Unmanned Aerial Vehicle (UAV) remote sensing, with its advantages of rapid information acquisition and low cost, has been widely applied in scenarios such as emergency response. However, due to the long imaging distance and complex imaging mechanisms, targets in remote sensing images often face challenges such as small object size, dense distribution, and low inter-class discriminability. To address these issues, this paper proposes a multi-modal remote sensing object detection network called RemoteDet-Mamba, which is based on a patch-level four-direction selective scanning fusion strategy. This method simultaneously learns unimodal local features and fuses cross-modal patch-level global semantic information, thereby enhancing the distinguishability of small objects and improving inter-class discrimination. Furthermore, the designed lightweight fusion mechanism effectively decouples densely packed targets while reducing computational complexity. Experimental results on the DroneVehicle dataset demonstrate that RemoteDet-Mamba achieves superior detection performance compared to current mainstream methods, while maintaining low parameter count and computational overhead, showing promising potential for practical applications.

Constructive neural routing solvers usually score the next action by matching a decoder context to candidate embeddings, hiding deterministic one-step consequences such as travel, waiting, slack, and capacity changes. We propose LINC (Local Inference via Normed Comparison), a decoder-side candidate decision architecture that computes these consequences explicitly. LINC uses them according to their decision role: centered relative consequences are compared by a shared linear local scorer, while feasible-set summaries modulate the decoder context. This preserves standard global matching and relieves the hidden state from rediscovering transition arithmetic. The Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) serves as the main constrained-routing stress test; the same interface extends to the Capacitated Vehicle Routing Problem (CVRP) and Traveling Salesman Problem (TSP). In particular, for CVRPTW, LINC reduces PolyNet's Solomon/Homberger gaps from 13.83\%/38.15\% to 7.26\%/14.71\%; for TSP and CVRP, it also improves external-benchmark gaps.

In emergency communications constrained by bandwidth limitations, unmanned aerial vehicle (UAV)-based coded caching presents a promising approach for the efficient dissemination of high-bandwidth-demanding services. This paper focuses on content download and content repair in aerial caching networks, where UAVs deliver contents to both ground users and invalid UAVs. To address potential data loss due to limited power and high mobility, fault-tolerant codes are utilized to maintain data availability and reliability. Initially, we derive the expressions of communication cost and success rate for content download and content repair. The size of coded fragments, determined by the coding design, affects both the success rate and transmission cost, while the resource allocation, which influences the cooperative relationships, also impacts these two aspects. The interplay between coding design and resource allocation is thus established to jointly optimize the overall performance. Then, we design a joint optimization problem of erasure coding schemes, coding parameters, matching relations, and UAV trajectories to maximize the overall success rate. Moreover, we propose a hierarchical multi-agent parameterized deep Q-network (H-MA-PDQN) algorithm integrating a dual-component structure for long-term coding and immediate resource allocation to solve the mixed integer nonlinear programming (MINLP), and each agent employs a PDQN with hybrid discrete-continuous action space. Simulation results demonstrate that our proposed H-MA-PDQN algorithm increases the success probability by 26.7% and 66.7% and reduces the transmission cost by 27.3% and 42.9% compared with the DQN and greedy-based strategies, respectively.

Visible-infrared image fusion constitutes a pivotal paradigm for achieving all-weather robust object detection. However, its performance gains are frequently impeded by cross-modal semantic heterogeneity. This issue degrades representation quality in two primary ways. First, heterogeneous noises (e.g., thermal artifacts and shadows) induce severe feature contamination. Second, modality-specific advantages are often diluted. Due to heterogeneous feature representations, targets salient in only a single modality are frequently suppressed by invalid information from the other, ultimately leading to missed detections. Addressing these issues, we propose a novel framework for mining heterogeneous advantages via feature purification and recombination for multimodal object detection, termed FPR-Net. To this end, a cross-adversarial heterogeneous noise suppression (CAHN) mechanism is designed to facilitate feature purification. By mining the untainted semantic advantages of the complementary modality, CAHN dynamically guides the noise-corrupted modality to achieve robust semantic clarity. Furthermore, a saliency-guided feature recombination (SGFR) strategy is proposed to mitigate the dilution of modality-specific advantages. SGFR uses local response deviation to construct spatial saliency priors and enhance task-relevant components. By disentangling and recombining these features, it systematically mines heterogeneous advantages. Extensive experiments on four public datasets demonstrate that FPR-Net achieves significant performance gains in both remote sensing and natural scenes. Specifically, FPR-Net achieves an mAP50 of 81.5% on the DroneVehicle dataset and 83.0% on the VEDAI dataset.

To meet the extended ultra-low latency and high reliability (xURLLC) requirements for autonomous driving systems, multiple access schemes must operate reliably in high-mobility and complex propagation environments. Recently, rate-splitting multiple access (RSMA) has emerged as a promising multi-user transmission framework, showing robustness in dynamic situations where imperfect and outdated channel state information (CSI) is prevalent.Moreover, the advanced sensing, localization, and on-board computation capabilities of autonomous driving vehicles facilitate the construction of a channel knowledge map (CKM), which is a key enabler for environment-aware communications in future 6G networks.In this work, we propose a CKM empowered finite-blocklength (FBL) RSMA for downlink autonomous driving system. The location-dependent large-scale channel information provided by CKM is exploited in RSMA to develop a refined rate-splitting design. The min-rate performance of FBL rate splitting is analyzed explicitly to ensure user fairness. We derive a new and tight closed-form bound for the private-stream ergodic rate. Combined with the closed-form common-stream expression, an efficient optimization design of rate-splitting ratios has been formulated. Numerical results show that the CKM empowered FBL RSMA outperforms space-division multiple access (SDMA) and non-orthogonal multiple access (NOMA), particularly in high-mobility scenarios. Its performance is improved by a data-based CKM, which provides more accurate large-scale channel information than model-based approaches and enables more precise common-stream allocation. The results also reveal that RSMA is sensitive to errors in large-scale channel knowledge, emphasizing the importance of accurate CKM information for optimal rate-splitting.

In mountainous environments, terrain occlusion causes non-line-of-sight (NLoS) transmission, significantly reducing the signal propagation range. To improve emergency rescue efficiency, a mobile uncrewed aerial vehicle (UAV)-based integrated localization and communication (ILAC) network should be deployed to achieve optimal performance through adaptive trajectory planning and resource allocation. However, irregular and unpredictable terrain occlusions, coupled with dynamic users, make traditional optimization ineffective and reinforcement learning (RL) inefficient. To address these challenges, this paper proposes a hybrid action space soft Actor-Critic with geographic information-based state aggregation (GeoAgg-HSAC) decision-making scheme. First, an RL state aggregation method based on graph contrastive learning is designed. Through a pre-trained graph neural network (GNN), the UAV network states experiencing the same occlusion are mapped to similar low-dimensional representations. This method reduces the state dimension and allows similar states to share policy experience, thereby improving sample efficiency and accelerating convergence. A hybrid action space SAC network is then designed, which simultaneously makes decisions for continuous UAV trajectories and discrete resource allocation. Finally, a simulation environment based on real mountain terrain and wireless data is built for the experiment. The experimental results show that the proposed scheme has significant advantages for optimizing communication and localization performance.

In emergency rescue missions, the perception of embodied agents (e.g., rescue robots) rely heavily on high-quality semantic information. However, deploying complex embodied semantic models within constrained Unmanned Aerial Vehicle (UAV) networks introduces severe resource conflicts. We propose a semantic-aware framework tailored for embodied intelligence networks, jointly optimizing model caching, user association, semantic compression factors, and computational resources to minimize total system delay while satisfying energy and semantic fidelity constraints. To solve this multi-timescale mixed-integer problem, we develop a Two-Timescale integrated Matching and Multi-Agent Deep Reinforcement Learning (2T-M2ADRL) algorithm. It utilizes Hierarchical Bipartite Matching (HBM) for discrete large timescale topologies and a Multi-Head Attention based MADDPG (MHA-MADDPG) for small timescale resource orchestration. Furthermore, we construct an offline surrogate model to bypass evaluating semantic similarity via Large Language Models (LLMs) during training. Extensive simulations confirm our framework outperforms baselines, ensuring optimal latency and semantic accuracy.

With the growing demand for large-scale and high-quality data in edge intelligence systems, mobile robots are increasingly deployed to collect data proactively, particularly in complex environments. However, existing robot-assisted data collection methods face significant challenges in achieving reliable and efficient performance, especially in non-line-of-sight (NLoS) environments. This paper proposes a communication-and-learning dual-driven (CLD) autonomous navigation scheme that incorporates region-aware propagation characteristics and a non-point-mass robot representation. This scheme enables simultaneous optimization of navigation, communication, and learning performance. An efficient algorithm based on majorization-minimization (MM) is proposed to solve the non-convex and non-smooth CLD problem. Simulation results demonstrate that the proposed scheme achieves superior performance in collision-avoidance navigation, data collection, and model training compared to benchmark methods. It is also shown that CLD can adapt to different scenarios by flexibly adjusting the weight factor among navigation, communication and learning objectives.

Multi-device collaborative computing has emerged as a promising paradigm to address the computational limitations of edge devices. However, the existing methods fail to simultaneously achieve high precision and low latency perception, while adapting to bandwidth fluctuations. Specifically, feature compression methods cause imbalanced category precision loss. We propose a category-adaptive feature compensation mechanism based on this, which analyzes gradient responses specific to each target category. It dynamically adjusts the feature reconstruction weights. Then, the feature compression method needs to consider the trade-off between high accuracy and low latency. We implement it by training a variable-sized discrete dictionary to dynamically map intermediate features to dictionary indices, adaptively reducing the amount of data transmitted. Experimental results show that our method can reduce the transmission latency by up to 98%, and limit the accuracy loss to less than 2%.

The unmanned aerial vehicle (UAV)-assisted Internet-of-Things (IoT) network architecture has emerged as a key technology for supporting communications in emergency scenarios. The quality and freshness of the data collected by UAVs directly impact the effectiveness of emergency decision-making and the overall responsiveness of the system in time-critical situations. However, limited by the battery capacity of UAVs, a fundamental tradeoff exists between information freshness and energy consumption in UAV-assisted IoT networks, which constrains the effective coverage range of emergency operations. Accordingly, this paper explores the inherent trade-off between Age of Information (AoI) and energy consumption in UAV-assisted emergency IoT systems. To reflect the highly dynamic and uncertain channel conditions typical of post-disaster environments, this work further incorporates an imperfect channel state information (CSI) model. Then, a probabilistic constraint AoI-energy tradeoff problem is formulated to jointly optimizing the time allocation of data collection, UAV trajectory, and duration of time slots. To address the resulting non-convex non-linear problem, we first convert the probabilistic constraint problem into a non-probability one, then employ a block coordinate descent method to iteratively solve the highly coupled multi-variable problem. Finally, comprehensive simulation results validate the effectiveness of the proposed method.

In disaster scenarios, infrastructure damage and wireless resource scarcity pose significant challenges for providing prompt and reliable communication and sensing (C&S) services. Recently, unmanned aerial vehicle (UAV) enabled integrated sensing and communication (ISAC) has emerged as a promising technique to tackle the above issues by leveraging flexibility and mobility of multiple UAVs to offer high-quality and cost-efficient C&S services. In parallel, rate-splitting multiple access (RSMA) facilitates customized transmission by partitioning messages into private and common parts with adjustable rates, thereby making it well-suited for on-demand data transmission in disaster scenarios. In this paper, we propose a framework that utilizes coordinated RSMA for ISAC (Coordinated RSMA-ISAC) in an emergency UAV system. This framework enables multiple transmit UAVs to simultaneously communicate with several communication survivors (CSs) and detect a potentially trapped survivor (TS) in a coordinated manner with imperfect channel state information (CSI). In addition, an optimization problem is formulated to jointly optimize the UAV-CS association, UAV deployment, and transmit beamforming to maximize the weighted sum rate (WSR) of the system, subject to the sensing signal-to-noise ratio (SNR) requirement. To efficiently solve such a mixed-integer non-convex programming (MINCP) problem, an iterative algorithm is proposed by applying the generalized Benders decomposition (GBD) technique. Specifically, the original problem is decoupled into a master problem for pure integer programming and a primal problem for non-convex programming. Then, we further use successive convex approximation (SCA), semi-definite relaxation (SDR), and cutting-plane techniques to solve the decoupled problems. Simulation results verify the effectiveness of the proposed algorithm, and demonstrate that the coordinated RSMA-ISAC framework outperforms conventional space division multiple access (SDMA), non-orthogonal multiple access (NOMA), and orthogonal multiple access (OMA) in terms of both C&S performance.

Streaming 3D reconstruction under a strict constant-memory budget hinges on how the recurrent state is updated as the stream evolves. We profile TTT3R-style per-token gates across five benchmarks and discover a structural bottleneck: the gate is intrinsically bounded in magnitude (median 0.31; never exceeding 0.6) and nearly frame-invariant, yielding an effective memory horizon of only ∼3 frames per state token, which serves as the structural origin of long-sequence drift. We trace this to a missing axis: existing inference-time methods modulate updates only at the per-token, intra-frame level, while the orthogonal frame-level question of how strongly each frame should contribute to the state has been treated as content-independent. We close this gap with a scalar frame-level gate α_t ∈ (0, 1] derived in closed form from frame-to-frame changes of internal features – a continuous relaxation of classical Simultaneous Localization and Mapping (SLAM) keyframe selection that requires no parameters, no training, and no extra forward pass. Across six benchmarks spanning camera pose, video depth, and 3D reconstruction at sequence lengths up to 4,541 frames, our gate cuts ATE by 51% on long TUM-RGBD pose sequences, reduces AbsRel by 12.8% on Bonn video depth, and on KITTI long-sequence pose estimation surpasses both LongStream and Keyframe-VO, while retaining strictly constant memory at zero training cost.

In this letter, we propose an airborne maneuverable bi-static integrated sensing and communication system where both the transmitter and receiver are unmanned aerial vehicles. By timely forming a dynamic bi-static range based on the motion information of the target, such a system can provide an adaptive two dimensional tracking and communication services. Towards this end, a trajectory optimization problem for both transmits and receive UAV is formulated to achieve high-accurate motion state estimation by minimizing the time-variant Cramer Rao bound, subject to the sufficient communication signal-to-noise ratio to maintain communication channel prediction error. Then we develop an efficient approach based on the successive convex approximation technique and the S-procedure to address the problem. Numerical results demonstrate that our proposed airborne maneuverable bi-static ISAC system is able to obtain higher tracking accuracy compared with the static or semi-dynamic ISAC system.

Fine-tuning large vision-language models such as CLIP on edge devices remains challenging due to severe memory and computational constraints. Although existing parameter-efficient fine-tuning methods can effectively reduce training costs, they are not specifically designed for cloud-edge collaborative scenarios. To address this limitation, we propose CE-CLIP, a cloud-edge collaborative fine-tuning framework. Specifically, we keep the pretrained CLIP backbone frozen and maintain it at the cloud, and deploy a lightweight multimodal side network at the edge enabling efficient adaptation of visual and textual features. We further propose a semantic token reduction module to compress features extracted in the cloud, significantly reducing communication overhead. In addition, we introduce a feature consistency loss to preserve the pretrained model's generalization capability during adaptation. Experimental results show that CE-CLIP achieves an average accuracy of 82.1% on few-shot adaptation tasks and outperforms most existing approaches in base-to-new generalization, demonstrating its strong adaptability. Furthermore, CE-CLIP features only 0.72M trainable parameters, 159 MB of edge memory usage, and 1.38 MB of communication overhead per image, demonstrating an effective balance among accuracy, efficiency, and adaptability in the cloud-edge collaboration paradigm.

Using solar power to drive ground sensors in an uncrewed aerial vehicle-Internet of Things (UAV-IoT) mobile edge computing (MEC) system deployed in inaccessible or hazardous areas provides a sustainable solution to battery replacement for IoT sensors. Nevertheless, this approach faces two critical challenges. First, terrain variations and landscape shadowing cause uneven light distribution, leading to significant disparities in solar energy harvesting among nodes, which subsequently affects system throughput fairness due to unequal energy availability for data computation and task offloading. Second, atmospheric attenuation dynamics introduce stochastic variations in solar panel output, resulting in energy conversion instability and potential temporal battery outages. These challenges are further aggravated by the randomness of data arrival, which can destabilize the data queue. To address these difficulties, in this article, we first design an $\alpha $ -fairness utility function to tackle the throughput fairness issue. After that, to handle the randomness of energy and data arrivals, we employ a Lyapunov-based optimization approach to maximize the long-term system utility function, formulating the problem as a multistage online stochastic optimization, with time-average constraints on solar energy supply, data queue stability, and energy consumption of the sensor. We then decompose the original problem into a series of deterministic per-slot optimization problems to decouple control solutions across slots. Afterward, we iteratively optimize the data admission control, communication and computation resource allocations, and the UAV's trajectory in each slot. The proposed scheme has low computation complexity for online execution. Extensive simulations demonstrate its effectiveness in achieving application-specific throughput fairness while maintaining energy and data queue stability under fluctuating working conditions. In addition, compared with benchmark algorithms, our scheme achieves higher system throughput through more judicious resource management and trajectory control strategies.

In emergency scenarios, uncrewed aerial vehicles (UAVs) show significant potential as aerial base stations (BSs) to establish reliable communication links and provide localization services through integrated air-ground collaboration. This paper proposes a novel energy-efficient collaborative framework based on the solo-UAV-rescuer cooperative (SURC) paradigm, which synergistically enhances both communication capacity and localization accuracy. From a system optimization perspective, we formulate an optimization problem using a normalized combination of three critical metrics: achievable data rate, localization accuracy, and energy consumption. Specifically, to maximize the system's utility, we design a signal perception-based localization method that incorporates angle-of-arrival (AOA) localization information for guidance, and develop a beamforming scheme to facilitate high data rate communication. Building on these methods, we propose a deep reinforcement learning (DRL)-based synergistic communication and localization reinforcement (SYNCORE) approach that dynamically optimizes three key operational parameters: UAV trajectory planning, flight time, and transmission power control, achieving reliable services with energy-efficient operation. Based on the simulation results, we validate that the proposed scheme enhances communication and localization performance, while also improving energy efficiency, surpassing the baseline schemes.

In this paper, we propose a novel uncrewed aerial vehicle (UAV) and uncrewed ground vehicle (UGV) enabled air-ground collaborative system (UAV-UGV-AGCS) where a UAV and a UGV are utilized to realize multi-functional integration. Specifically, the UAV simultaneously provides communication, data collection services, and tracks a ground mobile target (GMT); while the UGV assists in collecting data from clusters of sensor nodes (SNs) and receiving sensing echo signals, thereby achieving high-rate communication, efficient data collection, and real-time tracking. Based on the dynamic characteristics and collaboration of the UAV and UGV, a joint trajectory optimization problem is formulated to maximize the average system throughput, subject to the UAV and UGV motion constraints as well as energy consumption limits. In particular, we derive the posterior Cram & eacute;r-Rao bound (PCRB) as a tracking performance metric. To tackle the non-convexity problem, we first design the tracking method based on the extended Kalman filtering (EKF) to estimate the motion parameters of GMT in each time slot and group the SNs via the immune optimization (IO)-based clustering algorithm. Subsequently, the problem is decomposed into two subproblems and solved via a successive convex approximation (SCA)-based iterative optimization algorithm to obtain the optimal solutions. Simulation results demonstrate that the proposed framework achieves efficient data collection and superior communication performance, thereby meeting the demands of high-precision tracking by dynamically adjusting trajectories of UGV and UAV, compared with other benchmark network designs.

Rate-splitting multiple access (RSMA) is a key technology for next-generation multiple access systems due to its robustness against imperfect channel state information (CSI). This makes RSMA particularly suitable for high-mobility autonomous driving, where ultra-reliable and low-latency communication (URLLC) is essential. To address the stringent requirements, this study enables RSMA finite blocklength (FBL) transmissions and explicitly evaluates the ergodic performance. We derive the closed-form lower bound for the ergodic sum-rate of RSMA, considering vital factors such as the vehicle velocities, vehicle positions, power allocation of each stream, blocklengths, and block error rates (BLERs). To further enhance the ergodic sum-rate while complying with quality of service (QoS) rate constraints, we jointly optimize the global power coefficient, private power distribution, and common rate splitting. Guided by gradient descent, we first adjust the global power coefficient based on its sum-rate solution. This parameter regulates the power state of the common stream, allowing for dynamic activation or deactivation: if active, we optimize the private power distribution and adjust the common rate splitting to meet minimum transmission constraints; if inactive, we use the sequential quadratic programming for private power distribution optimization. Simulation results confirm that our RSMA scheme significantly improves the ergodic performance, reduces blocklength and BLER, surpassing the RSMA counterpart with average private power and space division multiple access (SDMA). Furthermore, our approach is validated to guarantee the rates for users with the poorest channel conditions, thereby enhancing fairness across the network.

Driven by the rapid proliferation of edge inference, collaborative inference among edge devices has emerged as a promising paradigm for addressing resource constraints in computationally intensive tasks. However, existing approaches often suffer from inefficient utilization of heterogeneous resources and lack dynamic adaptation to varying device capabilities and network conditions. To address these challenges, we present a novel collaborative inference framework that prioritizes energy-accuracy co-optimization through workload partitioning and resource-aware model pruning. Specifically, we first design a workload partitioning strategy aimed at maximizing resource utilization efficiency in heterogeneous environments. Dynamically distributes image slices to edge devices by closely matching tasks with their specific computational, energy, and network profiles, ensuring balanced load and minimized latency. Moreover, we propose an adaptive pruning method that is aware of energy-accuracy tradeoffs. It is equipped with dual predictors that model the accuracy and energy consumption under different pruning rates in real-time, enabling dynamic pruning rate selection to optimally balance performance with energy efficiency. The experimental results on the VisDrone2019 dataset demonstrate that our method achieves significant improvements over existing approaches, improving detection accuracy by up to 1.3% in mAP50 while increasing inference speed by 5.3× compared to slicing methods. Furthermore, our adaptive pruning strategy reduces energy consumption by up to 32% while maintaining comparable accuracy across various edge devices, such as the NVIDIA AGX Orin and Jetson TX2.

This paper introduces a collaborative mode between ground rescue vehicle (GRV) and unmanned aerial vehicle (UAV) into emergency delivery, aiming to alleviate the action burden on rescue personnel through collaborative transportation. In the constructed collaborative model, multiple UAVs and GRVs are included, with UAVs capable of visiting multiple task nodes after each launch. The GRV is not required to complete the UAV's support operations at the same node, and the collaborative process is dynamic. The GRV can synchronously perform rescue tasks, while the UAV can also visit some ineligible nodes to obtain support. These considerations are expected to enhance the flexibility of the collaborative process. To solve this model, a hybrid heuristic algorithm with a synchronization strategy is designed, comprising three components: route planning, time synchronization, and heuristic search. Compared to multiple benchmark algorithms, case test results verify the effectiveness of the proposed method. Through testing multiple parameter combinations and real-world cases, detailed analysis of collaboration characteristics under varying capabilities was conducted, providing management insights for applying collaboration modes in emergency delivery scenarios.