamic changes during military missions, the application of virtualization is challenged by the changing demands on network resources when instantiating multiple virtual networks (VNs) on a shared substrate network (SN), known as virtual network embedding (VNE). However, existing dynamic VNE algorithms, mostly designed for wired networks, focus on the dynamic changes of single VN and do not consider the dynamic changes of different VNs. The proper processing of changes of different VNs is beneficial to improve the embedding profits of VNs. On these backgrounds, we investigate the dynamic wireless VNE that processes the dynamic changes of different VNs in the wireless environment of ATNs. A relationship matrix-based dynamic wireless VNE (DWVNE-RM) algorithm is proposed in order to maximize the acceptance ratio of VN requests. The dynamic changes of different VNs are classified into negative changes (requiring resource) and positive changes (releasing resource). Then, the relationship matrix between negative and positive changes is constructed to properly process the resource relationship among different changes in order to improve the acceptance ratio. In addition, the complex interference of ATNs is considered in the mapping or remapping of virtual nodes and links to improve the reliability. Through extensive simulation, DWVNE-RM is compared with multiple existing dynamic algorithms and outperforms over others.

航空集群机载网络作为集群成员间信息交互的纽带,其路由策略性能优劣直接影响信息传输实时性与可靠性,从而制约网络化集群作战效能发挥.考虑到航空集群机载网络具有诸多不确定性,为应对路由失效以及尽可能避免路由更新,从路由选择算法的角度,在软件定义网络架构下提出Failure-Oblivious路由策略.与传统路由策略不同的是,该策略利用随机算法生成多条路由,能够在不进行路由失效预测的情况下应对路由随机失效问题.理论推导与仿真验证表明,该策略能够在保证通信时效性代价可控的基础上,降低航空集群机载网络路由失效风险.

In threat assessment method, the relationships among indicators considering insufficiency and weights set unreasonable, the threat assessment method via network model put forward, used for coordinate target recognition system based on secondary radar. Firstly, the indicators were given by hierarchical structure, thinking of the relationships among indexes, then giving the indicators by network model. Then making standardization of indicators, solving the indicators weights by Analytic Network Process (ANP). Getting the systems' threat degree through threat degree assessment model, then made sequencing. Finally an example is used to verify the method, shows it is reasonable and effective. And the result is helping for allocation of interfere resources and operational deployment in later.

针对网络僵化的问题,目前多采用网络虚拟化(NV)方法进行解决,其关键技术是虚拟网络映射(VNE).为解决无线VNE过程中功率和带宽资源使用不均衡的问题,基于负载均衡原理提出一种联合资源分级的无线VNE算法.首先,采用新的节点资源排序方式,其中将节点功率和平均链路带宽作为排序依据;其次,对资源进行分级,以动态调整虚拟网络请求对功率和带宽资源的需求;最后,改进功率和带宽资源的单位成本,并以最小化成本为目标函数选择资源分配方案.与原有的无线VNE算法WVNE-JBP相比,所提算法的总体接受率提高了11.7个百分点,平均功率利用率提高了4.4个百分点,平均带宽利用率提高了1.6个百分点.实验结果表明,所提算法能有效提高虚拟网络接受率和资源利用率.

The flexible and easy-to-deploy Unmanned Aerial Vehicle(UAV) is used to control the manned-aircraft formations with highly dynamic topology changes, forming a manned and unmanned cooperative airborne network, which can effectively reduce network deployment costs while ensuring network connectivity. Aiming at the problem of UAV deployment, in order to optimize network reliability and deployment cost overhead indicators, a UAV deployment algorithm based on connectivity and cost minimization is proposed. Firstly, in order to achieve full coverage of the mission area to ensure connectivity, preliminary deployment is performed based on the communication range of the UAV. Then, based on deployment constraints and optimization indicators, the redundant UAVs in the preliminary deployment are determined and deleted. The experimental results show the effectiveness of the proposed algorithm.