Yu Guo, Shenghao Ye, Shuangwu Chen, Zijian Wen, Tao Zhang, Bai Qirui, Dong Jin, Yunpeng Hou, Huasen He, Jianyang, Xiaobin Tan. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Zijian Wen, Tao Zhang, Shuangwu Chen, Shenghao Ye, Yu Guo, Qirui Chen, Jingxian Shuai, Yunpeng Hou, Huasen He, Jianyang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Motivated by the intelligent decision-making ability, Deep Reinforcement Learning (DRL) has been extensively applied in multi-Unmanned Aerial Vehicle (UAV) trajectory planning. This article investigates the application of DRL in Three-Dimensional (3D) trajectory planning in environments with obstacles, where multiple UAVs act as aerial Base Stations (BSs) to provide services to ground user hotspots. The existing DRL-based algorithms require a significant amount of trial and error iterations to obtain sufficiently high-performing agents. To cope with this drawback, we introduce Transfer Learning (TL) into DRL, enabling the UAV agent to possess prior knowledge upon initialization, thereby quickly adapting to unfamiliar environments and significantly improving performance. Considering the limited local observations of UAVs, a multi-modal fusion autoencoder is proposed for extracting and compressing cross-modal features from global observations to obtain the global fusion state, which enhances the perception capabilities of UAVs without incurring excessive communication overhead. Finally, we propose an exploration novelty-driven collaborative trajectory planning algorithm for multiple UAVs, which ensures obstacle avoidance and enhances the UAVs’ exploration capabilities to cover all hotspots. We adopt a probabilistic channel model and discretize both the time and DRL action space to achieve a balance between practicality and tractability. Extensive experiments demonstrate that our proposed transfer reinforcement learning method can improve the initial performance of the UAV agents by 76%. The perception and exploration-enhanced trajectory planning algorithm significantly increased the exploration efficiency and improved hotspot coverage by 50%.

Trajectory planning for unmanned aerial vehicle (UAV) formations has garnered significant research attention due to its potential to enhance UAV-assisted communications. While deep reinforcement learning (DRL) has been widely adopted for UAV trajectory planning owing to its strong learning and decision-making capabilities, existing DRL-based algorithms suffer from slow convergence and high training cost. To address these problems, this paper proposes a novel hierarchical control framework for UAV formation trajectory planning, where a leader UAV determines the global trajectory while follower UAVs dynamically adjust their relative trajectories. We further design a multi-imperfect expert guided DRL algorithm to substantially improve the learning efficiency of the LUAV agent, enabling rapid adaptation to unfamiliar environments. Additionally, an artificial potential field (APF) based coordination mechanism is integrated to ensure safe navigation and maintain formation for follower UAVs. Experimental results demonstrate that the pro-posed algorithm achieves 100% hotspot coverage while reducing convergence time by 82% compared to state-of-the-art methods.

Most existing routing algorithms for Low-Earth-Orbit (LEO) satellite networks neglect the spatio-temporal features inherent in the network states, leading to suboptimal performance in scenarios with dynamic network topologies. In this paper, we propose a Spatio-Temporal Graph Attention Network (STGAN) architecture for the extraction of spatio-temporal features from satellite network states. Building upon this, we propose a State Prediction based Dynamic Routing Algorithm (SP-DRA), which combines STGAN with the enhanced Shortest Path First algorithm (eSPF). SP-DRA utilizes STGAN to predict future network states by exploiting the spatio-temporal correlations of historic observations. Based on state predictions, each link is assigned with a delay-based prediction weight, which is used as the input for eSPF. The proposed eSPF dynamically selects the path with the smallest prediction weight to facilitate congestion avoidance and reduce transmission delay. Simulation results show that our STGAN architecture provides up to 18.6% prediction accuracy improvement than the existing deep learning-based methods, namely STGCN and ST-MGAT. Meanwhile, the SP-DRA algorithm outperforms existing routing strategies including SPF, Explicit load balancing algorithm (ELB), and Satellite networks Link State Routing algorithm (SLSR) in terms of packet loss rate and average end-to-end delay. Specifically, the performance gains increase with network traffic.

Satellite networks have been proposed as a vital component in 6G networks for providing global connections. In recent years, satellite network traffic has been increasing. However, the limited onboard resources and inter-satellite link bandwidth make network congestion a critical issue. In order to avoid network congestion and improve quality of service, satellite traffic prediction has received more attention. However, the traditional forecasting models do not fully consider the dynamic topology of satellite networks and the spatial-temporal features of traffic. Thus, we propose a novel traffic prediction method for dynamic satellite networks. Considering that there is traffic correlation between nodes without direct connection, our model introduces a graph generation module to generate adjacency matrices based on dynamic attributes. Moreover, to improve the accuracy of prediction, we further propose the spatial attention module and time sequence processing module to exploit the temporal and spatial correlations of satellite traffic respectively. The experimental results show that our method outperforms the compared algorithms and increases up to 6.84 % prediction accuracy.

Fault detection (FD) is essential for wafer quality control in semiconductor manufacturing (SM), as it can identify abnormal wafers in the early stages. However, frequent chamber discrepancies leads to distribution shifts in the data from different chambers, which causes performance degradation of the existing model. In this paper, we conceive a robust cross-chamber fault detection method in SM, which to the best of our knowledge is the first work that employs domain generalization to address the issue of cross-chamber fault detection in SM. Our basic idea is to map the samples from the source chambers and target chambers to the same hypersphere space, making normal samples cluster around a specific feature center while faulty samples stay away from it. Due to the scarcity of faulty samples, we propose a one-class classification-based fault detection method relying on normal samples to establish classification boundaries. To generalize the model to unseen target chambers, we design a meta-learning-based one-class domain generalization approach. We also devise a strategy to enhance distribution alignment within hypersphere, making the classification boundaries of various chambers be close to each other. The evaluations on real-world data collected from a wet process equipment in SM verify the high robustness of the model to chamber discrepancies with limited faulty samples.

The integrated merits of Unmanned Aerial Vehicle (UAV) networks including high mobility, ease of deployment and low cost have promoted their widely application in both civilian and military areas. However, the complex communication environments, dynamic network topology and intermittent links pose significant challenges to the transmission reliability of UAV networks. Existing end-to-end reliable transmission mechanisms rely on feedbacks from receivers to trigger retransmission, which impose extra transmission delay and redundant retransmission. In this work, we propose a Hop-by-Hop Redundancy-guaranteed Adaptive Coding (HHRAC) approach for enhancing transmission reliability of dynamic UAV Networks. To cope with lossy links, a link quality-adaptive coding algorithm is proposed, which dynamically adjusts coding redundancy rate according to link quality. Meanwhile, the Cauchy matrix is employed to design efficient coding matrices, which greatly improve decoding efficiency and enable the intermediate nodes to perform low-complexity verification. Moreover, a redundancy-guaranteed hop-by-hop transmission mechanism is provided to avoid End-to-End (E2E) retransmission and ensure the destination node has a high probability to receive sufficient packets. To avoid receive queue overflow, we further propose a queue length prediction based congestion control algorithm to control the sending rates of UAVs. The experimental results show that HHRAC achieves significant performance gains compared to existing algorithms in terms of transmission delay and retransmission times.

Space-Ground Integrated Networks (SGINs) hold the potential to enable seamless and high-quality global coverage in an economically viable manner. However, the limited bandwidth and relatively long delay of satellite-ground links pose significant challenges in meeting the increasing demands driven by surging traffic. To address this issue, we propose a three-tier cooperative caching architecture incorporating base stations (BSs), satellites, and a content server, which aims to reduce the average content retrieval delay through cooperative caching between BSs and satellites. The joint optimization problem among them is both challenging to solve and non-scalable due to its exponentially increasing computational complexity. Meanwhile, the geographic characteristics of requests are overlooked in existing work. As a consequence, we propose a novel block partitioning algorithm based on the popularity similarity across areas to facilitate cooperative caching, which reduces the computational complexity, and ensures the scalability and the effectiveness of cooperative caching. Based on the partitioned blocks, the optimization problem is decomposed into two subproblems: intra-block cooperative optimization among BSs and inter-block cooperative optimization among satellites, effectively catering to the characteristics of wide-area coverage in SGINs. For the subproblems with finite dimensions, Semidefinite Relaxation (SDR) based intra-block and inter-block cooperative caching approaches are proposed to obtain the optimal cooperative caching strategies. Extensive simulations demonstrate that, compared to directly solving the original problem, the proposed algorithm reduces the solving time from exponential to linear growth. Moreover, our algorithm outperforms existing schemes by reducing 13% average retrieval delay and improving 12% overall cache hit rate.

Low Earth Orbit Satellite Networks (LEOSNs) have emerged as a promising paradigm for space information networks, where multiple inter-satellite links facilitate the rapid transmission of on-orbit data to multi-ground station systems. When the destination of the transmission is not a certain ground station, but anyone of the ground stations, it can be modeled as an anycast problem. However, the time-varying topology, dynamic inter-satellite link status and limited onboard resources bring challenges to the routing of on-orbit data. Existing unicast routing solutions failed to address the anycast routing problem as they could not fully utilize multiple ground stations. Inspired by the Potential Field (PF) theory in physics, we make the first attempt to adopt the PF approach in the on-orbit data anycast routing problem. We design a synthesized PF model including several sub-fields corresponding to network states such as length of path, node transmission load and link bandwidth. By implementing inter-satellite propagation and synthesis of PF, dynamic perception and unified measurement of network states can be achieved. Based on our PF model, we propose a distributed PF-based and Network State-aware Anycast Routing (PFNSAR) algorithm, which regards the ground stations as multiple sources of attractive potential and guides packets along the gradient of synthesized PF. Meanwhile, the occurrence of the well-known local minimum is novelly handled by setting PF configuration subject to a parameter constraint and switching routing modes when routing packets. Extensive simulations demonstrate that PFNSAR provides unified assessment of multi-dimensional network states, reduces up to 30% of average delivery delay, and improves the performance including packet delivery rate and load balancing compared with existing works.

Robust Privacy-Preserving Federated Learning (PPFL) is a secure distributed machine learning paradigm designed for untrusted environments, aiming to aggregate gradients while ensuring the reliability of the results without disclosing user gradients. However, existing single-server robust PPFL schemes require users to generate commitments in each aggregation round to ensure the correctness of the aggregation results, which leads to high computational overhead. We propose an efficient single-server robust PPFL scheme named Privacy-Preserving Robust Federated Learning (PRFL). PRFL achieves efficient gradient aggregation through a “detection-identification-exclusion” strategy. PRFL only performs quick detection without requiring high-complexity commitments in most of aggregation round, thereby ensuring excellent efficiency. PRFL comprises three pivotal components: Privacy-Preserving Gradient Aggregation Based on Packed Secret Sharing (PGAPS), Swift Share Verification based on Dual Codes (SSVDC), and Commitment-based Malicious User Identification (CMUI). PGAPS is utilized to implement the FLTrust rule without disclosing gradients. SSVDC swiftly detects incorrect shares without using commitments. CMUI identifies malicious users when SSVDC detects incorrect shares. Experimental results demonstrate the robustness and efficiency of PRFL. In a PPFL system with 100 users, PRFL can robustly aggregate gradients of a million dimensions within 37 seconds of average computational time.

Distributed training of deep neural networks (DNNs) suffers from efficiency declines in dynamic heterogeneous environments, due to the resource wastage brought by the straggler problem in data parallelism (DP) and pipeline bubbles in model parallelism (MP). Additionally, the limited resource availability requires a trade-off between training performance and long-term costs, particularly in online settings. To address these challenges, this article presents a novel online approach to maximize long-term training efficiency in heterogeneous environments through uneven data assignment and communication-aware model partitioning. A group-based hierarchical architecture combining DP and MP is developed to balance discrepant computation and communication capabilities, and offer a flexible parallel mechanism. In order to jointly optimize the performance and long-term cost of the online DL training process, we formulate this problem as a stochastic optimization with time-averaged constraints. By utilizing Lyapunov’s stochastic network optimization theory, we decompose it into several instantaneous sub-optimizations, and devise an effective online solution to address them based on tentative searching and linear solving. We have implemented a prototype system and evaluated the effectiveness of our solution based on realistic experiments, reducing batch training time by up to 68.59% over state-of-the-art methods.

Microservice architecture offers great scalability and flexibility to the development of online services systems. Performance anomalies, which happen frequently due to code bugs or runtime environment misconfiguration, can severely damage the system availability and cause great losses. However, it is challenging to detect performance anomalies and locate their root causes considering the large volume of monitoring data (e.g., metrics and traces) and the complex dynamic interdependence between heterogeneous services. Against these challenges, we propose HG-PAD, an automatic performance anomaly diagnosis (PAD) framework for microservice systems. We build the multi-relation heterogeneous graph to model the intricate dependency between services. We further design a structure learning mechanism combining graph neural network (GNN) and node embedding learning to capture the dynamic and latent dependencies. Based on the optimized dependency graph, we devise a Conditional Variational Auto-Encoder (CVAE) based unsupervised anomaly detection method and a graph attention network (GAT) based root cause localization method for accurate anomaly diagnosis. We use datasets of different scales based on real world applications to verify the effectiveness of HG-PAD, and the experimental results show that HG-PAD achieves better diagnostic performance compared with existing baseline methods.

Encrypted tunnels are increasingly applied to privacy protection, however, a passive eavesdropper can still infer which website a user is visiting via website fingerprinting (WF). State-of-the-art WF suffers from several critical challenges in a realistic multi-tab web browsing scenario, where the number of concurrent tabs is dynamic and uncertain, training a separate model for each website is too overweight to deploy, and the robustness against the packet loss, duplication and disorder caused by dynamic network conditions is rarely considered. To address these challenges, we propose a robust and lightweight multi-tab WF method over the encrypted tunnel, named RobustWF. Due to the causality relationship between user’s request and website’s response, RobustWF employs causality correlation to associate the interactive packets belonging to the same website together, which form a causality chain. Then, RobustWF utilizes context learning to capture the dependencies between the causality chains. The missing of some specific details does not have a significant impact on the overall structure of target web, thus enhancing the robustness of RobustWF. To make the model lightweight enough, RobustWF trains an integrated model to adapt to the dynamic number of concurrent tabs. The experimental results demonstrate that the accuracy of RobustWF improves 14% in dynamic multi-tab WF scenario compared to the State-of-the-art method.

The deployment of UAVs as aerial base stations (BSs) has been considered as a promising supplement to the ground networks, which can quickly build an emergency communication network in a disaster area or significantly relief the communication burden imposed by hot-spots. However, the application of UAVs as aerial BSs is constrained by the limited onboard energy and communication coverage of UAVs. In particular, for a large target area, multiple UAVs should be deployed to meet the communication requirements. Therefore, designing the optimal trajectories of multiple UAVs is crucial to boost the UAV network performance. Inspired by the promising future of UAV BSs, this paper aims at proposing a distributed 3-dimensional (3D) trajectory design algorithm for multiple UAVs to optimize the system performance. We formulate the trajectory design problem as a multi-objective optimization problem to improve the user equipment (UE) access rate, ensure fair access opportunities, increase transmitted data volume and reduce energy consumption. Further inspired by the decision-making ability of deep reinforcement learning (DRL) in complex environments, we propose a DRL based trajectory design algorithm for multiple UAVs, namely DMTD, in which UAVs can explore both the optimal flight altitude and the potential UE distribution area in the iterative interactions with the environment, and then select the optimal flight trajectories to boost the network performance from multiple aspects. Extensive experimental results under different UE distributions have demonstrated that the proposed DMTD algorithm can find the optimal altitude to provide maximum coverage. Moreover, DMTD beats existing algorithms by providing high UE access rate, ensuring fair network service and increasing total transmitted data volume at the cost of a relatively low energy consumption. Especially in the scenes with dense and randomly distributed UEs, DMTD provides a UE access rate close to 0.9 and transmits 6 times of data volume than existing algorithms.

Low Earth Orbit Satellite Networks (LEO-SNs) have emerged as a promising paradigm for future space information networks. However, the time-varying topology, link intermittency, limited onboard resource and relatively long transmission distance imposed unprecedented challenges on guaranteeing the delay Quality of Service (QoS). In contrast with existing routing-based or resource optimization-based solutions, onboard processing provides an alternative way to reduce the transmission delay by dwindling the transmitted data size. The employment of onboard processing makes it critical to select a routing path with sufficient energy and properly allocate resources for transmission and processing. This paper studies the untouched onboard processing aided transmission delay minimization problem of LEO-SNs. A Distributed Network State Learning (DNSL) mechanism is proposed for synchronizing the network states, which induces Potential Field (PF) to model both the attraction of resources and the repulsion of transmission load. By jointly considering the channel conditions, onboard resources and transmission load, a Deep Q-network (DQN) based Intelligent In-orbit Routing (DIIR) algorithm is proposed for selecting a routing path with good channel conditions, sufficient energy and low transmission load to facilitate onboard processing. Moreover, a Deep Deterministic Policy Gradient (DDPG) based Intelligent Resource Allocation (DIRA) algorithm is provided to achieve intelligent and continuous resource allocation for exploiting onboard processing to minimize the transmission delay, while the resource and load states of satellites on the routing path are taken into consideration by including PF as an input. Extensive simulation results demonstrate that employing onboard processing with the proposed DIIR and DIRA algorithms significantly reduces the average transmission delay and packet loss rate.

Anomaly diagnosis relying on system logs to record runtime events is essential for improving the service quality of distributed systems and reducing economic losses. However, most existing log-based anomaly detection approaches depend on the assumption of the fixed quantitative or sequential patterns of a normal log event sequence. This assumption is challenged in the context of practical distributed and parallel systems due to the dynamic pattern of the log sequence, log data noise as well as concurrency of multiple anomalies. Against these challenges, this paper aims to perform the robust log-based anomaly diagnosis by capturing the event context information of the log event graph, called LogGraph, instead of straightforwardly employing the fixed quantitative or sequential patterns of the log records, thus reducing its sensitivity to the log flaws and the concurrency of multiple anomalies. Specifically, in order to handle multiple anomalies concurrency, LogGraph invokes the association rule to decouple log sequences. It further reinterprets a log record sequence into a log event graph modeled by event semantic embedding and event adjacency matrix. An attention-based Gated Graph Neural Network (GGNN) model is developed to capture the semantic information of the log graph, which enables the fine-grained and robust anomaly identification of the proposed scheme. We use real log data sets collected from Hadoop systems and network switches to verify the effectiveness of the proposed LogGraph in log data scenarios that contain noise and multiple anomalies concurrency problems. The experimental results show that the proposed LogGraph achieves high performance and strong robustness in anomaly diagnosis.

Due to the concentrated distribution of network traffic, the Internet is highly vulnerable to link flooding attack in Distributed Denial-of-Service attacks (DDoS-LFA), which utilizes the legitimate low-rate attack traffic to block the selected network area. In recent years, building trusted networks has been considered as a promising strategy to address the security issues. Nevertheless, deploying a trusted link defense mechanism in the attacked network area faces many challenges imposed by the smart scheme and legitimate disguise of DDoS-LFA. In order to overcome these challenges, we propose a blockchain-based DDoS-LFA detection and mitigation scheme, named CREDIT, to guarantee the security of attacked area, while existing works only use blockchain to share the detection results of traditional solutions. CREDIT uses blockchain to record and share the information of links and flows in real time, which enables routers in the protected area to easily trace the paths of all active flows and capture the fragile links. On the basis of link features, a credible deep learning method performed on randomly selected nodes is proposed to detect DDoS-LFA against data spoofing. When an attack alarm is raised, CREDIT performs similarity analysis to locate attackers and migrate suspicious traffic based on the flow features of alarm links. Experimental results based on real implementation and attack testbed show that, by integrating blockchain, CREDIT performs better than traditional non-blockchain-based DDoS-LFA defense methods when faced with data tampering.

As a new computing paradigm, multi-data center computing enables service providers to deploy their applications close to the users. However, due to the spatio-temporal changes in workloads, it is challenging to coordinate multiple distributed data centers to provide high-quality services while reducing service operation costs. To address this challenge, this article studies the joint optimization problem of task scheduling and resource scaling in multi-data center systems. Since the task scheduling and the resource scaling are usually performed in different timescales, we decompose the joint optimization problem into two sub-problems and propose a two-timescale optimization framework. The short-timescale task scheduling can promptly relieve the bursty arrivals of computing tasks, and the long-timescale resource scaling can adapt well to the long-term changes in workloads. To address the distributed optimization problem, we propose a two-timescale multi-agent deep reinforcement learning algorithm. In order to characterize the graph-structured states of connected data centers, we develop a directed graph convolutional network based global state representation model. The evaluation indicates that the proposed algorithm is able to reduce both the task makespan and the task timeout while maintaining a reasonable cost.

Deploying microservices on the cooperative edge computing system greatly shortens the interaction delay between users and service and alleviates the traffic burden on the backbones, which has emerged as a new paradigm for service provision. However, it is challenging to embed microservices, having diverse resource demands and heterogeneous invocation relationships, into a distributed edge computing system with irregular network topology. In order to characterize the invocation relationship, we conceive a graph attention network based model to capture the structural features of microservices. Similarly, we propose a multi-channel directed graph convolutional network model to capture the spatial dynamic of edge resources distribution, which jointly considers the heterogeneity of the edge nodes and the links between them. Then, we develop a sequence-to-sequence based multi-step decision model, which maps the feature sequence of the current state to a sequence of deployment actions. Using this model, we further propose a microservice deployment algorithm based on graph neural network aided deep reinforcement learning, where a parallel asynchronous training process is used to accelerate convergence. The performance evaluation shows that the proposed algorithm can improve the deployment success ratio and resource utilization, while ensuring the load balance of edge nodes.

The IPv6 over low-power wireless personal area network (6LoWPAN) protocol stack is a promising solution to connect wireless sensor networks (WSNs) with the Internet for realizing a ubiquitous network interconnection of all things. However, 6LoWPAN networks face a critical challenge to control congestion caused by the burst of data traffic from wireless sensors. Packet loss will occur when the buffer overflows. This article focuses on the loss-tolerant congestion control problem in 6LoWPAN networks, which has not been addressed in existing works. We formulate the congestion control problem as a noncooperative Markov game framework and conceive a novel congestion control method, namely, deep reinforcement learning-aided loss-tolerant congestion control (DLCC), to alleviate congestion while maintaining a tolerable packet loss imposed by the buffer overflow. The proposed DLCC employs deep reinforcement learning (DRL) to solve the curse of state dimensionality, while packet loss constraints are handled by utilizing Lagrange multipliers to integrate the reward with loss constraints. By dynamically updating Lagrange multipliers in an online learning procedure, DLCC finds the optimal congestion control policy. Our simulation results show that DLCC maintains the packet loss rate below the tolerable threshold in the presence of congestion. In contrast to existing hybrid congestion control algorithms, the proposed DLCC algorithm is more energy efficient and provides higher throughput, lower average delay, and better fairness.

Widespread deployment of Small Satellite Networks (SSN) fosters the foreseen integration of space-air-ground networks to provide worldwide Internet access to oceanic and remote airspace. However, the dynamic topology of SSN, the lossy wireless link, and the limited transmission resources induce unprecedented challenges to Deterministic Satellite Network Transmission (DSNT) for the sake of improving the utility of the SSN facility. Motivated by these challenges, this work aims to develop a Deterministic Satellite Network Transmission approach with deterministic Spatio-temporal routing, Redundant coding and Multipath scheduling (DSNT-SRM) for bolstering superior communications of SSN. DSNT-SRM uses the ephemeris information and dynamic resource update mechanism to predict all upcoming communication opportunities and construct the deterministic spatio-temporal routing paths. By combining sparse and redundant network coding mechanisms, DSNT-SRM no longer cares about the arrival of each packet, but the number of coded packets it receives, since the lost packets can be compensated with deterministic redundant traffic. Moreover, the adaptive traffic balance between multiple spatio-temporal paths is designed to provide a deterministic delay guarantee when facing limited node resources and multi-user competition. Extensive experiments show that DSNT-SRM can achieve satisfactory performance improvement in reducing delay and improving delivery rate.

Self-piercing riveting (SPR) is widely used for joining lightweight and dissimilar materials in automotive body manufacturing, the quality of which directly affects the safety of vehicles. However, there is still no reliable method that can be used for SPR quality control without destructive test and manual intervention. This article presents an online nondestructive SPR defect detection method based on deep learning. By learning the temporal dependencies of punch force varying with rivet displacement under different joint combinations, the proposed method can provide real-time defect alarms and avoid the enormous cost of joint dissection. We develop an SPR parameter selection mechanism to rule out the irrelevant parameters, which enhances the learning performance. For the problem of model overfitting caused by the savage imbalance of SPR data, we design a conditional generative adversarial network based data generation model. In order to accommodate the difference in defect patterns between factory and laboratory, we devise a transfer learning based model migration method, which substantially reduces the amount of labeled factory data for model training. The evaluations on real SPR data collected from two car assembly lines of Audi and NIO verify that the proposed method achieves a high detection accuracy and a low missing rate in SPR defect detection.

Driven by the prevalence of the computation-intensive and delay-intensive mobile applications, Mobile Edge Computing (MEC) is emerging as a promising solution. Traditional task offloading methods usually rely on centralized decision making, which inevitably involves a high computational complexity and a large state space. However, the MEC is a typical distributed system, where the edge servers are geographically separated, and independently perform the computing tasks. This fact inspires us to conceive a distributed cooperative task offloading system, where each edge server makes its own decision on how to allocate local computing resources and how to migrate tasks among the edge servers. To characterize diverse task requirements, we divide the arrival tasks into different priorities according to the tolerance time, which enables to dynamically schedule the local computing resources for reducing the task timeout. In order to coordinate the independent decision makings of geographically separate edge servers, we propose a priority driven cooperative task offloading algorithm based on multi-agent deep reinforcement learning, where the decision making of each edge server not only depends on its own state but also on the shared global information. We further develop a Variational Recurrent Neural Network (VRNN) based global state sharing model which significantly reduces the communication overhead among edge servers. The performance evaluation conducted on a movement trajectories dataset of mobile devices verifies that the proposed algorithm can reduce the task consumption time and improve the edge computing resources utilization.

Aeronautical Ad-hoc Networks (AANETs) have been proposed as the promising complement to terrestrial networks for promoting the global interconnection to provide in-flight network service, emergency communication, vessel traffic service, etc. However, the large network scale of AANETs may induce severe synchronization overhead when the traditional topology-based networking algorithms are adopted. Moreover, the high-dynamic topology and changeable flight routes make the existing position-based routing algorithms suffer loop routing and forwarding failure. Motivated by these problems, this paper aims at developing efficient and low-cost networking and routing algorithms relying on the concept of dynamic virtual topology which organizes the disordered topology of AANETs into a structural and simplified one. The basic idea is that each connected aircraft is assigned with a unique and sequentially increased Virtual Identifier (VID) and thus all the connected aircrafts are organized into a virtual cluster consisting of one trunk and several branches. An event-driven synchronization mechanism is leveraged for maintaining the virtual topology as well as relieving the communication burden imposed by periodical broadcasting. By jointly considering the geographic locations and the virtual locations of aircrafts, we formulate the routing problem in AANETs as a weighted distance minimization problem, and further propose a novel routing algorithm, namely Trunk-Branch Cooperation aided Routing (TBCR) algorithm. Specifically, TBCR employs the geographic greedy forwarding strategy for enhancing its flexibility and boosts the routing efficiency by adopting the loop-free virtual topology based local forwarding. For extending the networking and routing algorithms to the global range, a multi-domain routing solution is also provided. Extensive experimental results show that the proposed Virtual Topology based Networking (VTN) cooperated with TBCR can reduce at least 30% average end-to-end transmission delay in large-scale AANETs and provide more than 90% lower synchronization overhead than the existing solutions.

This paper considers the anti-jamming optimization problem for tracking multiple moving target flight vehicles in the presence of deception jammers. Since the radar is not able to separate the real target vehicles from a large number of deceptive vehicles, we promote the existing non-anti-jamming tracking model to the anti-jamming partially observable Markov decision process-based (POMDP-based) game tracking model by establishing a new anti-jamming Bayesian tracker. The proposed tracker is able to separate the hidden real target vehicles and establish their accurate trajectories, but the limited radar resources will decrease the accuracy. In order to effectively utilize the limited resources to guarantee the anti-jamming performance, this work deduces the anti-jamming performance gradients with respect to the resource management policy, which can be estimated with the asymptotically vanished biases. With the gradient estimates, the optimal anti-jamming resource management policy can be found with the tolerable complexity. The convergence analysis shows that the algorithm converges to a Nash equilibrium solution with probability 1. Numerical results show that the proposed algorithm can obtain the accurate target trajectories in the presence of jammers.

In this paper, we analyze the performance of a cloud radio access network (CRAN), consisting of multiple randomly distributed remote radio heads (RRHs) and a macro base station (MBS), each equipped with multiple antennas. To model the spatial distribution of RRHs and analyze its performance, we use stochastic geometry tools. In contrast to previous works on CRAN that consider Poisson Point Process (PPP) model for the spatial distribution of RRHs, we consider a more realistic Matérn hard-core point process (MHCPP) model that imposes a certain minimal distance (referred to as hard-core distance) between the two RRHs so that the RRHs are not too close to each other. To compare system performance of CRAN when different transmission strategies are used, three RRH selection schemes are adopted including 1) the best RRH selection (BRS); 2) all RRHs participation (ARP); and 3) nearest RRH selection (NRS). Considering downlink transmission, the ergodic capacity, outage probability, and system throughput of CRAN are analytically characterized for different RRH selection schemes. The presented results demonstrate that compared to PPP model, the increase in hard-core distance will result in a higher outage probability and cause a negative impact on ergodic capacity. Furthermore, when the same total transmit power is consumed, BRS scheme provides the best outage performance while ARP scheme is the best RRH selection scheme when the same transmit SNR at each RRH is assumed. Moreover, it is shown that the hard-core distance has a more significant impact on systems with higher intensity of PPP distributed candidate points and in large hard-core distance regime increasing the intensity of candidate points can only provide a small improvement in outage performance. We extend our work to multiuser case with zero-forcing (ZF) precoding where it is proven that the results in multiuser case reduce to the derived results in this work by substituting K=1 for single-user.