arXiv subjects

Yuwen Qian

Publications and source records attributed to Yuwen Qian.

16 recordsLinked to original sources

arXiv2026

Covert Semantic Transmission in ISAC: Dual-Functional Waveform Design and Rectified Flow-Assisted Recovery

Semantic integrated sensing and communication (ISAC) is envisioned as a promising paradigm for efficient and intelligent connectivity in future wireless networks. However, the open wireless channel exposes the dual-functional waveform to detection, which challenges the joint guarantee of covertness, sensing fidelity, and semantic accuracy. To address the challenge, we propose CoSMIC, a novel covertness-oriented semantic ISAC framework, where the sensing output is embedded into a dual-functional ISAC waveform through semantic modulation. Specifically, a semantic rotation coding scheme is established to map semantic latents onto the pairwise rotation and scaling of Gaussian reference sequences, which satisfies a derived closed-form covertness constraint by a differentiable budget projection. Moreover, the radar performance is analyzed to confirm an invariant matched-filter mainlobe response and a bounded output signal-to-interference-plus-noise ratio (SINR) under the semantic embedding. Subsequently, a reliability-guided rectified flow (RFlow) refiner is designed to effectively reconstruct high-fidelity semantic representations from coarse observations. Simulation results demonstrate that CoSMIC improves the semantic reconstruction quality by 18% over diffusion-based baseline schemes with substantially reduced inference latency under strict covertness constraints, which validates the applicability to practical ISAC scenarios. The source code and video demonstrations are available at https://github.com/LanceAnlan/CoSMIC-covertness-oriented-semantic-ISAC-framework.

eess.SP↗

arXiv2026

A Covert Precision Satellite Communication Framework Assisted by Cooperative IRSs

Satellite communication (SatCom), as an effective complement to terrestrial networks, has attracted considerable attention from both academia and industry owing to its wide coverage and high flexibility. However, the inherent openness of satellite links renders them highly vulnerable to eavesdropping, thereby posing significant security challenges. In this paper, we propose a satellite covert precision wireless communication (CPWC) system, where multiple intelligent reflecting surfaces (IRSs) cooperate to assist satellite transmissions, ensuring that confidential information is delivered to legitimate users while remaining undetectable to wardens. To further enhance covertness, an orthogonal frequency division multiplexing (OFDM)-based random subcarrier selection (RSCS) method is developed to concentrate the signal energy at the intended receiver. Under a practical satellite-terrestrial channel model, we derive closed-form covertness constraints for the CPWC system based on relative entropy and detection error probability. Under the relative-entropy constraint and the satellite power constraint, we maximize the covert rate by an alternating-optimization (AO) based semidefinite relaxation (SDR) iterative algorithm and obtain a high-quality feasible solution. Using this solution as a warm start, we further impose the detection-error-probability constraint and refine the beamformer through a sequential quadratic programming (SQP) based algorithm. Numerical results demonstrate the effectiveness of the proposed CPWC system, where the detection-error-probability-based scheme outperforms the second-order cone programming (SOCP) benchmark, the random-phase-shift design, the one-bit IRS quantized scheme, and the SDR baseline without precise communication (PC) in terms of covert rate.

eess.SP↗

arXiv2024

Refine, Discriminate and Align: Stealing Encoders via Sample-Wise Prototypes and Multi-Relational Extraction

This paper introduces RDA, a pioneering approach designed to address two primary deficiencies prevalent in previous endeavors aiming at stealing pre-trained encoders: (1) suboptimal performances attributed to biased optimization objectives, and (2) elevated query costs stemming from the end-to-end paradigm that necessitates querying the target encoder every epoch. Specifically, we initially Refine the representations of the target encoder for each training sample, thereby establishing a less biased optimization objective before the steal-training phase. This is accomplished via a sample-wise prototype, which consolidates the target encoder's representations for a given sample's various perspectives. Demanding exponentially fewer queries compared to the end-to-end approach, prototypes can be instantiated to guide subsequent query-free training. For more potent efficacy, we develop a multi-relational extraction loss that trains the surrogate encoder to Discriminate mismatched embedding-prototype pairs while Aligning those matched ones in terms of both amplitude and angle. In this way, the trained surrogate encoder achieves state-of-the-art results across the board in various downstream datasets with limited queries. Moreover, RDA is shown to be robust to multiple widely-used defenses.

cs.LG↗

arXiv2024

EmInspector: Combating Backdoor Attacks in Federated Self-Supervised Learning Through Embedding Inspection

Federated self-supervised learning (FSSL) has recently emerged as a promising paradigm that enables the exploitation of clients' vast amounts of unlabeled data while preserving data privacy. While FSSL offers advantages, its susceptibility to backdoor attacks, a concern identified in traditional federated supervised learning (FSL), has not been investigated. To fill the research gap, we undertake a comprehensive investigation into a backdoor attack paradigm, where unscrupulous clients conspire to manipulate the global model, revealing the vulnerability of FSSL to such attacks. In FSL, backdoor attacks typically build a direct association between the backdoor trigger and the target label. In contrast, in FSSL, backdoor attacks aim to alter the global model's representation for images containing the attacker's specified trigger pattern in favor of the attacker's intended target class, which is less straightforward. In this sense, we demonstrate that existing defenses are insufficient to mitigate the investigated backdoor attacks in FSSL, thus finding an effective defense mechanism is urgent. To tackle this issue, we dive into the fundamental mechanism of backdoor attacks on FSSL, proposing the Embedding Inspector (EmInspector) that detects malicious clients by inspecting the embedding space of local models. In particular, EmInspector assesses the similarity of embeddings from different local models using a small set of inspection images (e.g., ten images of CIFAR100) without specific requirements on sample distribution or labels. We discover that embeddings from backdoored models tend to cluster together in the embedding space for a given inspection image. Evaluation results show that EmInspector can effectively mitigate backdoor attacks on FSSL across various adversary settings. Our code is avaliable at https://github.com/ShuchiWu/EmInspector.

cs.CR↗

arXiv2024

Design of Two-Level Incentive Mechanisms for Hierarchical Federated Learning

Hierarchical Federated Learning (HFL) is a distributed machine learning paradigm tailored for multi-tiered computation architectures, which supports massive access of devices' models simultaneously. To enable efficient HFL, it is crucial to design suitable incentive mechanisms to ensure that devices actively participate in local training. However, there are few studies on incentive mechanism design for HFL. In this paper, we design two-level incentive mechanisms for the HFL with a two-tiered computing structure to encourage the participation of entities in each tier in the HFL training. In the lower-level game, we propose a coalition formation game to joint optimize the edge association and bandwidth allocation problem, and obtain efficient coalition partitions by the proposed preference rule, which can be proven to be stable by exact potential game. In the upper-level game, we design the Stackelberg game algorithm, which not only determines the optimal number of edge aggregations for edge servers to maximize their utility, but also optimize the unit reward provided for the edge aggregation performance to ensure the interests of cloud servers. Furthermore, numerical results indicate that the proposed algorithms can achieve better performance than the benchmark schemes.

cs.GT↗

arXiv2023

Sparse Federated Training of Object Detection in the Internet of Vehicles

As an essential component part of the Intelligent Transportation System (ITS), the Internet of Vehicles (IoV) plays a vital role in alleviating traffic issues. Object detection is one of the key technologies in the IoV, which has been widely used to provide traffic management services by analyzing timely and sensitive vehicle-related information. However, the current object detection methods are mostly based on centralized deep training, that is, the sensitive data obtained by edge devices need to be uploaded to the server, which raises privacy concerns. To mitigate such privacy leakage, we first propose a federated learning-based framework, where well-trained local models are shared in the central server. However, since edge devices usually have limited computing power, plus a strict requirement of low latency in IoVs, we further propose a sparse training process on edge devices, which can effectively lighten the model, and ensure its training efficiency on edge devices, thereby reducing communication overheads. In addition, due to the diverse computing capabilities and dynamic environment, different sparsity rates are applied to edge devices. To further guarantee the performance, we propose, FedWeg, an improved aggregation scheme based on FedAvg, which is designed by the inverse ratio of sparsity rates. Experiments on the real-life dataset using YOLO show that the proposed scheme can achieve the required object detection rate while saving considerable communication costs.

cs.LG↗

arXiv2023

Personalized Federated Deep Reinforcement Learning-based Trajectory Optimization for Multi-UAV Assisted Edge Computing

In the era of 5G mobile communication, there has been a significant surge in research focused on unmanned aerial vehicles (UAVs) and mobile edge computing technology. UAVs can serve as intelligent servers in edge computing environments, optimizing their flight trajectories to maximize communication system throughput. Deep reinforcement learning (DRL)-based trajectory optimization algorithms may suffer from poor training performance due to intricate terrain features and inadequate training data. To overcome this limitation, some studies have proposed leveraging federated learning (FL) to mitigate the data isolation problem and expedite convergence. Nevertheless, the efficacy of global FL models can be negatively impacted by the high heterogeneity of local data, which could potentially impede the training process and even compromise the performance of local agents. This work proposes a novel solution to address these challenges, namely personalized federated deep reinforcement learning (PF-DRL), for multi-UAV trajectory optimization. PF-DRL aims to develop individualized models for each agent to address the data scarcity issue and mitigate the negative impact of data heterogeneity. Simulation results demonstrate that the proposed algorithm achieves superior training performance with faster convergence rates, and improves service quality compared to other DRL-based approaches.

cs.MA↗

arXiv2023

Effective Community Search on Large Attributed Bipartite Graphs

Community search over bipartite graphs has attracted significant interest recently. In many applications such as user-item bipartite graph in E-commerce, customer-movie bipartite graph in movie rating website, nodes tend to have attributes, while previous community search algorithm on bipartite graphs ignore attributes, which makes the returned results with poor cohesion with respect to their node attributes. In this paper, we study the community search problem on attributed bipartite graphs. Given a query vertex q, we aim to find attributed $\left(α,β\right)$-communities of $G$, where the structure cohesiveness of the community is described by an $\left(α,β\right)$-core model, and the attribute similarity of two groups of nodes in the subgraph is maximized. In order to retrieve attributed communities from bipartite graphs, we first propose a basic algorithm composed of two steps: the generation and verification of candidate keyword sets, and then two improved query algorithms Inc and Dec are proposed. Inc is proposed considering the anti-monotonity property of attributed bipartite graphs, then we adopt different generating method and verifying order of candidate keyword sets and propose the Dec algorithm. After evaluating our solutions on eight large graphs, the experimental results demonstrate that our methods are effective and efficient in querying the attributed communities on bipartite graphs.

cs.SI↗

arXiv2023

Efficient and Low Overhead Website Fingerprinting Attacks and Defenses based on TCP/IP Traffic

Website fingerprinting attack is an extensively studied technique used in a web browser to analyze traffic patterns and thus infer confidential information about users. Several website fingerprinting attacks based on machine learning and deep learning tend to use the most typical features to achieve a satisfactory performance of attacking rate. However, these attacks suffer from several practical implementation factors, such as a skillfully pre-processing step or a clean dataset. To defend against such attacks, random packet defense (RPD) with a high cost of excessive network overhead is usually applied. In this work, we first propose a practical filter-assisted attack against RPD, which can filter out the injected noises using the statistical characteristics of TCP/IP traffic. Then, we propose a list-assisted defensive mechanism to defend the proposed attack method. To achieve a configurable trade-off between the defense and the network overhead, we further improve the list-based defense by a traffic splitting mechanism, which can combat the mentioned attacks as well as save a considerable amount of network overhead. In the experiments, we collect real-life traffic patterns using three mainstream browsers, i.e., Microsoft Edge, Google Chrome, and Mozilla Firefox, and extensive results conducted on the closed and open-world datasets show the effectiveness of the proposed algorithms in terms of defense accuracy and network efficiency.

cs.CR↗

arXiv2022

Path Planning for the Dynamic UAV-Aided Wireless Systems using Monte Carlo Tree Search

For UAV-aided wireless systems, online path planning attracts much attention recently. To better adapt to the real-time dynamic environment, we, for the first time, propose a Monte Carlo Tree Search (MCTS)-based path planning scheme. In details, we consider a single UAV acts as a mobile server to provide computation tasks offloading services for a set of mobile users on the ground, where the movement of ground users follows a Random Way Point model. Our model aims at maximizing the average throughput under energy consumption and user fairness constraints, and the proposed timesaving MCTS algorithm can further improve the performance. Simulation results show that the proposed algorithm achieves a larger average throughput and a faster convergence performance compared with the baseline algorithms of Q-learning and Deep Q-Network.

cs.NI↗

arXiv2021

Impact of Low-Resolution ADC on DOA Estimation Performance for Massive MIMO Receive Array

In this paper, we present a new scenario of direction of arrival (DOA) estimation using massive multiple-input multiple-output (MIMO) receive array with low-resolution analog-to-digital convertors (ADCs), which can strike a good balance between performance and circuit cost. Based on the linear additive quantization noise model (AQNM), the effect of low-resolution ADCs on the methods, such as Root-MUSIC method, is analyzed. Also, the closed-form expression of Cramer-Rao lower bound (CRLB) is derived to evaluate the performance loss caused by the low-resolution ADCs. The simulation results show that the Root-MUSIC methods can achieve the corresponding CRLB. Furthermore, 2-3 bits are acceptable for most applications if the 1dB performance loss.

cs.IT↗

arXiv2021

Federated Learning Over Wireless Channels: Dynamic Resource Allocation and Task Scheduling

With the development of federated learning (FL), mobile devices (MDs) are able to train their local models with private data and sends them to a central server for aggregation, thereby preventing sensitive raw data leakage. In this paper, we aim to improve the training performance of FL systems in the context of wireless channels and stochastic energy arrivals of MDs. To this purpose, we dynamically optimize MDs' transmission power and training task scheduling. We first model this dynamic programming problem as a constrained Markov decision process (CMDP). Due to high dimensions rooted from our CMDP problem, we propose online stochastic learning methods to simplify the CMDP and design online algorithms to obtain an efficient policy for all MDs. Since there are long-term constraints in our CMDP, we utilize Lagrange multipliers approach to tackle this issue. Furthermore, we prove the convergence of the proposed online stochastic learning algorithm. Numerical results indicate that the proposed algorithms can achieve better performance than the benchmark algorithms.

cs.DC↗

arXiv2020

UAV-enabled Secure Communication with Finite Blocklength

In the finite blocklength scenario, which is suitable for practical applications, a method of maximizing the average effective secrecy rate (AESR) is proposed for a UAV-enabled secure communication by optimizing the UAV's trajectory and transmit power subject to the UAV's mobility constraints and transmit power constraints. To address the formulated non-convex optimization problem, it is first decomposed into two non-convex subproblems. Then the two subproblems are converted respectively into two convex subproblems via the first-order approximation. Finally, an alternating iteration algorithm is developed by solving the two subproblems iteratively using successive convex approximation (SCA) technique. Numerical results show that our proposed scheme achieves a better AESR performance than both the benchmark schemes.

eess.SP↗

arXiv2019

User Association and Path Planning for UAV-Aided Mobile Edge Computing with Energy Restriction

Mobile edge computing (MEC) provides computational services at the edge of networks by offloading tasks from user equipments (UEs). This letter employs an unmanned aerial vehicle (UAV) as the edge computing server to execute offloaded tasks from the ground UEs. We jointly optimize user association, UAV trajectory, and uploading power of each UE to maximize sum bits offloaded from all UEs to the UAV, subject to energy constraint of the UAV and quality of service (QoS) of each UE. To address the non-convex optimization problem, we first decompose it into three subproblems that are solved with integer programming and successive convex optimization methods respectively. Then, we tackle the overall problem by the multi-variable iterative optimization algorithm. Simulations show that the proposed algorithm can achieve a better performance than other baseline schemes.

cs.IT↗

arXiv2018

An Efficient Hybrid Beamforming Design for Massive MIMO Receive Systems via SINR Maximization Based on an Improved Bat Algorithm

Hybrid analog and digital (HAD) beamforming has been recently receiving considerable deserved attention for a practical implementation on the large-scale antenna systems. As compared to full digital beamforming, partial-connected HAD beamforming can significantly reduce the hardware cost, complexity, and power consumption. In this paper, in order to mitigate the jamming along with lowering the hardware complexity and cost by reducing the number of RF chains needed, a novel hybrid analog and digital receive beamformer based on an improved bat algorithm (I-BA) and the phase-only is proposed. Our proposed beamformer is compared with robust adaptive beamformers (RABs) methods proposed by us, which are considered in the digital beamforming part. The evolutionary optimization algorithm is proposed since most of the RAB methods are sensitive to the DOA mismatch, and depending on the complex weights, resulting in an expensive receiver. In the analog part, analog phase alignment by linear searching (APALS) with a sufficiently fine grid of points is employed to optimize the analog beamformer matrix. The performance of the proposed I-BA is revealed using MATLAB simulation and compared with BA, and Particle swarm optimization (PSO) algorithms, which shows a better performance in terms of convergence speed, stability, and the ability to jump from the local minima.

cs.IT↗

arXiv2018

Power Allocation Strategies for Secure Spatial Modulation

In secure spatial modulation (SM) networks, power allocation (PA) strategies are investigated in this paper under the total power constraint. Considering that there is no closed-form expression for secrecy rate (SR), an approximate closed-form expression of SR is presented, which is used as an efficient metric to optimize PA factor and can greatly reduce the computation complexity. Based on this expression, a convex optimization (CO) method of maximizing SR (Max-SR) is proposed accordingly. Furthermore, a method of maximizing the product of signal-to-leakage and noise ratio (SLNR) and artificial noise-to-leakage-and noise ratio (ANLNR) (Max-P-SAN) is proposed to provide an analytic solution to PA with extremely low-complexity. Simulation results demonstrate that the SR performance of the proposed CO method is close to that of the optimal PA strategy of Max-SR with exhaustive search and better than that of Max-P-SAN in the high signal-to-noise ratio (SNR) region. However, in the low and medium SNR regions, the SR performance of the proposed Max-P-SAN slightly exceeds that of the proposed CO.

eess.SP↗