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.

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.

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.

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.

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.

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.

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.