arXiv subjects

Dapeng Oliver Wu

Publications and source records attributed to Dapeng Oliver Wu.

At least 19 recordsLinked to original sources

arXiv2026

Adaptive Plug-and-Play Channel Estimation with Consistency Models for MIMO Systems

This paper proposes a consistency-model-based channel estimation algorithm for multiple-input multiple-output (MIMO) systems. The proposed algorithm employs a consistency model (CM) to learn the angle-domain channel distribution and uses the trained CM as a plug-and-play (PnP) generative prior for MIMO channel estimation. The proposed algorithm alternates between a pilot-observation-based data-consistency update and a CM-prior-based denoising update. In addition, the proposed algorithm adaptively selects the penalty parameter according to residual energy and residual whiteness, and adjusts the CM denoising level according to the observed signal-to-noise ratio (SNR), thereby avoiding the performance degradation caused by fixed inference schedules under varying observation conditions. Simulation results show that the proposed algorithm not only reduces the number of inference steps by 50%--90, but also achieves high estimation accuracy and favorable cross-dataset performance.

eess.SP↗

arXiv2026

ANIMC: A Soft Framework for Auto-weighted Noisy and Incomplete Multi-view Clustering

Multi-view clustering has wide applications in many image processing scenarios. In these scenarios, original image data often contain missing instances and noises, which is ignored by most multi-view clustering methods. However, missing instances may make these methods difficult to use directly and noises will lead to unreliable clustering results. In this paper, we propose a novel Auto-weighted Noisy and Incomplete Multi-view Clustering framework (ANIMC) via a soft auto-weighted strategy and a doubly soft regular regression model. Firstly, by designing adaptive semi-regularized nonnegative matrix factorization (adaptive semi-RNMF), the soft auto-weighted strategy assigns a proper weight to each view and adds a soft boundary to balance the influence of noises and incompleteness. Secondly, by proposingθ-norm, the doubly soft regularized regression model adjusts the sparsity of our model by choosing differentθ. Compared with existing methods, ANIMC has three unique advantages: 1) it is a soft algorithm to adjust our framework in different scenarios, thereby improving its generalization ability; 2) it automatically learns a proper weight for each view, thereby reducing the influence of noises; 3) it performs doubly soft regularized regression that aligns the same instances in different views, thereby decreasing the impact of missing instances. Extensive experimental results demonstrate its superior advantages over other state-of-the-art methods.

cs.CV↗

arXiv2026

V3H: View Variation and View Heredity for Incomplete Multi-view Clustering

Real data often appear in the form of multiple incomplete views. Incomplete multi-view clustering is an effective method to integrate these incomplete views. Previous methods only learn the consistent information between different views and ignore the unique information of each view, which limits their clustering performance and generalizations. To overcome this limitation, we propose a novel View Variation and View Heredity approach (V3H). Inspired by the variation and the heredity in genetics, V3H first decomposes each subspace into a variation matrix for the corresponding view and a heredity matrix for all the views to represent the unique information and the consistent information respectively. Then, by aligning different views based on their cluster indicator matrices, V3H integrates the unique information from different views to improve the clustering performance. Finally, with the help of the adjustable low-rank representation based on the heredity matrix, V3H recovers the underlying true data structure to reduce the influence of the large incompleteness. More importantly, V3H presents possibly the first work to introduce genetics to clustering algorithms for learning simultaneously the consistent information and the unique information from incomplete multi-view data. Extensive experimental results on fifteen benchmark datasets validate its superiority over other state-of-the-arts.

cs.LG↗

arXiv2026

Unbalanced Incomplete Multi-view Clustering via the Scheme of View Evolution: Weak Views are Meat; Strong Views do Eat

Incomplete multi-view clustering is an important technique to deal with real-world incomplete multi-view data. Previous works assume that all views have the same incompleteness, i.e., balanced incompleteness. However, different views often have distinct incompleteness, i.e., unbalanced incompleteness, which results in strong views (low-incompleteness views) and weak views (high-incompleteness views). The unbalanced incompleteness prevents us from directly using the previous methods for clustering. In this paper, inspired by the effective biological evolution theory, we design the novel scheme of view evolution to cluster strong and weak views. Moreover, we propose an Unbalanced Incomplete Multi-view Clustering method (UIMC), which is the first effective method based on view evolution for unbalanced incomplete multi-view clustering. Compared with previous methods, UIMC has two unique advantages: 1) it proposes weighted multi-view subspace clustering to integrate these unbalanced incomplete views, which effectively solves the unbalanced incomplete multi-view problem; 2) it designs the low-rank and robust representation to recover the data, which diminishes the impact of the incompleteness and noises. Extensive experimental results demonstrate that UIMC improves the clustering performance by up to 40% on three evaluation metrics over other state-of-the-art methods.

cs.LG↗

arXiv2026

SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation

Recent self-supervised stereo matching methods have made significant progress. They typically rely on the photometric consistency assumption, which presumes corresponding points across views share the same appearance. However, this assumption could be compromised by real-world disturbances, resulting in invalid supervisory signals and a significant accuracy gap compared to supervised methods. To address this issue, we propose SMFormer, a framework integrating more reliable self-supervision guided by the Vision Foundation Model (VFM) and data augmentation. We first incorporate the VFM with the Feature Pyramid Network (FPN), providing a discriminative and robust feature representation against disturbance in various scenarios. We then devise an effective data augmentation mechanism that ensures robustness to various transformations. The data augmentation mechanism explicitly enforces consistency between learned features and those influenced by illumination variations. Additionally, it regularizes the output consistency between disparity predictions of strong augmented samples and those generated from standard samples. Experiments on multiple mainstream benchmarks demonstrate that our SMFormer achieves state-of-the-art (SOTA) performance among self-supervised methods and even competes on par with supervised ones. Remarkably, in the challenging Booster benchmark, SMFormer even outperforms some SOTA supervised methods, such as CFNet.

cs.CV↗

arXiv2026

Fine-Grained Motion Compression and Selective Temporal Fusion for Neural B-Frame Video Coding

With the remarkable progress in neural P-frame video coding, neural B-frame coding has recently emerged as a critical research direction. However, most existing neural B-frame codecs directly adopt P-frame coding tools without adequately addressing the unique challenges of B-frame compression, leading to suboptimal performance. To bridge this gap, we propose novel enhancements for motion compression and temporal fusion for neural B-frame coding. First, we design a fine-grained motion compression method. This method incorporates an interactive dual-branch motion auto-encoder with per-branch adaptive quantization steps, which enables fine-grained compression of bi-directional motion vectors while accommodating their asymmetric bitrate allocation and reconstruction quality requirements. Furthermore, this method involves an interactive motion entropy model that exploits correlations between bi-directional motion latent representations by interactively leveraging partitioned latent segments as directional priors. Second, we propose a selective temporal fusion method that predicts bi-directional fusion weights to achieve discriminative utilization of bi-directional multi-scale temporal contexts with varying qualities. Additionally, this method introduces a hyperprior-based implicit alignment mechanism for contextual entropy modeling. By treating the hyperprior as a surrogate for the contextual latent representation, this mechanism implicitly mitigates the misalignment in the fused bi-directional temporal priors. Extensive experiments demonstrate that our proposed codec achieves an average BD-rate reduction of approximately 10% compared to the state-of-the-art neural B-frame codec, DCVC-B, and delivers comparable or even superior compression performance to the H.266/VVC reference software under random-access configurations.

eess.IV↗

arXiv2025

Adaptive Subarray Segmentation: A New Paradigm of Spatial Non-Stationary Near-Field Channel Estimation for XL-MIMO Systems

To address the complexities of spatial non-stationary (SnS) effects and spherical wave propagation in near-field channel estimation (CE) for extremely large-scale multiple-input multiple-output (XL-MIMO) systems, this paper proposes an SnS-aware CE framework based on adaptive subarray partitioning. We first investigate spherical wave propagation and various SnS characteristics and construct an SnS near-field channel model for XL-MIMO systems. Due to the limitations of uniform array partitioning in capturing SnS, we analyze the adverse effects of the non-ideal array segmentation (over- and under-segmentation) on CE accuracy. To counter these issues, we develop a dynamic hybrid beamforming-assisted power-based subarray segmentation paradigm (DHBF-PSSP), which integrates power measurements with a dynamic hybrid beamforming structure to enable joint subarray partitioning and decoupling. A power-adaptive subarray segmentation (PASS) algorithm leverages the statistical properties of power profiles, while subarray decoupling is achieved via a subarray segmentation-based sampling method (SS-SM) under radio frequency (RF) chain constraints. For subarray CE, we propose a subarray segmentation-based assorted block sparse Bayesian learning algorithm under the multiple measurement vectors framework (SS-ABSBL-MMV). This algorithm exploits angular-domain block sparsity under a discrete Fourier transform (DFT) codebook and inter-subcarrier structured sparsity. Simulation results confirm that the proposed framework outperforms existing methods in CE performance.

eess.SP↗

arXiv2025

RoSe: Robust Self-supervised Stereo Matching under Adverse Weather Conditions

Recent self-supervised stereo matching methods have made significant progress, but their performance significantly degrades under adverse weather conditions such as night, rain, and fog. We identify two primary weaknesses contributing to this performance degradation. First, adverse weather introduces noise and reduces visibility, making CNN-based feature extractors struggle with degraded regions like reflective and textureless areas. Second, these degraded regions can disrupt accurate pixel correspondences, leading to ineffective supervision based on the photometric consistency assumption. To address these challenges, we propose injecting robust priors derived from the visual foundation model into the CNN-based feature extractor to improve feature representation under adverse weather conditions. We then introduce scene correspondence priors to construct robust supervisory signals rather than relying solely on the photometric consistency assumption. Specifically, we create synthetic stereo datasets with realistic weather degradations. These datasets feature clear and adverse image pairs that maintain the same semantic context and disparity, preserving the scene correspondence property. With this knowledge, we propose a robust self-supervised training paradigm, consisting of two key steps: robust self-supervised scene correspondence learning and adverse weather distillation. Both steps aim to align underlying scene results from clean and adverse image pairs, thus improving model disparity estimation under adverse weather effects. Extensive experiments demonstrate the effectiveness and versatility of our proposed solution, which outperforms existing state-of-the-art self-supervised methods. Codes are available at \textcolor{blue}{https://github.com/cocowy1/RoSe-Robust-Self-supervised-Stereo-Matching-under-Adverse-Weather-Conditions}.

cs.CV↗

arXiv2025

Beamforming-based Achievable Rate Maximization in ISAC System for Multi-UAV Networking

Airborne mobile Integrated Sensing and Communication (ISAC) base stations have garnered significant attention recently, with ISAC technology being a crucial application for 6G networks. Since ISAC can sense potential mobile communication users, this paper studies an effective scheme for a multi-UAV network tailored for emergency communication. In this paper, we develop a temporal-assisted frame structure utilizing integrated omnidirectional and directional beampattern to facilitate efficient and frequent searching, with extended Kalman filtering (EKF) as an aid to beam alignment. Further, we address an optimization problem to maximize the total achievable rate per slot by jointly designing UAV beamforming, load management, and UAV direction planning, all while adhering to the constraints of the predicted beam coverage. Given the problem NP-hard, we introduce three robust mechanisms for its resolution: an enhanced distributed Successive Convex Approximation (SCA)-Iterative Rank Minimization (IRM) algorithm, an coalition game approach, and a Fermat point search method. In particular, the proposed SCA-IRM algorithm decomposes the original complex optimization problem into several sub-problems and assigns them equally to each UAV, so as to realize distributed computing and improve computational efficiency. Our proposed simulations demonstrate the improved system performance in terms of communication rate, fairness, and sensing accuracy, providing design guidelines of UAV-assisted emergency communication networking.

cs.PF↗

arXiv2025

Survey on Near-Space Information Networks: Channel Modeling, Transmission, and Networking Perspectives

Near-space information networks (NSINs) composed of high-altitude platforms (HAPs) and high- and low-altitude unmanned aerial vehicles (UAVs) are a new regime for providing quick, robust, and cost-efficient sensing and communication services. Precipitated by innovations and breakthroughs in manufacturing, materials, communications, electronics, and control techniques, NSINs have been envisioned as an essential component of the emerging sixth-generation of mobile communication systems. This article reveals some critical issues needing to be tackled in NSINs through conducting experiments and discusses the latest advances in NSINs in the research areas of channel modeling, networking, and transmission from a forward-looking, comparative, and technical evolutionary perspective. In this article, we highlight the characteristics of NSINs and present the promising use cases of NSINs. The impact of airborne platforms' unstable movements on the phase delays of onboard antenna arrays with diverse structures is mathematically analyzed. The recent advances in HAP channel modeling are elaborated on, along with the significant differences between HAP and UAV channel modeling. A comprehensive review of the networking techniques of NSINs in network deployment, handoff management, and network management aspects is provided. Besides, the promising techniques and communication protocols of the physical (PHY) layer, medium access control (MAC) layer, network layer, and transport layer of NSINs for achieving efficient transmission over NSINs are reviewed, and we have conducted experiments with practical NSINs to verify the performance of some techniques. Finally, we outline some open issues and promising directions for NSINs deserved for future study and discuss the corresponding challenges.

cs.NI↗

arXiv2025

SIMAC: A Semantic-Driven Integrated Multimodal Sensing And Communication Framework

Traditional single-modality sensing faces limitations in accuracy and capability, and its decoupled implementation with communication systems increases latency in bandwidth-constrained environments. Additionally, single-task-oriented sensing systems fail to address users' diverse demands. To overcome these challenges, we propose a semantic-driven integrated multimodal sensing and communication (SIMAC) framework. This framework leverages a joint source-channel coding architecture to achieve simultaneous sensing decoding and transmission of sensing results. Specifically, SIMAC first introduces a multimodal semantic fusion (MSF) network, which employs two extractors to extract semantic information from radar signals and images, respectively. MSF then applies cross-attention mechanisms to fuse these unimodal features and generate multimodal semantic representations. Secondly, we present a large language model (LLM)-based semantic encoder (LSE), where relevant communication parameters and multimodal semantics are mapped into a unified latent space and input to the LLM, enabling channel-adaptive semantic encoding. Thirdly, a task-oriented sensing semantic decoder (SSD) is proposed, in which different decoded heads are designed according to the specific needs of tasks. Simultaneously, a multi-task learning strategy is introduced to train the SIMAC framework, achieving diverse sensing services. Finally, experimental simulations demonstrate that the proposed framework achieves diverse sensing services and higher accuracy.

cs.LG↗

arXiv2025

From Dense to Sparse: Event Response for Enhanced Residential Load Forecasting

Residential load forecasting (RLF) is crucial for resource scheduling in power systems. Most existing methods utilize all given load records (dense data) to indiscriminately extract the dependencies between historical and future time series. However, there exist important regular patterns residing in the event-related associations among different appliances (sparse knowledge), which have yet been ignored. In this paper, we propose an Event-Response Knowledge Guided approach (ERKG) for RLF by incorporating the estimation of electricity usage events for different appliances, mining event-related sparse knowledge from the load series. With ERKG, the event-response estimation enables portraying the electricity consumption behaviors of residents, revealing regular variations in appliance operational states. To be specific, ERKG consists of knowledge extraction and guidance: i) a forecasting model is designed for the electricity usage events by estimating appliance operational states, aiming to extract the event-related sparse knowledge; ii) a novel knowledge-guided mechanism is established by fusing such state estimates of the appliance events into the RLF model, which can give particular focuses on the patterns of users' electricity consumption behaviors. Notably, ERKG can flexibly serve as a plug-in module to boost the capability of existing forecasting models by leveraging event response. In numerical experiments, extensive comparisons and ablation studies have verified the effectiveness of our ERKG, e.g., over 8% MAE can be reduced on the tested state-of-the-art forecasting models.

cs.LG↗

arXiv2024

Joint Source-Channel Optimization for UAV Video Coding and Transmission

This paper is concerned with unmanned aerial vehicle (UAV) video coding and transmission in scenarios such as emergency rescue and environmental monitoring. Unlike existing methods of modeling UAV video source coding and channel transmission separately, we investigate the joint source-channel optimization issue for video coding and transmission. Particularly, we design eight-dimensional delay-power-rate-distortion models in terms of source coding and channel transmission and characterize the correlation between video coding and transmission, with which a joint source-channel optimization problem is formulated. Its objective is to minimize end-to-end distortion and UAV power consumption by optimizing fine-grained parameters related to UAV video coding and transmission. This problem is confirmed to be a challenging sequential-decision and non-convex optimization problem. We therefore decompose it into a family of repeated optimization problems by Lyapunov optimization and design an approximate convex optimization scheme with provable performance guarantees to tackle these problems. Based on the theoretical transformation, we propose a Lyapunov repeated iteration (LyaRI) algorithm. Both objective and subjective experiments are conducted to comprehensively evaluate the performance of LyaRI. The results indicate that, compared to its counterparts, LyaRI achieves better video quality and stability performance, with a 47.74% reduction in the variance of the obtained encoding bitrate.

eess.SP↗

arXiv2024

Connection Performance Modeling and Analysis of a Radiosonde Network in a Typhoon

This paper is concerned with the theoretical modeling and analysis of uplink connection performance of a radiosonde network deployed in a typhoon. Similar to existing works, the stochastic geometry theory is leveraged to derive the expression of the uplink connection probability (CP) of a radiosonde. Nevertheless, existing works assume that network nodes are spherically or uniformly distributed. Different from the existing works, this paper investigates two particular motion patterns of radiosondes in a typhoon, which significantly challenges the theoretical analysis. According to their particular motion patterns, this paper first separately models the distributions of horizontal and vertical distances from a radiosonde to its receiver. Secondly, this paper derives the closed-form expressions of cumulative distribution function (CDF) and probability density function (PDF) of a radiosonde's three-dimensional (3D) propagation distance to its receiver. Thirdly, this paper derives the analytical expression of the uplink CP for any radiosonde in the network. Finally, extensive numerical simulations are conducted to validate the theoretical analysis, and the influence of various network design parameters are comprehensively discussed. Simulation results show that when the signal-to-interference-noise ratio (SINR) threshold is below -35 dB, and the density of radiosondes remains under 0.01/km^3, the uplink CP approaches 26%, 39%, and 50% in three patterns.

cs.NI↗

arXiv2024

Towards Optimal Customized Architecture for Heterogeneous Federated Learning with Contrastive Cloud-Edge Model Decoupling

Federated learning, as a promising distributed learning paradigm, enables collaborative training of a global model across multiple network edge clients without the need for central data collecting. However, the heterogeneity of edge data distribution drags the model towards the local minima, which can be distant from the global optimum. Such heterogeneity often leads to slow convergence and substantial communication overhead. To address these issues, we propose a novel federated learning framework called FedCMD, a model decoupling tailored to the Cloud-edge supported federated learning that separates deep neural networks into a body for capturing shared representations in Cloud and a personalized head for migrating data heterogeneity. Our motivation is that, by the deep investigation of the performance of selecting different neural network layers as the personalized head, we found rigidly assigning the last layer as the personalized head in current studies is not always optimal. Instead, it is necessary to dynamically select the personalized layer that maximizes the training performance by taking the representation difference between neighbor layers into account. To find the optimal personalized layer, we utilize the low-dimensional representation of each layer to contrast feature distribution transfer and introduce a Wasserstein-based layer selection method, aimed at identifying the best-match layer for personalization. Additionally, a weighted global aggregation algorithm is proposed based on the selected personalized layer for the practical application of FedCMD. Extensive experiments on ten benchmarks demonstrate the efficiency and superior performance of our solution compared with nine state-of-the-art solutions. All code and results are available at https://github.com/elegy112138/FedCMD.

cs.LG↗

arXiv2024

The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective

Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge. By shifting the load of cloud computing to individual local servers, MEC helps meet the requirements of ultralow latency, localized data processing, and extends the potential of Internet of Things (IoT) for end-users. However, the crosscutting nature of MEC and the multidisciplinary components necessary for its deployment have presented additional security and privacy concerns. Fortunately, Artificial Intelligence (AI) algorithms can cope with excessively unpredictable and complex data, which offers a distinct advantage in dealing with sophisticated and developing adversaries in the security industry. Hence, in this paper we comprehensively provide a survey of security and privacy in MEC from the perspective of AI. On the one hand, we use European Telecommunications Standards Institute (ETSI) MEC reference architecture as our based framework while merging the Software Defined Network (SDN) and Network Function Virtualization (NFV) to better illustrate a serviceable platform of MEC. On the other hand, we focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI. Finally, we comprehensively discuss the opportunities and challenges associated with applying AI to MEC security and privacy as possible future research directions.

cs.CR↗

arXiv2023

Cross-modal Orthogonal High-rank Augmentation for RGB-Event Transformer-trackers

This paper addresses the problem of cross-modal object tracking from RGB videos and event data. Rather than constructing a complex cross-modal fusion network, we explore the great potential of a pre-trained vision Transformer (ViT). Particularly, we delicately investigate plug-and-play training augmentations that encourage the ViT to bridge the vast distribution gap between the two modalities, enabling comprehensive cross-modal information interaction and thus enhancing its ability. Specifically, we propose a mask modeling strategy that randomly masks a specific modality of some tokens to enforce the interaction between tokens from different modalities interacting proactively. To mitigate network oscillations resulting from the masking strategy and further amplify its positive effect, we then theoretically propose an orthogonal high-rank loss to regularize the attention matrix. Extensive experiments demonstrate that our plug-and-play training augmentation techniques can significantly boost state-of-the-art one-stream and twostream trackers to a large extent in terms of both tracking precision and success rate. Our new perspective and findings will potentially bring insights to the field of leveraging powerful pre-trained ViTs to model cross-modal data. The code will be publicly available.

cs.CV↗

arXiv2023

QoE-Driven Video Transmission: Energy-Efficient Multi-UAV Network Optimization

This paper is concerned with the issue of improving video subscribers' quality of experience (QoE) by deploying a multi-unmanned aerial vehicle (UAV) network. Different from existing works, we characterize subscribers' QoE by video bitrates, latency, and frame freezing and propose to improve their QoE by energy-efficiently and dynamically optimizing the multi-UAV network in terms of serving UAV selection, UAV trajectory, and UAV transmit power. The dynamic multi-UAV network optimization problem is formulated as a challenging sequential-decision problem with the goal of maximizing subscribers' QoE while minimizing the total network power consumption, subject to some physical resource constraints. We propose a novel network optimization algorithm to solve this challenging problem, in which a Lyapunov technique is first explored to decompose the sequential-decision problem into several repeatedly optimized sub-problems to avoid the curse of dimensionality. To solve the sub-problems, iterative and approximate optimization mechanisms with provable performance guarantees are then developed. Finally, we design extensive simulations to verify the effectiveness of the proposed algorithm. Simulation results show that the proposed algorithm can effectively improve the QoE of subscribers and is 66.75\% more energy-efficient than benchmarks.

cs.NI↗