Shenghao Ye, Yu Guo, Dong Jin, Yuxiang Wang, Yikai Shen, Yunpeng Hou, Shuangwu Chen, Jianyang, Xiaofeng Jiang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

This paper presents an improved Quantum Convolutional Neural Network (QCNN) architecture that overcomes key limitations of current quantum-classical hybrid models for image classification. To address challenges in expressivity and feature extraction on noisy intermediate-scale quantum (NISQ) devices, we propose two key innovations: (1) Gate Encoding Optimization, which reduces quantum bit (qubit) usage while retaining spatial information, and (2) Quantum Channel Attention (QCA), a novel attention mechanism tailored for hybrid QCNNs which utilizes measurement-driven feedback to capture inter-channel dependencies. Evaluated on the MNIST dataset, our model achieves 95.68 % accuracy—on par with classical CNNs (95.73 %) and significantly better than a baseline QCNN (93.23%)—while reducing parameters by 49.6 %. These results demonstrate the proposed model's effectiveness and practicality for quantum machine learning in the NISQ era.

The detection of small objects by unmanned aerial vehicles (UAVs) is a popular research direction in the field of computer vision, and its applications cover a wide range of civil, commercial and military fields. In this paper, we propose a density information-based algorithmic framework for UAV small object detection, aiming to improve the detection accuracy and recall of small object features in complex backgrounds. Firstly, we generate a corresponding density map based on the labeling information of the train set and then use a sliding-window algorithm on the density map to obtain the subregion with the highest density for data enhancement during model training, so as to enable the model to learn the features more fully. In addition, during the model inference process, a dense cropping algorithm is introduced to detect the dense regions in a coarse-to-fine manner, with the aim of reducing the number of misses and false positives. The effectiveness of the proposed method is verified on the Visdrone 2019 dataset, and the experimental results show that the method improves in terms of detection accuracy compared with the existing methods.

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.

In this paper, an optimal asymmetric controlled teleportation protocol is proposed, where a three-dimensional (3D) GHZ entangled state is utilized to teleport an arbitrary unknown two-dimensional (2D) qubit and the correlation between the two entangled qutrits caused by their continuous transmission through a noisy channel is considered. We design a high-dimensional feed-forward control operator and use weak measurement reversal instead of the unitary operations in standard teleportation to mitigate the reduction in fidelity caused by noise. We further derive the average fidelity and overall success probability of the proposed teleportation protocol, and simulate its performance under correlated amplitude damping (CAD) and amplitude damping (AD) noise channels. The simulation results show that our protocol significantly improves the average fidelity under CAD noise. In particular, under AD noise, the average fidelity remains constant at 1. Moreover, we derive the optimal overall success probability without compromising fidelity. To verify the superiority of the feed-forward control method, we also calculate and analyze the performance of an asymmetric controlled teleportation protocol using only environment-assisted measurement (EAM), and compare it with the performance of our protocol through simulation. The results demonstrate that our protocol outperforms the protocol using only EAM methods in both CAD and AD noise.

Website Fingerprinting (WF) attacks have posed a serious threat to the anonymity of the onion router (Tor) communication system, as attackers can passively pry into the encrypted traffic and infer the website visited by users. To defend against WF, recent studies focus on adversarial perturbations. However, most of them suffer from a high bandwidth overhead and a low defense performance. To address this problem, our basic idea is to generate perturbation only on the sensitive regions, which can effectively mask the website’s fingerprint, thus misleading the WF attack models and reducing the bandwidth overhead. In this paper, we formulate a joint optimization problem of perturbation position and magnitude by confining the perturbations within sensitive regions, which is rarely considered in the literature. We propose a robust low-overhead WF defense method based on reinforcement learning (RL), named RLpatch. RLpatch identifies the common sensitive regions of various surrogate models and adjusts perturbation according to the query result from a query WF model. It further employs the positional frequency of perturbations to generate a common perturbation paradigm for different traces of a same website. Experimental results show that RLpatch achieves higher defense performance, lower bandwidth overhead and better robustness against adversarial training compared to the state-of-the-art methods.

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.

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.

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.

The Tucker Decomposition (TKD) is able to provide the low-dimensional and informative representations of real-world large-scale tensorial data, which are necessary to extract potential features and enhance the original data. However, computing such decomposition directly for a dense tensor is usually computationally elusive, due to the repetitive operations of computing large-scale tensor-matrix product. Instead of direct decomposition, this paper proposes an efficient algorithm for seeking the Faster TKD of the large-scale tensor, which is a lightweight decomposition approach based on the technique of randomized sampling. The proposed algorithm first converts the original large-scale tensor into a small-scale subtensor via full-mode sampling operation, and then the core tensor of TKD can be computed directly based on the subtensor with low complexity. Finally, an approximate TKD of the original large-scale tensor can be obtained after sequentially computing approximate full-mode factor matrices. A theoretical error analysis is provided to show that the approximation error approximates zero with high probability, and the proposed algorithm is verified based on real tensorial data of$\text{23821.24}~GB$.

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.

The volatile, covert and slow multistage attack patterns of Advanced Persistent Threat (APT) present a tricky challenge of APT detection, which are vital for organisations to protect their critical assets. In this article, we aim to develop system that aggregates and uses existing systems’ alerts to detect APTs. In order to achieve this, we propose a causal correlation aided semantic analysis system, calledPoirot, for detecting the multi-stage threats over a long-time span from existing systems’ alerts.Poirotis capable of autonomously mining causality between anomalous events, which instructs us in reorganizing the original alerts and in constructing alert-chains. The system further exploits the Latent Dirichlet Allocation (LDA) to model the semantic context of the alert-chains. This LDA model facilitates us to carry out the semantic analysis for capturing the latent attack intent as well as for reconstructing the APT scenario. We use an alert dataset provided by a cyber security company to verify the proposedPoirotin terms of the detection accuracy and the capability of attack scenario reconstruction. The experiment results are presented to show the achievable performance of the proposed semantic analysis based APT detection.

The performance of Dynamic Adaptive Streaming (DAS) in multi-client scenarios can be improved by taking advantage of the aggregation capability of Named Data Networking (NDN). In this paper, we propose a client-side game theory based (GB) ABR algorithm for NDN that can achieve proactive aggregation of requests among clients as much as possible without requiring coordinating with other clients or scheduling by a central controller. We model the interaction between a DAS client and network as an incomplete information non-cooperative game. Then, this game is transformed into a complete but imperfect information game by Harsanyi transformation, and each client can issue an appropriate bitrate request by solving the Bayesian Nash Equilibrium (BNE) problem respectively. By designing the payoff function pair elaborately, the equilibrium point of the game can correspond to the situation that multiple clients issuing the same video bitrate request, that is, requests aggregation, which will reduce the repeated traffic and also achieve fairness. Compared with the existing solutions, through simulation and real-world experiments in multi-client video distribution scenarios, the GB algorithm outperforms the comparison algorithms in terms of overall Quality of Experience (QoE), fairness, and network bandwidth utilization, etc.

Caching contents close to end-users can improve the network performance, while causing the problem of guaranteeing consistency. Specifically, solutions are classified into validation and invalidation, the latter of which can provide strong cache consistency strictly required in some scenarios. To date, little work on the analysis of cache invalidation has been covered. In this work, by using conditional probability to characterize the interactive relationship between existence and validity, we develop an analytical model that evaluates the performance (hit probability and server load) of four different invalidation schemes with LRU replacement under arbitrary invalidation frequency distribution. The model allows us to theoretically identify some key parameters that affect our metrics of interest and gain some common insights on parameter settings to balance the performance of cache invalidation. Compared with other cache invalidation models, our model can achieve higher accuracy in predicting the cache hit probability. We also conduct extensive simulations that demonstrate the achievable performance of our model.

Low Earth Orbit (LEO) satellite networking has been an indispensable and promising concept for extending the Internet coverage of future space-air-ground integrated networks to oceanic and remote airspace. However, the topology dynamics of the LEO Satellite Network (LEO-SN) for network state perception (Liet al., 2019) and the intermittent nature of the Inter-Satellite Links (ISLs) for multipath routing discovery (Wanget al., 2019, Jianget al., 2019) both induce new complicated challenges to multipath traffic scheduling for the sake of improving the utility of the LEO-SN facility (Songet al., 2014, Zhanget al., 2018, and Yanget al., 2020). Motivated by these challenges, this paper aims to develop an AI aided intelligent multipath traffic scheduling approach for bolstering autonomous and efficient communications of LEO-SN. To achieve this, we formulate the multipath traffic scheduling problem into a pheromone incentivized Markov Decision Process (MDP) by considering ant routing protocol and adapting pheromone to LEO-SN. Employing enhanced pheromone characterizing network state, we propose ant-inspired multipath routing discovery, which is capable of promptly discovering routing paths available in the dynamic topology. To improve the utility of these discovered routing paths, we employ deep deterministic policy gradient into the pheromone-incentivized MDP-based scheduling problem to derive an intelligent multipath traffic scheduling strategy. The experimental results are further presented to show the achievable performance improvement.

The soaring mobile data traffic demands have spawned the innovative concept of mobile edge caching in ultra-dense next-generation networks, which mitigates their heavy traffic burden. We conceive cooperative content sharing between base stations (BSs) for improving the exploitation of the limited storage of a single edge cache. We formulate the cooperative caching problem as a partially observable Markov decision process (POMDP) based multi-agent decision problem, which jointly optimizes the costs of fetching contents from the local BS, from the nearby BSs and from the remote servers. To solve this problem, we devise a multi-agent actor-critic framework, where a communication module is introduced to extract and share the variability of the actions and observations of all BSs. To beneficially exploit the spatio-temporal differences of the content popularity, we harness a variational recurrent neural network (VRNN) for estimating the time-variant popularity distribution in each BS. Based on multi-agent deep reinforcement learning, we conceive a cooperative edge caching algorithm where the BSs operate cooperatively, since the distributed decision making of each agent depends on both the local and the global states. Our experiments conducted within a large scale cellular network having numerous BSs reveal that the proposed algorithm relying on the collaboration of BSs substantially improves the benefits of edge caches.

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.

Promptly discovering unknown network attacks is critical for reducing the risk of major loss imposed on organizations and information infrastructure. This paper aims at developing an intelligent intrusion detection system capable of classifying known attacks as well as inferring unknown ones. To achieve this, we formulate the problem of fine-grained known/unknown intrusion detection as a two-stage minimization problem, where the first stage is to seek a score measure for minimizing the empirical risk of misclassifying the known attacks, while the second stage is to find another score measure for minimizing the identification risk of inferring unknown attacks. The hierarchical nature of problem formulation allows us to employ the class conditioned auto-encoders to construct a hierarchical intrusion detection framework. Since the reconstruction errors of unknown attacks are generally higher than that of the known attacks, we further employ extreme value theory in the second stage to model the distribution of reconstruction errors for differentiating known/unknown attack. To further reduce the false positive rate, we add a benign clustering module for learning the multimodal distribution of benign traffic. We conduct an experiment on two widely used datasets for assessing intrusion detection. The results show that the proposed method improves the detection rate of unknown attacks while keeping a low false positive rate.

Scalable video streaming over femtocell networks relying on two-tier spectrum-sharing is designed for coping with time-varying channel conditions, stringent video QoS requirements as well as with strong cross-tier interference between the over-sailing macro- and the femtocells. Dynamic video layer selection and resource allocation are invoked to enable the adaptation of the scalable video streaming service to the dynamics of both channel quality and interference price fluctuations. We formulate the design as a constrained stochastic optimization problem, which strikes a compelling compromise between the perceivable quality of experience and the monetary implications of the interference. Since the time scale of resource allocation is more short term than that of the video layer selection, we decompose the original long-term utility optimization problem into a pair of readily tractable subproblems with the aid of two different time-scales by invoking the powerful technique of Lyapunov drift and optimization. By exploiting the specific structure of these subproblems, low-complexity algorithms are derived for dynamic video layer selection and resource allocation, which rely on the near-instantaneously available information rather than on any prior statistical knowledge. Finally, we derive the analytical bounds of the theoretically achievable performance. Experimental results are presented for characterizing the performance attained.

This paper considers the problem of opportunistically accessing a wide range of frequency band in which multiple subbands may be occupied. A major obstacle to utilizing such wideband spectrum is that performing Nyquist sampling on the wideband signal is either infeasible or too expensive. We propose an adaptive energy-constrained sensing scheme based on sub-Nyquist sampling and stochastic control theory. In contrast to the existing sub-Nyquist approaches, we select the subband that has high probability to be idle based on the sub-Nyquist samples and spectrum prediction, without reconstructing the wideband signal. The sensing process is formulated as a constrained partially observable Markov decision process to exploit the statistical characteristics of the wideband signal, and a simulation-based gradient algorithm is proposed to compute the optimal adaptive sensing policy. The algorithm is shown to converge to the optimal solution with probability one. Simulation results show that with low computational complexity, the adaptive sensing policy performs well even in the crowded spectrum with low SNR.