mmWave radar-based human pose estimation is garnering increasing attention in Internet of Things (IoT) applications owing to its robustness under adverse lighting conditions and occlusion scenarios. However, accurately estimating human poses from mmWave signals remains challenging due to two key limitations: the lack of structural consistency when perceiving targets as a unified whole, and the absence of spatiotemporal consistency when tracking individual targets. To tackle these two challenges, this paper introduces RF-PoseR, a dedicated human pose rectifier for mmWave radar-based pose estimation. RF-PoseR serves as a corrective framework that refines the pose outputs generated by mmWave pose estimation systems. Based on the observation that perceptual inconsistencies in radar signals result in severely erroneous joint estimations, we introduce spatial and temporal constraints to enhance pose estimation accuracy. Specifically, RF-PoseR incorporates three core components: (1) unreliable joint removal guided by spatial structure, (2) pose completion leveraging temporal continuity, and (3) cross-modal pre-training utilizing large-scale visual pose datasets. Extensive experiments on mmWave radar pose datasets demonstrate that RF-PoseR significantly enhances the accuracy of poses generated by existing radar-based estimation networks. Furthermore, experiments on visual datasets confirm the method’s broader applicability beyond radar perception tasks.

WiFi indoor localization plays an important role in many real-world applications and has gained widespread attentions from both academia and industry over the past decade. While existing works have already achieved remarkable performance under various practical scenarios, they solely utilize multiple WiFi Access Points (APs) to achieve a higher accuracy and do not deeply explore the intrinsic relationships among them. To further unleash the potential of multiple APs and reach the limits of WiFi indoor localization, in this paper, we propose Qidi, a novel multi-APs collaboration based localization system. To the best of our knowledge, we are the first to explicitly reveal three underlying relationships among multiple spatially distributed APs, i.e., consistency, continuity, and non-uniformity. By combining these three basic principles with the unique characteristics of specific tasks, a series of long-lasting practical challenges, such as automatic phase offset calibration, bilateral angle ambiguity, elevation angle estimation with Uniform Linear Array (ULA) and Non-Line-of-Sight (NLoS), can be efficiently resolved. Extensive experiments in various complex environments are provided to demonstrate that Qidi can achieve 2.4°, 3.2° median errors of joint azimuth and elevation angle estimation, and 0.4mlocalization median error even in 20m×20mexhibition hall. Moreover, a one-month longitudinal evaluation conducted on a real-world deployed WiFi ISAC system further validates the effectiveness and robustness of the proposed localization system.

Cardiovascular diseases dominate global mortality, with premature ventricular contractions (PVCs) among the most prevalent arrhythmias. PVCs are paroxysmal and sporadic. If undetected, they may escalate to lethal arrhythmias or sudden cardiac death, underscoring the imperative for early and accurate diagnosis. Conventional monitoring via electrocardiogram (ECG) necessitates prolonged skin-electrode contact, provoking discomfort and potential allergic reactions, whereas photoplethysmography (PPG) is intrinsically susceptible to ambient light and motion artifacts. These limitations restrict both modalities to short-term or intermittent use. Radio-frequency (RF) sensing offers a contactless alternative for unobtrusive, long-term cardiac surveillance. The cardiac-induced mechanical displacements, however, are orders of magnitude smaller than respiratory and body-motion artifacts, rendering PVC-related signatures in RF echoes highly susceptible to noise and consequently difficult to extract and classify. To handle this, we propose a PVC diagnosis model based on RF signals, which constructs an auxiliary learning framework, thereby improving the accuracy of PVC diagnosis. To further enhance the effectiveness of the auxiliary framework, we designed a residual-assisted Mixture of Experts architecture, which effectively alleviates the gradient conflict between the auxiliary task and the main task. We conducted experiments on a large-scale dataset (7,015 subjects) in a hospital outpatient setting, where our system achieved the best performance, demonstrating the superiority of our approach.

In recent years, Channel State Information (CSI), recognized for its fine-grained spatial characteristics, has attracted increasing attention in WiFi-based indoor localization. However, despite its potential, CSI-based approaches have yet to achieve the same level of deployment scale and commercialization as those based on Received Signal Strength Indicator (RSSI). A key limitation lies in the fact that most existing CSI-based systems are developed and evaluated in controlled, small-scale environments, limiting their generalizability. To bridge this gap, we explore the deployment of a large-scale CSI-based localization system involving over 400 Access Points (APs) in a real-world building under the Integrated Sensing and Communication (ISAC) paradigm. We highlight two critical yet often overlooked factors: the underutilization of unlabeled data and the inherent heterogeneity of CSI measurements. To address these challenges, we propose a novel CSI-based learning framework for WiFi localization, tailored for large-scale ISAC deployments on the server side. Specifically, we employ a novel graph-based structure to model heterogeneous CSI data and reduce redundancy. We further design a pretext pretraining task that incorporates spatial and temporal priors to effectively leverage large-scale unlabeled CSI data. Complementarily, we introduce a confidence-aware fine-tuning strategy to enhance the robustness of localization results. In a leave-one-smartphone-out experiment spanning five floors and 25, 600m2, we achieve a median localization error of 2.17 meters and a floor accuracy of 99.49%. This performance corresponds to an 18.7% reduction in mean absolute error (MAE) compared to the best-performing baseline.

Physical adversarial attacks (PAAs) pose a serious security threat to millimeter-wave (mmWave) radar systems used in safety-critical applications such as autonomous driving and security checking. These attacks, manipulating radar signals via specially crafted materials, are proven feasible and can cause severe sensing failures; however, effective defenses remain unexplored due to the difficulty of distinguishing adversarial examples from normal environmental objects. This paper presents mmGuard, a physics-based defense framework that addresses this challenge by exploiting a fundamental insight: the engineering process that makes materials adversarial inevitably creates detectable physical signatures. We identify three key domains where adversarial examples show artificial nature: spatial phase discontinuities, anomalous radar cross-section patterns, and violations of natural physico-kinematic relationships. mmGuard systematically captures these signatures through multi-domain feature extraction, enhances their discriminability via neural refinement, and enables efficient per-object attack detection and mitigation compatible with automotive radar update rates. To enable evaluation, we introduce mmAD, comprising over 110,000 annotated radar frames with diverse adversarial examples across realistic deployment scenarios. Experimental results demonstrate that mmGuard achieves over 90% detection accuracy while exhibiting strong in-distribution performance, with few-shot adaptation enabling calibration to unseen settings Case studies further validate that mmGuard can reliably defend against PAAs in real-world settings.

As RF sensing increasingly moves toward real-world deployments, critical concerns emerge around unauthorized model usage and access control. Existing protection approaches-such as watermarking-are typically reactive, task-specific, and ineffective at runtime. This paper presents AuthRF (Authentication with passports for RF sensing models), a novel signal-level passport mechanism that proactively enforces access control by mapping a user-specific passport to phase-compensation weights in the signal processing pipeline. Valid passports yield coherent phase alignment and high-fidelity representations, while invalid or forged ones induce phase distortion that significantly degrades model performance. This design effectively deters unauthorized access, supports scalable multi-user authentication, and enables personalized service provisioning through controlled passport variation. We evaluate AuthRF on six representative RF sensing tasks using both WiFi and radar signals. Experimental results demonstrate its robust protection capabilities and seamless integration with existing sensing pipelines, positioning AuthRF as a practical foundation for secure and commercial-grade RF sensing deployment.

Radar sensing has emerged as a promising solution for the contactless monitoring of Heart Rate Variability (HRV), a crucial indicator of the cardiovascular and autonomic nervous systems. However, due to signal noise and interference that easily obscure heartbeat details, along with variations in heartbeat across different physiological conditions, existing methods remain restricted to laboratory settings with healthy subjects and fail in real-world scenarios involving more complex physiological conditions. In this study, we propose a physiological prior-inspired deep learning framework for robust radar-based HRV monitoring. Specifically, we leverage the prior that internal heartbeats drive movements across the entire torso surface and design a hybrid deep neural network to model the spatio-temporal relationship between full-body radio reflections and heartbeats, effectively mitigating interference. Then, we incorporate the cardiac motion's self-similarity prior to establish a signal augmentation strategy, effectively remodeling the HRV distribution and enhancing performance across diverse physiological conditions. We build and validate our method on a large-scale dataset comprising 7,150 outpatients with complex physiological conditions in real-world scenarios. The experimental results demonstrate that our method achieves a mean IBI error of 19.21 ms, an RMSSD error of 16.23 ms, an SDSD error of 16.70 ms, and a pNN50 error of 7.28%. We further validate the performance by classifying five common cardiac conditions based on HRV results, demonstrating performance comparable to ECG-based methods. These results highlight the great potential of our approach for accurate, contactless HRV monitoring in real-world applications.

Sleep staging, crucial for diagnosing sleep disorders, requires precise recognition of physiological signals within 30-second epochs, a task fundamentally different from managing long-term semantic dependencies in natural language processing (NLP). Our model aims to refine the integration of local and global features for more accurate sleep stage classification. Following the American Academy of Sleep Medicine (AASM) guidelines, it focuses on rigorous intra-epoch feature extraction to ensure reliable identification of sleep stages. Moreover, our approach incorporates a global perspective by analyzing whole-night data, which is essential for handling transitional periods and ambiguities. Existing sequential modeling techniques often overlook the unique requirements of sleep staging, leading to performance declines when epochs extend beyond approximately 200. Our model addresses this by structurally processing local and global information and carefully balancing detailed intra-epoch analysis with an overarching view of sleep cycles through a gating mechanism. This gate mechanism selectively integrates long-term dependencies, optimizing the balance between local accuracy and global context. This approach represents a significant advancement over existing models, offering more accurate, reliable, and clinically relevant sleep staging. Extensive experiments on the SHHS, SleepEDF-20, and SleepEDF-78 datasets demonstrate that our method outperforms state-of-the-art approaches.

Arrhythmias are prevalent cardiac disorders affecting millions worldwide. By analyzing cardiac motion modulated in mmWave reflections, mmWave sensing is emerging as a promising contactless revolution in arrhythmia detection compared to conventional contact-based methods. However, the fundamental bottleneck of existing mmWave sensing methods is their restriction to controlled laboratory settings with small-scale cohorts, limiting generalization to real-world populations. This limitation arises because mmWave signals undergo complex signal transformations during propagation, resulting in an explosion of signal diversity across large populations in real-world scenarios. Such diversity significantly complicates the direct recognition of arrhythmia. In this paper, we theoretically analyze the mechanism and impact of mmWave cardiac signal diversity. Leveraging the inherent transformation properties of mmWave signals, we propose a Diversity-Invariant Contrastive mmWave Sensing framework, which learns invariant features robust to complex signal transformations encountered in real-world scenarios. We evaluate our method in a practical, clinically-oriented scenario involving a large-scale population of 7,338 subjects, achieving an average F1-score of 0.8241 across four common arrhythmias. These results demonstrate that our method effectively bridges the diversity gap, representing a significant step toward practical clinical deployment of contactless arrhythmia detection via mmWave sensing.

Continuous blood pressure (BP) estimation remains a significant challenge in non-contact hemodynamic monitoring, where existing methods struggle to strike a balance between clinical accuracy and usability. We present Physio-BP, a physiologically-guided dual-radar system enabling calibration-free, continuous tracking of systolic (SBP) and diastolic blood pressure (DBP) by integrating spatially resolved vascular dynamics. Unlike conventional single-waveform radar approaches, Physio-BP synchronously captures mechanical signals from the heart and pulse waves from the carotid artery, allowing precise measurement of pulse arrival time (PAT) and inter-beat intervals (IBI). To enhance signal quality, we introduce a novel joint signal selection algorithm that optimizes feature extraction across dual radars, effectively addressing the inherent signal-to-noise ratio (SNR) disparities between the chest and neck monitoring sites. To validate our system, an extensive dataset exceeding 50 hours was collected under diverse conditions, including different seasons, times of day, body postures, and radar devices, ensuring comprehensive coverage. Experimental results on the dataset confirm that Physio-BP enables continuous and precise SBP and DBP tracking, a capability not achieved by current baseline methods, thereby advancing non-contact sensing for hemodynamic monitoring.

Millimeter-wave (mmWave) radar systems with large array have pushed radar sensing into a new era, thanks to their high angular resolution. However, our long-term experiments indicate that array elements exhibit phase drift over time and require periodic phase calibration to maintain high-resolution, creating an obstacle for practical high-resolution mmWave sensing with large array. Unfortunately, existing calibration methods are inadequate for periodic recalibration, either because they rely on artificial references or fail to provide sufficient precision. To address this challenge, we introduce AutoCalib, the first framework designed to automatically and accurately calibrate high-resolution mmWave radars by identifying Ambient Radio Anchors (ARAs)—naturally existing objects in ambient environments that offer stable phase references. AutoCalib achieves calibration by first generating spatial spectrum templates based on theoretical electromagnetic characteristics. It then employs a pattern-matching and scoring mechanism to accurately detect these anchors and select the optimal one for calibration. Extensive experiments across 11 environments demonstrate that AutoCalib is capable of identifying ARAs that existing methods miss due to their focus on strong reflectors. AutoCalib's calibration performance approaches corner reflectors (74% phase error reduction) while outperforming existing methods by 83%. Beyond radar calibration, AutoCalib effectively supports other phase-dependent applications like handheld imaging, delivering 96% of corner reflector calibration performance without artificial references.

The learning-based RF sensing methods typically involve signal processing to transform the RF signals into spectrograms, which are then input into neural networks. However, this approach is suboptimal due to the potential degradation caused by the inherent multi-path interference, resulting in blurred RF spectrograms. To tackle this challenge, we introduce a novelRefocusingTransformerbackbone customized forRFsensing (RF$^{2}$Transformer). Rather than attempting to precisely model interference using the traditional signal processing techniques, the RF$^{2}$Transformer utilizes a self-compensation mechanism to treat the interference-induced phase shifts as learnable parameters. This mechanism learns and compensates for the phase shift caused by interference in the complex feature space to obtain refocused and high-quality feature maps of RF spectrograms, thereby improving the performance of downstream tasks. We show that the RF$^{2}$Transformer is general for various RF sensing tasks by evaluating it on six typical RF sensing tasks using two general RF signals (WiFi and radar). Experimental results indicate that the RF$^{2}$Transformer takes an important step toward learning-based solutions for RF sensing.

Although radio frequency (RF) sensing offers a promising approach for monitoring human cardiac activity, it traditionally requires subjects to remain in a steady-state throughout the monitoring process to avoid motion artifacts—an inherently impractical constraint that hampers real-world adoption. However, existing methods either yield incorrect estimates from motion-affected segments or discard them entirely, leading to fragmented data and incomplete observations. To address the challenge, we introduce MaskSense, a novel framework designed to address motion interference in long-term monitoring. The key insight is that latent patterns within dynamic inter-beat interval (IBI) sequences allow for accurate heartbeat reconstruction from incomplete observations. Leveraging this insight, MaskSense treats motion-affected periods as ”masked” and steady-state periods as ”unmasked”, and employs a contrastive-learning-assisted masked modeling architecture to reconstruct the masked information. Our 400-hour evaluation with 18 participants confirms that MaskSense effectively recovers IBIs in the presence of motion artifacts, paving the way for more natural and unobtrusive RF-based cardiac activity monitoring.

The past few decades have seen significant advancements in WiFi indoor localization leveraging fine-grained Channel State Information (CSI). However, existing methods often require multiple access points (APs) or the target device to share sensing data. This cooperative localization complicates the deployment of practical localization systems. In this paper, we present SniFi, a non-cooperative localization method that seamlessly integrates with existing WiFi infrastructure. Unlike prior methods, SniFi does not require multiple CSI-capable APs or additional user actions. We use a single network interface card (NIC) to sniff WiFi traffic and obtain the angle information needed for localization. For APs that do not support CSI acquisition, we first utilize Beamforming Feedback Information (BFI) to estimate the Angle of Departure (AoD). Then, by analyzing the sniffed packets, we can obtain the CSI and the corresponding Angle of Arrival (AoA) from the target device to the sniffer. During experiments, we also address the challenge of angle ambiguity of the latest Intel AX210 NIC by integrating map constraints and temporal continuity. These techniques together enable us to achieve accurate localization without making changes to existing AP networks. Extensive evaluations across various environments and APs demonstrate that SniFi achieves decimeter-level accuracy in median error.

WiFi-based indoor localization, a fundamental technology for numerous real-world applications, including indoor navigation and emergency evacuation, has garnered significant attention over the past decade. While existing works have already achieved remarkable performance under various practical scenarios, they simply take corridor as a test scenario, and attribute performance degradation to complex multipath. To further release the functionality of corridor as bridges connecting different physical spaces, in this paper, we propose Widor, the firstWiFi indoor localization system that designed specifically for corridor. We first explore the neglected elevation angle dimension information in WiFi positioning and creatively place AP vertically to avoid the large-angle effect, which is caused by the unique slender structure of the corridor. To eliminate the impact of complex multipath introduced by the narrow environment, we make full use of the Toeplitz structure of the covariance matrix to further improve the spatial resolution of existing commercial WiFi AP without increasing the additional hardware cost. Furthermore, we design a tailored multi-APs joint height compensation algorithm to bridge the gap between 2-D and 3-D localization, which iteratively optimize the height difference between the AP and the client through an alternating optimization method. Both the time dimensional information and map constraint can be used to further improve localization accuracy. We evaluate Widor under various complex corridors in an$82m \times 65m$building, and extensive experimental results show that Widor can achieve$5.2^{\circ}$median angle estimation error and 58 cm localization median error. We also highlight that the proposed Widor system has little impact on the communication performance of existing commercial APs, and can be easily extended to any slender building scenarios, such as mines and tunnels, further broadening the application boundaries of WiFi indoor localization.

WiFi-based indoor localization serves as a critical foundation for numerous real-world applications and has attracted widespread attentions over the past decade. Recent advancements have demonstrated the feasibility of achieving decimeter-level accuracy by leveraging Angle of Arrival (AoA) information. However, existing commercial WiFi Access Points (APs) suffer from phase offset across different antennas, which significantly degrade the performance of AoA-based methods. Previous works either relied on labor-intensive manual calibration or involved inaccurate and non-robust automatic calibration, which hinders their widespread use in large-scale deployments. Moreover, to expand the signal coverage and enhance communication performance, the inter-antenna spacing in existing commercial APs typically exceeds the standard half-wavelength. The resulting angle ambiguity problem can mislead target detection results, which has not been well resolved in existing works. To address the above two practical challenges, in this paper, we propose CAFE, a practical WiFi indoor localization system based on theContinuousAngleFocusingEffect. The key insight lies on the fact that the angle information of multiple APs originates from the same client, and thus exhibits highly convergent properties in both the temporal and spatial dimensions. By further exploring the binary nature of phase offset and the periodicity of grating lobes, our approach can efficiently resolve the above two practical challenges. Extensive experiments are provided to demonstrate the effectiveness of the proposed CAFE system, which outperforms state-of-the-art methods by$22.1\%$in median localization error for simple scenarios and by$37.1\%$for complex multipath scenarios.

Event cameras, which capture pixel-level brightness changes asynchronously, provide rich motion information that is often missed during traditional frame-based camera exposures, thereby offering fresh perspectives for motion deblurring. Although current approaches incorporate event intensity, they neglect essential spatial motion information. Unlike their CNN architectures, Transformers excel in modeling long-range dependencies but struggle with establishing relevant non-local connections in sparse events and fail to highlight significant interactions in dense images. To address these limitations, we introduce a Motion-Adaptive Transformer network (MAT) that utilizes spatial motion information to forge robust global connections. The core design is an Adaptive Motion Mask Predictor (AMMP) that identifies key motion regions, guiding the Motion-Sparse Attention (MSA) to eliminate irrelevant event tokens and enabling the Motion-Aware Attention (MAA) to focus on relevant ones, thereby enhancing long-range dependency modeling. Additionally, we elaborately design a Cross-Modal Intensity Gating mechanism that efficiently merges intensity data across modalities while minimizing parameter use. The learnable Expansion-Controlled Spatial Gating further optimizes the transmission of event features. Comprehensive testing confirms that our approach sets a new benchmark in image deblurring, surpassing previous methods by up to 0.60dB on the GoPro dataset, 1.04dB on the HS-ERGB dataset, and achieving an average improvement of 0.52dB across two real-world datasets.

Passive non-line-of-sight (NLOS) imaging has developed rapidly in recent years. However, existing models generally suffer from low-quality reconstruction due to the severe loss of information during the projection process. In this paper, we introduce ParaEncodeNet, an NLOS imaging method for reconstructing high-quality, complex hidden scenes. Our approach utilizes a reconstruction network with parallel encoder to bridge the distribution gap between projection images and hidden images. The parallel encoder employs a codebook pretrained on a natural image dataset to construct a discrete prior, enabling the efficient encoding of projection images into hidden images. Moreover, we apply pixel-level constraints to the projection images to further reduce noise and distortion during reconstruction. Extensive experiments on a large-scale passive NLOS dataset have effectively demonstrated the superiority of our method over existing approaches, achieving a 1.2 dB increase in the Peak Signal-to-Noise Ratio (PSNR) metric. This validates the effectiveness and robustness of our proposed model in improving reconstruction quality and handling complex scenes.

Contactless stress monitoring, with its non-intrusive nature, is invaluable for maintaining mental and physical health. Recent studies have demonstrated encouraging results in contactless stress monitoring during daytime using radio frequency (RF) signals. However, the weak correlation between stress levels and behaviors during the night poses a significant challenge in stress monitoring, which remains unsolved. In this paper, we propose mmWave Nighttime Stress monitoring (mmNS), a learning-based end-to-end framework for contactless nighttime stress monitoring. Specifically, this framework incorporates radar signal processing and a self-supervised physiological feature separation strategy, combined with a signal complexity-oriented network design, to effectively extract and encode periodic physiological features for accurate stress level classification. To evaluate the stress monitoring performance of mmNS, we collect a RF-based nighttime stress monitoring dataset, which contains stress data from 10 volunteers. The experimental results demonstrate that our method achieves state-of-the-art stress monitoring performance, about 76% accuracy and 72% F1-score in classifying low, medium and high stress. To our knowledge, this is the first attempt dealing with contactless nighttime stress monitoring.

Human-centric perception with radio frequency (RF) signals has recently entered a new era of end-to-end processing with Transformers. Considering the long-sequence nature of RF signals, the State Space Model (SSM) has emerged as a superior alternative due to its effective long-sequence modeling and linear complexity. However, integrating SSM into RF-based sensing presents unique challenges including the fundamentally different signal representation, distinct frequency responses in different scenarios, and incomplete capture caused by specular reflection. To address this, we carefully devise a dual-branch SSM block that is characterized by adaptively grasping the most informative frequency cues and the assistant spatial information to fully explore the human representations from radar echoes. Based on these two branchs, we further introduce an SSM-based network for handling various downstream human perception tasks, named RFMamba. Extensive experimental results demonstrate the superior performance of our proposed RFMamba across all three downstream tasks. To the best of our knowledge, RFMamba is the first attempt to introduce SSM into RF-based human-centric perception.

Passive non-line-of-sight (NLOS) imaging aims to recover hidden scenes from indirect reflections. While reconstruction has been extensively studied, the high-level tasks for understanding hidden scenes, such as recognition, remain insufficiently explored despite their importance for practical applications. Direct classifying using either the projection or reconstructed images yields limited performance due to the severe image degradation. In this paper, we propose NLOS-R2, an alternate reconstruction-recognition framework that leverages the complementary nature of both tasks to enhance NLOS scene understanding. By iteratively optimizing reconstruction and recognition networks, our framework effectively improves recognition accuracy while maintaining reconstruction quality. To enable systematic evaluation, we introduce the first large-scale multi-class passive NLOS dataset, containing 42 classes and 50,400 projection and hidden image pairs. Extensive experiments demonstrate that our approach achieves 52.88% recognition accuracy, significantly outperforming existing methods. The code and dataset are available at https://github.com/ustceewy/NLOS-R2.

Radio sensing has emerged as a promising contactless revolution for cardiac monitoring. However, considering the complexity of radio propagation, extracting stable and clinically meaningful features remains challenging, posing a barrier to scaling this technology for practical and clinical reliable deployment. In this demo, we present RadioCardiogram, a system that leverages AI-powered knowledge transfer from well-established ECG diagnostic paradigms to accurately interpret complex radio signals. It enables all-in-one cardiac function monitoring including heart rate variability analysis, arrhythmia detection, and ECG-aligned waveform reconstruction. The system is implemented in a mobile phone-sized prototype and validated in a large-scale, clinically oriented cohort involving 6,258 outpatient visitors. Results demonstrate performance approaching the gold standard in both HRV monitoring and arrhythmia detection, highlighting a pathway toward effortless continuous, and reliable cardiac health coverage in real-world usage. The demo video is available at the following link.

The rapid evolution of millimeter-wave radar sensing technology has given rise to a proliferation of open-source radar datasets, creating an urgent need for innovative digital copyright protection techniques. However, conventional image and audio watermarking techniques are inadequate for radar copyright protection due to radar signals' sparsity, vulnerability and complexity. In this paper, we present RFinger, an ownership verification framework for static indoor millimeter-wave radar datasets. Our approach encodes environmental information extracted from radar signals into digital watermarks, strategically embedding these within carefully selected data frames to establish robust verification credentials. We develop statistical hypothesis testing metrics to detect unauthorized access to RFinger-protected data in black-box setting. Our strategic watermark design ensures that the unauthorized models exhibit distinctly anomalous performance on verification data compared to legitimate models. Through experiments on two large millimeter-wave radar datasets, we have validated that our designed strategy provides high watermark retrieval accuracy without compromising downstream tasks.

By analyzing cardiac motion modulated in body reflections, radio signals offer a novel contactless pathway for heartbeat sensing, attracting growing research attention. However, current studies overlooked the unique signal interaction when radio signals are incident at non-normal directions to the torso surface. This missing component significantly limits the effectiveness and results in unreliable performance in practical usage, where normal sensing direction cannot always be guaranteed. In this paper, we aim to answer the questions of what causes this performance degradation and how to solve it. Specifically, we analyze the signal interaction using a fine-grained thoracic motion model and reveal that non-stationary interference, caused by physiologically-driven spatial variation of the body surface, is the key to the problem. Correspondingly, we propose OSense, a framework based on a time-domain optimization method to cancel the non-stationary interference. This framework can be seamlessly integrated into various heartbeat sensing tasks. We validate OSense using a commercial Frequency Modulated Continuous Wave radar across multiple downstream tasks. The results demonstrate that our method effectively eliminates interference and enables direction-robust heartbeat sensing, highlighting its potential for practical cardiac monitoring using radio signals.

The major challenge in learning-based RF sensing is acquiring high-quality large-scale annotated datasets. Unlike visual datasets, RF signals are inherently non-intuitive and non-interpretable, making their annotation both time-consuming and labor-intensive. To address this challenge, we propose RF-URL 2.0, a novel unsupervised representation learning (URL) framework for RF sensing, which enables pre-training on easily collected, large-scale unannotated RF datasets to make downstream tasks solve easier. Existing URL techniques, such as contrastive learning, are primarily designed for natural images and are prone to learn shortcuts rather than meaningful information when applied to RF signals. RF-URL 2.0 is the first framework to overcome these limitations by constructing positive and negative pairs through well-established RF signal processing algorithms. Besides, it introduces a novel signal-model-driven augmentation technique, which augments signal representations by identifying and perturbing physically meaningful parameters of signal processing models. Moreover, the RF-URL 2.0 is carefully designed to take into account the heterogeneity characteristics of different RF signal processing representations. We show the universality of RF-URL 2.0 in three typical RF sensing tasks using two general RF devices (WiFi and radar), including human gesture recognition, 3D pose estimation, and silhouette generation. Extensive experiments on the HIBER and WiDAR 3.0 datasets demonstrate that RF-URL 2.0 takes a significant step toward learning-based solutions for RF sensing.

Millimeter-wave (mmWave) radar imaging has shown remarkable potential in critical applications. While previous researches have explored attacks on high-level radar perception, the vulnerability of low-level radar imaging to adversarial attacks remains largely unexplored. In this work, we introduce mmHide, the first general attack framework on mmWave radar imaging that utilizes neural rendering of meta-materials to hide imaging targets (e.g., handguns). mmHide’s novelty lies in its three-fold approach: (1) an implicit neural rendering network that efficiently represents and optimizes complex 3D meta-material structures, (2) an explicit differentiable forward imaging model that provides physical constraints, and (3) a self-supervised learning strategy that iteratively refines the meta-material design. This unique combination enables mmHide to create an “invisible cloak” for target objects while maintaining plausible imaging results. Extensive real-world experiments demonstrate mmHide’s effectiveness in significantly reducing target visibility while preserving background similarity. A user study confirms its high success rate in deceiving human observers, outperforming existing methods. These findings not only showcase the potential of our approach but also underscore the urgent need for robust defense mechanisms in mmWave imaging systems.

Passive non-line-of-sight (NLOS) imaging has witnessed rapid development in recent years, due to its ability to image objects that are out of sight. The light transport condition plays an important role in this task since changing the conditions will lead to different imaging models. Existing learning-based NLOS methods usually train independent models for different light transport conditions, which is computationally inefficient and impairs the practicality of the models. In this work, we propose NLOS-LTM, a novel passive NLOS imaging method that effectively handles multiple light transport conditions with a single network. We achieve this by inferring a latent light transport representation from the projection image and using this representation to modulate the network that reconstructs the hidden image from the projection image. We train a light transport encoder together with a vector quantizer to obtain the light transport representation. To further regulate this representation, we jointly learn both the reconstruction network and the reprojection network during training. A set of light transport modulation blocks is used to modulate the two jointly trained networks in a multi-scale way. Extensive experiments on a large-scale passive NLOS dataset demonstrate the superiority of the proposed method. The code is available at https://github.com/JerryOctopus/NLOS-LTM.

Radio sensing has emerged as a promising solution for monitoring vital signs in a contactless manner. However, most of the existing designs focus on stationary target and struggle with body motion interference. While some efforts have been made to address this issue, the lack of a physical explanation for the motion elimination principle makes them work as a blind signal separation way and thus leaves the body motion elimination problem still as an open challenge. In this paper, we reveal for the first time the existence of “dark pixels”–specific points on the same rigid body parts that share the same body movement but exhibit varying physiological motions, with these variations still preserving the physiological rhythm. By exploiting the inherent relationship between the dark pixels, we propose a cooperative sensing framework, Co-Sense, that can achieve robust radio sensing for non-stationary targets in an explainable way. Through extensive experiments, Co-Sense demonstrates its superiority over existing methods, achieving effective motion cancellation and breath sensing with a median absolute respiratory rate (RR) error of 0.36 respiration per minute (RPM) and breath wave correlation of 0.61 under non-stationary scenarios. The results indicate the great potential of Co-Sense in enhancing the accuracy of vital sign sensing with radio signals, especially in real-world environments where targets are rarely stationary.

Recent advancements have showcased the potential of handheld millimeter-wave (mmWave) imaging, which applies synthetic aperture radar (SAR) principles in portable settings. However, existing studies addressing handheld motion errors either rely on costly tracking devices or employ simplified imaging models, leading to impractical deployment or limited performance. In this paper, we present IFNet, a novel deep unfolding network that combines the strengths of signal processing models and deep neural networks to achieve robust imaging and focusing for handheld mmWave systems. We first formulate the handheld imaging model by integrating multiple priors about mmWave images and handheld phase errors. Furthermore, we transform the optimization processes into an iterative network structure for improved and efficient imaging performance. Extensive experiments demonstrate that IFNet effectively compensates for handheld phase errors and recovers high-fidelity images from severely distorted signals. In comparison with existing methods, IFNet can achieve at least 11.89 dB improvement in average peak signal-to-noise ratio (PSNR) and 64.91% improvement in average structural similarity index measure (SSIM) on a real-world dataset.

Self-supervised learning (SSL) is a powerful approach that learns general semantic representations from large-scale unlabeled data to make downstream tasks solve easier, offering significant potential in enhancing downstream performance and alleviating the appetite for large-scale annotated data. However, existing SSL techniques, predominantly designed for natural images, may be prone to shortcuts when applied to RF signals. This study presents surprising empirical findings showing that SSL can indeed learn meaningful RF representations by employing simple group shuffle (GS) and asymmetry augmentation techniques. The GS augmentation is inspired by blind calibration tasks in Time-Interleaved Analog-to-Digital Converters (TIADC). By treating the original RF signal as a composite output from sub-ADCs, GS augmentation enriches RF signals while preserving their global semantics. We also provide a theoretical validation of the GS augmentation’s singular value consistency. Notably, we observe that the shortcut is essentially a domain gap between the pre-trained and the downstream task models. This issue can be mitigated by an asymmetry augmentation technique, which maximizes the similarity between an original RF signal and its augmented version, rather than between two augmentations of the same RF signal. By integratinggroupshuffle andasymmetryaugmentation (GSAA) into an existing contrastive learning framework, we develop an effective contrastive learning approach for RF signals. Our evaluations, spanning seven downstream RF sensing tasks across two general RF devices (WiFi and radar), strongly demonstrate that GSAA plays a significant role in advancing SSL-based solutions in RF sensing.

In the above article [1], on page 13900, right column, there is an empty reference citation “[?]” in the sentence “By applying Model-Agnostic Meta-Learning (MAML) to fingerprint localization, MetaLoc [?] enables the model to quickly adapt to new environments based on the obtained meta-parameters, thus reducing human labor costs.” The missing reference is listed below as [2].

Human tracking plays a crucial role in various wireless sensing applications. However, recent advancements have primarily focused on constrained experimental scenarios with less interference, often involving a few individuals performing actions in an empty space without obstacles. In empirical unconstrained scenarios, such as daily office scenes, severe interference and attenuation caused by chaotic environments is inevitable which results in dramatic performance degradation. In this paper, we introduce TBDNet, which incorporates tracking-before-detect (TBD) from conventional signal processing into learning-based models, achieving impressive tracking performance in unconstrained scenarios. TBDNet follows first-track-then-detect pipeline. It maps input heatmap sequence into high-level frame-wise features to adapt the time-varying intensity distribution and motion pattern of targets. After that, the temporal information is accumulated in feature space to obtain trace proposals. We then predict the accurate positions and probability of traces at each timestamp. To assess the efficiency of TBDNet, we collect and release the first RF-UNIT (RF-based Unconstrained Indoor Tracking) dataset, which comprises 4,030,880 radar heatmaps and the corresponding tracking annotations under 6 different scenarios. To our knowledge, RF-UNIT is the first dataset for RF-based human tracking in unconstrained scenes. We anticipate that TBDNet and the RF-UNIT dataset will significantly contribute to the advancement of RF-based sensing technologies.

Millimeter-wave (mmWave) radar sensing powered by deep learning is now emerging in numerous applications, which are predominantly trained in a supervised manner. However, due to the non-interpretable nature of mmWave signals, labeling the radar data has always been a difficult task. While there have been investigations on unsupervised pre-training for mmWave radar sensing, these methods are tailored to specific signal representations. In this paper, we propose UMIMO, an unsupervised learning framework combining the hardware nature of MIMO radar and deep learning techniques to resolve the challenge raised by the insufficient labeled data. UMIMO leverages the antenna arrays synthesized from multiple transmitting and receiving antennas in mmWave radar to construct positive samples for contrastive learning. To achieve this, we propose the constraints on angular resolution and grating lobes to generate effective signal representations with different synthetic arrays. We conduct experiments using UMIMO on three tasks: contactless ECG monitoring, 3D human pose estimation, and human silhouette generation. All experimental results demonstrate that UMIMO can effectively improve the performance of learning-based mmWave radar sensing in an unsupervised manner.

Intelligent Reflecting Surface (IRS) has emerged as a crucial technology for indoor localization in future wireless networks. However, existing IRS-aided systems that utilize commercial off-the-shelf (COTS) WiFi devices face challenges due to two key factors. First, the passive reflective nature of IRS generates relatively weak reflected signals, which can be easily drowned out by multipath interference and noise. Secondly, existing works require a large number of IRS codebooks for fine-grained scanning of the entire area, resulting in significant time requirements. This paper introduces SRAL-IRS, which enables swift, robust, and accurate IRS-aided indoor localization using commercial WiFi devices. Through intelligent codebook design and theoretical derivation, we reveal the theoretical relationship between the IRS codebook modifications, target position, and variations in the received signal. Subsequently, SRAL-IRS mitigates the impact of environmental and hardware noise by carefully selecting and combining WiFi subcarriers. Finally, by utilizing the received data from multiple codebooks and subcarriers and leveraging the orthogonality between the noise subspace and the IRS-reflected signal subspace, we can achieve precise localization even with a limited number of IRS codebooks. Real-world implementation of SRAL-IRS, utilizing our designed IRS prototype and COTS WiFi devices, validates its feasibility and effectiveness.

Contactless vital signs estimation using mmWave radar has gained significant attention. However, existing studies are built upon the radar being directed facing the thorax to capture fine-grained vital signs, ignoring the angle variation between the radar and thorax in practical deployment. In this paper, we propose a spatial-temporal optimization model to estimate the human body orientations between the radar and thorax through extracting the multi-domain features of reflected signal. By aligning the signal variation captured from different angles, we can realize orientation-free vital sign sensing. The system achieves an average angle estimation error of 13.1°, and a 14.8% discrepancy reduction in terms of the mean absolute error of the signal captured at different angles.

Fall detection using Radio Frequency (RF) signals with deep learning has exhibited significant promise in recent years. However, the costly collection of RF data with falls has hampered the performance of existing methods. While there has been approaches which can generate RF signals using various simulation methods, they rely on human-body modeling based on other modalities. Moreover, the realism of the generated signals is insufficient because these approaches cannot accurately capture the human radar cross section (RCS). In this paper, we propose SimFall, which generates simulated data for RF-based fall detection without overhead for data collection. SimFall first simulates the fall process by manipulating the human body mesh based on practical fall model. Then a grid shooting and bouncing ray (SBR) method is utilized to calculate the accurate RCS. Finally, SimFall computes the original signal and transforms it into different forms that reveal the features of falls. The experimental results demonstrate that the data produced by SimFall effectively enhances the accuracy of the RF-based fall detection network.

Recent advancements have showcased the potential of hand-held millimeter-wave (mmWave) imaging, which applies synthetic aperture radar (SAR) principles in portable settings. However, existing studies addressing handheld motion errors either rely on costly tracking devices or employ simplified imaging models, leading to impractical deployment or limited performance. In this paper, we present IFNet, a novel deep unfolding network that combines the strengths of signal processing models and deep neural networks to achieve imaging and focusing for handheld mmWave systems. By integrating multiple priors and mapping the optimization processes into an iterative network structure, IFNet effectively compensates for phase errors and recovers high-fidelity images from severely distorted signals. Extensive experiments demonstrate that IFNet outperforms state-of-the-art methods, both qualitatively and quantitatively.

Radar sensing has been a promising solution for contactless monitoring of Heart Rate Variability (HRV), an essential indicator of the cardiovascular and autonomic nervous systems. However, existing works neglect heartbeat-driven body surface motions spreading across the entire body with spatial variations, which limits their accuracy in identifying fine-grid consecutive heartbeat timings and overall HRV performance. In this paper, we propose to exploit the entire body reflections and model the inherent spatial-temporal relationship between these reflections and heartbeats by deep neural network for contactless HRV monitoring. Specifically, a hybrid convolution-transformer-based network is designed to convert the complex multi-dimensional spatial-temporal modeling problem into an efficient sequence modeling process. Experimental results demonstrate its superiority over the baseline method, achieving the median IBI estimation error of 12ms (w.r.t. 98.47% accuracy), RMSDD error of 7.3ms, SDRR error of 2.9ms, pNN50 error of 5.5%.

Intrusion detection acts as a key to in-home security, where WiFi-based systems have gained wide attention due to the ubiquitous nature of WiFi signals. While existing methods achieve impressive performance in specific environments, they are susceptible to environmental changes, especially for complex scenarios where outdoor human activities can be mistaken as intrusions. In this paper, we propose RoFi, a robust WiFi intrusion detection system which can handle more complex scenarios. It achieves this by exploring the distribution of autocorrelation function (ACF) of Channel State Information (CSI) when intrusion occurs, where likelihood ratio testing is employed to discriminate intrusion and non-intrusion scenarios, eliminating the variance of different environments. Without complex calibration, RoFi achieves an accuracy of over 97.5% in practical deployment, outperforming existing methods.

Millimeter-wave (mmWave) radar has gained increasing attention in environmental perception due to its robustness under low-light conditions. However, existing methods fail to address the challenges of multipath interference and low angle resolution. In this paper, we introduce DiffRadar which leverages the diffusion probabilistic model (DPM) for high-quality mmWave environmental sensing. To adapt DPM for radar signals that lack pix-level structural information, we design a contour encoder to capture intrinsic scene features that enable the DPM to learn a robust representation from radar data. Then the DPM decoder utilizes this high-level semantic information to effectively reconstruct real-world scene distribution. Extensive experiments have demonstrated that our approach surpasses state-of-the-art methods in various complex scenarios.

The mutual interference mitigation between identical or similar radar systems in autonomous driving has gained wide spread attention from both academia and industry. The resulted ghost target interference will reduce the sensitivity of the radar sensor and increase the false alarm rate. To tackle this problem, in this paper, we make full use of two characteristics of interference to achieve ghost target interference mitigation in the Doppler domain. The key insight lies in the fact that the interference is one-way propagation, and thus the resulted ghost target can be converted to the noise floor in the Doppler domain through random slow-time coding. Moreover, the high power characteristic of interference allows us to further enhance the interference mitigation performance by adopting a signal-to-interference-plus-noise ratio (SINR) maximization principle. Numerical examples are provided to demonstrate the effectiveness of the proposed interference mitigation approach.

Recent advancements in WiFi indoor localization have demonstrated the potential for achieving decimeter-level accuracy based on Angle of Arrival (AoA). However, existing commercial WiFi Access Points (APs) suffer from phase offset across different antennas, which significantly degrade the performance of AoA-based methods in practical deployment. Previous work either relied on labor-intensive manual calibration or involved inaccurate and non-robust automatic calibration. In this paper, we propose AutoCali, an accurate and robust automatic phase offset calibration system. The key insight is to utilize the binary nature of phase offsets and the property that triangulation exhibits higher convergence when the correct combination of phase offsets is employed. Extensive experiments demonstrate that AutoCali outperforms state-of-the-art methods by 22.1% in median localization error for simple scenarios and by 37.1% for complex multipath scenarios.

Intelligent reflecting surfaces (IRS) is a novel integrated sensing and communication technology that can manipulate the propagation of wireless signals. However, existing IRS-based sensing systems require directional antennas for signal transmission, incompatible with commercial WiFi devices. This paper proposes an IRS-aided localization system using omnidirectional antennas and reveals two critical challenges in practical deployment. First, the accurate distance between the IRS and the transmitter is needed for IRS codebook design, but practice measurements invariably introduce centimeter-level bias, which seriously affects localization accuracy. We derive a linear relationship between measurement bias and localization error for calibration. Second, only relative IRS phase change under different bias voltages can be measurable, not the absolute phase offset, introducing an unknown fixed phase offset in reflections. We solve this challenge by eliminating the signals that are not related to the IRS. Experiments validate the proposed calibration techniques, proving that our system achieves high-precision passive localization.

This paper introduces a novel paradigm for learning-based RF sensing, termed Pre-training RF signals In Sparsity-aware Masked autoencoders (PRISM), which shifts the RF sensing paradigm from supervised training on limited annotated datasets to unsupervised pre-training on large-scale unannotated datasets, followed by fine-tuning with a small annotated dataset. PRISM leverages a carefully designed sparsity-aware masking strategy to predict missing contents by masking a portion of RF signals, resulting in an efficient pre-training framework that significantly reduces computation and memory resources. This addresses the major challenges posed by large-scale and high-dimensional RF datasets, where memory consumption and computation speed are critical factors. We demonstrate PRISM’s excellent generalization performance across diverse RF sensing tasks by evaluating it on three typical scenarios: human silhouette segmentation, 3D pose estimation, and gesture recognition, involving two general RF devices, radar and WiFi. The experimental results provide strong evidence for the effectiveness of PRISM as a robust learning-based solution for large-scale RF sensing applications.

Advanced human sensing technologies based on radio frequency (RF) signals have gained widespread attention in recent years. However, due to the sparsity and incompleteness of RF signals, fine-grained RF-based multi-person 3D pose estimation has progressed more slowly. In this paper, we present RF-based Pose Machine (RPM 2.0) for multi-person 3D pose estimation using RF signals. Specifically, we first develop a lightweight anchor-free detector module to locate and crop regions of interest from horizontal and vertical RF signals. Afterward, we treat the horizontal and vertical millimeter-wave radars as “RF cameras” with different viewing angles and propose a Multi-view Fusion Network to unproject the RF signals into a unified latent feature space, and then calculate the correlation for weighted fusion. Finally, a Spatio-Temporal Attention Network is designed to reconstruct the multi-person 3D skeleton sequences, in which the spatial attention module is proposed to recover invisible body parts using non-local correlations among joints and the temporal attention module refines the 3D pose sequences using temporal coherency learned from frame queries. We evaluate the performance of the proposed RPM 2.0 and state-of-the-art methods on a large-scale dataset with multi-person 3D pose labels and corresponding radar signals. The experimental results show that RPM 2.0 outperforms all of the baseline methods, which locates multi-person 3D key points with an average error of$73 mm$and generalizes well in new data such as occlusion, low illumination.

Millimeter-wave (mmWave) radar pointcloud offers attractive potential for 3D sensing, thanks to its robustness in challenging conditions such as smoke and low illumination. However, existing methods failed to simultaneously address the three main challenges in mmWave radar pointcloud reconstruction: specular information lost, low angular resolution, and severe interference. In this paper, we propose DREAM-PCD, a novel framework specifically designed for real-time 3D environment sensing that combines signal processing and deep learning methods into three well-designed components to tackle all three challenges: Non-Coherent Accumulation for dense points, Synthetic Aperture Accumulation for improved angular resolution, and Real-Denoise Multiframe network for interference removal. By leveraging causal multiple viewpoints accumulation and the "real-denoise" mechanism, DREAM-PCD significantly enhances the generalization performance and real-time capability. We also introduce RadarEyes, the largest mmWave indoor dataset with over 1,000,000 frames, featuring a unique design incorporating two orthogonal single-chip radars, Lidar, and camera, enriching dataset diversity and applications. Experimental results demonstrate that DREAM-PCD surpasses existing methods in reconstruction quality, and exhibits superior generalization and real-time capabilities, enabling high-quality real-time reconstruction of radar pointcloud under various parameters and scenarios. We believe that DREAM-PCD, along with the RadarEyes dataset, will significantly advance mmWave radar perception in future real-world applications.

The electrocardiogram (ECG) has always been an important biomedical test to diagnose cardiovascular diseases. Current approaches for ECG monitoring are based on body attached electrodes leading to uncomfortable user experience. Therefore, contactless ECG monitoring has drawn tremendous attention, which however remains unsolved. In fact, cardiac electrical-mechanical activities are coupling in a well-coordinated pattern. In this paper, we achieve contactless ECG monitoring by breaking the boundary between the cardiac mechanical and electrical activity. Specifically, we develop a millimeter-wave radar system to contactlessly measure cardiac mechanical activity and reconstruct ECG without any contact in. To measure the cardiac mechanical activity comprehensively, we propose a series of signal processing algorithms to extract 4D cardiac motions from radio frequency (RF) signals. Furthermore, we design a deep neural network to solve the cardiac related domain transformation problem and achieve end-to-end reconstruction mapping from RF input to the ECG output. The experimental results show that our contactless ECG measurements achieve timing accuracy of cardiac electrical events with median error below 14ms and morphology accuracy with median Pearson-Correlation of 90% and median Root-Mean-Square-Error of 0.081mv compared to the groudtruth ECG. These results indicate that the system enables the potential of contactless, continuous and accurate ECG monitoring.

While deep learning-based RF perception has received significant attention in recent years, the requirement for massive labeled RF data has hindered its further advancement. Despite existing efforts in synthesizing signals, they fail to accurately calculate the Radar Cross Section (RCS) of the target, leading to less practicality of the synthesized signals. In this paper, we introduce Simulated Body Radio Frequency (SBRF), a novel signal synthesis framework for calculating more realistic RCS by combining ray tracing with electromagnetic computation. SBRF involves three key components: a grid-based Shooting and Bouncing Ray (SBR) algorithm to calculate fine-grained human body RCS, a novel ray partitioning algorithm to improve the efficiency of ray tracing, and a coordinate transformation method to sense moving targets. Furthermore, we also design unique data augmentation techniques to improve the efficiency and generalizability of signal synthesis. Extensive experimental evaluations conducted on two publicly available datasets, involving wide-scale activity recognition and fine-grained gesture recognition, demonstrate the effectiveness of SBRF-generated signals in improving RF perception performance and alleviating the challenge of RF data collection.

WiFi-based indoor localization has gained widespread attention due to the pervasive availability of WiFi Access Points (APs). While signal processing-based methods can achieve decimeter-level localization, their performance is constrained by the limited spatial resolution of WiFi systems, especially in complex environments with strong interference. By contrast, deep learning-based methods have achieved impressive performance even in complex environments, which however often fail to generalize to new environments. In this paper, we propose a novel framework to learn domain-invariant model for WiFi-based indoor localization, which maintains impressive performance across different environments. The key insight is to design a deep learning-based WiFi localization system through the perspective of signal processing. Specifically, we let the neural network estimate APs-centered polar coordinates to avoid fitting the coordinates of APs strongly correlated with the environment, enabling us to obtain the domain-invariant model. To unleash the potential of neural networks in regressing high-precision parameters, we design a beamforming layer to integrate the knowledge of signal processing. Furthermore, we propose a multi-task learning scheme to further improve localization accuracy. Extensive experiments on diverse datasets have demonstrated that the localization performance of our method outperforms state-of-the-art methods and demonstrates superiority under cross-domain conditions.

WiFi-based respiration sensing technology has gained increasing attention due to its contactless sensing capabilities and utilization of existing WiFi devices. However, existing studies are limited to certain scenarios without addressing the motion interference from other individuals. In this paper, we tackle the challenge of robust respiration sensing in the presence of other individuals. Specifically, through an in-depth examination of the correlation between respiratory signals and spatial beam patterns, we develop a respiratory-energy based approach to evaluate the diverse impact of dynamic interference on respiratory signals. When significant interference is detected, we employ a convex-optimization-based beam control strategy, which exploits the inherent characteristics of human respiration, to adaptively adjust the spatial beam pattern. This approach enables a robust and precise gain adjustment between the target and interfering individual, effectively mitigating the impact of interference. Experimental results demonstrate that our approach can reduce the mean absolute error (MAE) of respiration detection by up to 32% compared to state-of-the-art methods, significantly enhancing the accuracy and robustness of WiFi-based respiration sensing.

Millimeter-wave (mmWave) radar has been widely used in wireless sensing due to its non-contact nature, privacy preservation, and immunity to adverse lighting conditions. However, with more and more mmWave radars working in the same frequency band, mutual device interference among them is inevitable and has become a serious problem. The device interference reduces the signal-to-interference-plus-noise ratio (SINR) and significantly degrades the detection performance. Existing works mainly focus on the vital sign monitoring in different practical scenarios (e.g., device movement, human movement, multi-person interference, and in-car scenario), and the vital sign monitoring in the presence of mutual device interference is still not well resolved. In this paper, we propose a novel interference mitigation framework, iSense, to enable radar vital sign sensing under device interference. By exploiting one-way propagation characteristic of device interference, iSense can effectively detect and suppress the interference. We evaluate iSense under a variety of complex device interference scenarios, including different distances, angles, and numbers of aggressor radars, as well as the impact of different environments. Experimental results show that the accuracy of respiration and heartbeat estimation of iSense can reach over 99.2% and 98.6%, indicating that iSense takes an important step towards the practical development of radar sensing.

Indoor localization is a fundamental task to many real-world applications, which however remains unresolved, especially with commodity WiFi Access Points (APs). In this paper, we tackle this problem and propose an accurate, robust, and real-time indoor localization system that can be directly deployed on commodity WiFi infrastructure. Specifically, the proposed system makes three key contributions: 1) we introduce a non-parametric metric to measure the accuracy of Angle of Arrival (AoA) estimation; 2) we are the first to explicitly consider the relationship among the AoAs of different APs and propose a multiple APs co-localization algorithm to exploit such a relationship to improve the localization performance; 3) we propose several strategies to reduce the computational complexity of our system to achieve real-time localization. Extensive experiments are conducted to evaluate the performance of the proposed system under various situations, which demonstrate that the proposed system can achieve a 4 degrees median error of AoA estimation and 30 cm localization median error, outperforming the state-of-the-art systems.

Intelligent reflecting surface has gained significant attention for supporting integrated sensing and communication (ISAC) by manipulating wireless signals. However, existing IRS-based sensing systems commonly utilize directional horn antennas for signal transmission, which is incompatible with commercial WiFi devices and contradicts the concept of ISAC. In this paper, we propose an IRS-aided localization system with omnidirectional antennas to overcome these limitations. We reveal two critical challenges for implementing such a system. First, precise distance between the IRS and transmitter is requisite for IRS codebook design, yet practical measurement introduces centimeter-scale biases, causing severe localization errors. We derive a linear relationship between measurement bias and localization error, which serves as the basis for a calibration method. Second, in practice, we can only obtain the relative phase change of the IRS under different bias voltages, but cannot measure the phase offset introduced by IRS at zero voltage. Consequently, an unknown fixed offset appears when calculating the channel response of the signal reflected by the IRS, which destroys subsequent IRS codebook design. We resolve this problem by eliminating the signals that are not related to the IRS. Extensive experiments validate the proposed calibration techniques and demonstrate that our system achieves accurate passive localization.

Radio-frequency (RF) based human sensing technologies, due to their great practical value in various applications and privacy-preserving nature, have gained tremendous attention in recent years. However, without fully exploiting the characteristics of radio signals, the performance of existing methods are still limited. First, RF features of the moving human body have different representations in dimensions such as channel and scale, which is challenging when performing feature fusion. Besides, the human body is specularly reflective with respect to the radar, which means the human body cannot be fully captured by a single RF snapshot. Therefore, the radar signal reflected by the human body is sparse and incomplete, which is difficult to extract high-quality features for 3D human pose estimation. In this paper, we present the RF-based Pose Machines (RPM), a novel framework which can generate 3D skeletons from RF signals. Considering the characteristics of RF signals, RPM includes several modules to overcome the challenges. Firstly, a Feature Fusion Network (FFN) is designed to effectively fuse radio signals from horizontal and vertical planes based on the channels' correlation and maintain high-quality feature via a multi-scale fusion block. A Spatio-Temporal Attention network is then designed to reconstruct 3D skeletons from the sparse and incomplete RF signals. Specifically, a spatial attention module is designed to model non-local relationships among joints and reconstruct body parts that a single RF snapshot cannot capture. Afterwards, a temporal attention module is proposed to refine 3D pose based on temporal coherency learned from frame queries. To evaluate the performance of our RPM framework, we construct a large-scale dataset of synchronized 3d skeletons and RF signals, RFSkeleton3D. Our experimental results show that RPM locates 3D key points of the human body with an average error of$5.71 cm$and maintains its performance in new environments with occlusion or bad illumination. The dataset and codes will be made in public.

Existing RF-based human pose estimation methods usually require intensive computations and cannot meet the real-time processing and portability requirements for mobile devices. To tackle the limitation, in this article, we introduce a lightweight RF-based pose estimation model, i.e., MobiRFPose, to construct the portable RF-based pose camera. Different from traditional optical-based cameras, the RF-based camera does not capture visual information, which means the privacy-preserving characteristic. Specifically, we only utilize a horizontal antenna array to transceive RF signals, then estimate the human locations on the RF signal heatmap and crop the human location regions, and finally estimate the fine-grained human poses based on the cropped small RF signal heatmaps. To evaluate the performance, we compare MobiRFPose with state-of-the-art methods. Experimental results demonstrate that MobiRFPose can achieve accurate 3D human pose estimation with fewer parameters and computations. We also test the trained MobiRFPose model using mobile computing devices, where the model structures and parameters only take up 268 KB and 3226 KB of disk space, and MobiRFPose can achieve 66 FPS processing speed. The pose estimation error is 11.05 cm in the case of a single person and 11.29 cm in the case of multiple people. All experimental results indicate that our proposed method can construct a portable RF camera to estimate human poses accurately.

Existing deep learning-based wireless sensing models usually require intensive computation. In this paper, we introduce a lightweight RF-based 3D human pose estimation model, i.e., Fast RFPose, to enable real-time human pose estimation. Specifically, Fast RFPose first estimates the human locations in the RF heatmap and crops the human location regions, then estimates the fine-grained human poses based on the cropped small RF heatmaps. In the experiments, we build a radio system and a multi-view camera system to acquire the RF signals and the ground-truth human poses, and compare Fast RFPose with state-of-the-art methods. Experimental results demonstrate that Fast RFPose outperforms the alternative methods. Besides, we further deploy the trained Fast RFPose model on a laptop with a CPU and Fast RFPose can achieve 66 FPS processing speed, which means it can meet the real-time running requirements in mobile devices.

In this paper, we present RF-based Multi-view Pose machine (RF-MvP) for multi-person 3D pose estimation using RF signals. Specifically, we first develop a lightweight anchor-free detector module to locate and crop regions of interest from horizontal and vertical RF signals. Afterward, we propose a Multi-view Fusion Network to unproject the RF signals from the horizontal and vertical millimeter-wave radars into a unified latent space, and then calculate the correlation for weighted fusion. Finally, a Spatio-Temporal Attention Network is designed to reconstruct the multi-person 3D skeleton sequences, in which the spatial attention module is proposed to recover invisible body parts using non-local correlations among joints and the temporal attention module refines the 3D pose sequences using temporal coherency learned from frame queries. We evaluate the performance of the proposed RF-MvP and state-of-the-art methods on a large-scale dataset with multi-person 3D pose labels and corresponding radar signals. The experimental results show that RF-MvP outperforms all of the baseline methods, which locates multi-person 3D key points with an average error of 73mm and generalizes well in new data such as occlusion, low illumination.

Due to a large number of brand logos and similar text appearances, existing deep detection networks are prone to generating multiple overlapping logos at a single location. In this paper, we propose a two-stage approach, Cascade-LogoNet, by adding a verification stage to eliminate the classification ambiguity. We group overlapping logo proposals and verify the class predictions inside each group by learning more discriminative features with strong inter-class discrepancy. We also design a lightweight detector to improve computational efficiency and prevent over-fitting given limited training data. In addition, we propose a new data augmentation strategy, named small logo preserving stitching (SLPS), to improve the detection accuracy of small logos. When stitching training images to increase the loss ratio of small logos, we keep the original small logos on the stitched image to avoid extremely undistinguishable small ones. Our Cascade-LogoNet achieves 63.5 mAP and 64.4 mAP on the dataset QMUL-OpenLogo [1] when using ResNet-50 and ResNet-50-DCN [2] as backbone respectively, which surpasses previous methods by a large margin.

Toxic gases inhalation is the most common cause of death in fire scenes, which can make people unconscious and unable to save themselves. Hence, discovering the unconscious victims is crucial to improve their survival rate. In this paper, we propose RF-Search, a victim searching system with RF device mounted on the drone. The challenge mainly comes from the fact that drone motion would overwhelm the subtle vital signs utilized for victim identification. To resolve this problem, we have noted that the physical signature of drone motion has been encoded in stationary object reflections. Leveraging this unique physical signature, we propose to identify the unconscious victim through the spatio-temporal correlation between signals reflected from the victim and the surrounding stationary objects. To extract respiration information of the victim, we propose a motion segmentation module and a motion compensation module to suppress the signal variation caused by drone movement. Extensive experiments have demonstrated that our system could achieve an accuracy of 92.5% for victim identification.

The past decade has witnessed emerging applications of breath monitoring using off-the-shelf WiFi devices owing to their low-cost, non-intrusive, and privacy-friendly characteristics. While existing works have achieved promising results in certain scenarios, the performance degradation introduced by the interfering person who moves around the target user has not been fully investigated, which hinders practical applications of WiFi-based breath sensing. In this paper, we propose a robust respiration sensing system with WiFi which could achieve accurate respiration sensing under strong interference. To achieve this, we first design a 2-D Capon beamformer to maximize the signal-to-interference-plus-noise ratio (SINR). Then, the interfering user’s trajectory is estimated through spatial-temporal processing. Finally, we design a respiration extracting algorithm based on the constraint of the interferer’s trajectory and breath energy to find the optimal position to extract breath signals. Extensive experimental results show that the proposed framework can reduce the Mean Absolute Error (MAE) of breath rate estimation by up to 48% compared with the existing state-of-the-art methods, which demonstrates the superior robustness and effectiveness of our system.

Human gesture recognition with WiFi signals has attained acclaim due to the omnipresence, privacy protection, and broad coverage nature of WiFi signals. These gesture recognition systems rely on neural networks trained with a large number of labeled data. However, the recognition model trained with data under certain conditions would suffer from significant performance degradation when applied in practical deployment, which limits the application of gesture recognition systems. In this paper, we propose UDAWiGR, an unsupervised domain adaptation framework for WiFi-based gesture recognition aiming to enhance the performance of the recognition model in new conditions by making effective use of the unlabeled data from new conditions. We first propose a pseudo-labeling method with confidence control constraint to utilize unlabeled data for model training. We then utilize consistency regularization to align the output distribution for enhancing the robustness of neural network under signal perturbations. Furthermore, we propose a cross-match loss to combine the pseudo-labeling and consistency regularization, which makes the whole framework simple yet effective. Extensive experiments demonstrate that the proposed framework could achieve 4.35% accuracy improvement comparing with the state-of-the-art methods on public dataset.

The past years have witnessed increasing research interests in achieving passive human localization using WiFi signals. However, due to the limited spatial resolution of WiFi devices, it is still difficult to achieve accurate localization with existing WiFi infrastructures. To tackle this problem, in this paper, we propose a RIS-aided passive localization frame-work, which exploits the degree of freedom provided by the Reconfigurable Intelligent Surface (RIS) to achieve accurate localization. We have noted that RIS is composed of a large number of controllable reflective elements, which can break through the resolution limitation of commodity WiFi devices without making any changes to existing infrastructures. Hence, we propose a phase control optimization algorithm that can maximize the discrepancy between human reflection and multi-path interference. In order to solve the near-far effect in multi-person scenario, we propose a Side-lobe Cancellation Algorithm to separate the reflected signals of different people and achieve accurate localization. Extensive simulation results demonstrate the proposed framework is capable of locating the moving persons passively with sub-centimeter accuracy in the presence of noise and multi-path interference.

The past decade has witnessed emerging applications of achieving indoor localization using WiFi by estimating the Angle of Arrival (AoA). While various algorithms have been proposed, their performance in practical indoor environment is still limited. An important limitation lies in the fact that the confidences of AoA estimations from different devices are actually unequal, which has not been considered nor addressed. In this paper, we propose WiCo, a confidence-aware localization framework by analyzing the confidence of the spatial spectrum. Specifically, we first evaluate the unequal confidence caused by different beamwidth and multipath on different APs. Then, we propose a normalized distribution confidence and a full reference confidence to quantify the reliability of spatial spectrum on different devices. Finally, we resolve the unequal confidence factor caused by geometric deployment through re-weighting and perform localization. Extensive real-world experiments demonstrate that WiCo could reduce the median localization error by 43.8%.

WiFi-based imaging enables pervasive sensing in a privacy-preserving and cost-effective way. However, most of existing methods either require specialized hardware modification or suffer from the poor imaging performance due to the fundamental limit of off-the-shelf commodity WiFi devices in spatial resolution. We observe that the recently developed reconfigurable intelligent surface (RIS) could be a promising solution to overcome these challenges. Thus, in this article, we propose an RIS-aided WiFi imaging framework to achieve high-resolution imaging with the off-the-shelf WiFi devices. Specifically, we first design a beamforming method to achieve the first-stage imaging by separating the signals from different spatial locations with the aid of the RIS. Then, we propose an optimization-based super-resolution imaging algorithm by leveraging the low-rank nature of the reconstructed object. During the optimization, we also explicitly take into account the effect of finite phase quantization in RIS to avoid the resolution degradation due to quantization errors. Simulation results demonstrate that our framework achieves median root-mean-square error (RMSE) of 0.03 and median structural similarity (SSIM) of 0.52. The visual results show that high-resolution imaging results are achieved with simulation signals at 5 GHz that are matched with commercial WiFi 802.11n/ac protocols.

Human gesture recognition with radio frequency (RF) signals has attained acclaim due to the omnipresence, privacy protection, and broad coverage nature of RF signals. These gesture recognition systems rely on neural networks trained with a large number of labeled data. However, the recognition model trained with data under certain conditions would suffer from significant performance degradation when applied in practical deployment, which limits the application of gesture recognition systems. In this article, we propose an unsupervised domain adaptation framework for RF-based gesture recognition aiming to enhance the performance of the recognition model in new conditions by making effective use of the unlabeled data from new conditions. We first propose pseudo labeling and consistency regularization to utilize unlabeled data for model training and eliminate the feature discrepancies in different domains. Then we propose a confidence constraint loss to enhance the effectiveness of pseudo labeling, and design two corresponding data augmentation methods based on the characteristic of the RF signals to strengthen the performance of the consistency regularization, which can make the framework more effective and robust. Furthermore, we propose a cross-match loss to integrate the pseudo labeling and consistency regularization, which makes the whole framework simple yet effective. Extensive experiments demonstrate that the proposed framework could achieve 4.35% and 2.25% accuracy improvement comparing with the state-of-the-art methods on public WiFi data set and millimeter wave (mmWave) radar data set, respectively.

This paper introduces a novel framework, i.e., RFPose-OT, to enable three-dimensional (3D) human pose estimation from radio frequency (RF) signals. Different from existing methods that predict human poses from RF signals at the signal level directly, we consider the structure difference between the RF signals and the human poses, propose a transformation of the RF signals to the pose domain at the feature level based on the optimal transport (OT) theory, and generate human poses from the transformed features. To evaluate RFPose-OT, we build a radio system and a multi-view camera system to acquire the RF signal data and the ground-truth human poses. The experimental results in a basic indoor environment, an occlusion indoor environment, and an outdoor environment demonstrate that RFPose-OT can predict 3D human poses with higher precision than state-of-the-art methods.

Radio frequency (RF) imaging has been of interest for years due to its ability to create images of targets without being affected by weather and light conditions. The existing heuristic solutions such as back projection (BP), suffer from the sidelobe interference due to limited antenna aperture. In this paper, we propose a super-resolution 3-D imaging algorithm to enhance imaging performance. Mathematically, the reconstruction problem is an inverse problem that can be formulated as an optimization problem. The low-rank property of the object is exploited to regularize the imaging problem by nuclear norm minimization. We also develop a coarse-to-fine scan structure to reduce the computational complexity of the proposed algorithm. We evaluate the proposed algorithm with both simulations and measurements. With a frequency band range from 2.7-4.1 GHz and a$16 \times 6$multiple-input-multiple-output (MIMO) antenna array, simulation results show high imaging quality with a median boundary keypoint precision of 2 cm, and experimental results validate the feasibility of the proposed algorithm in a real-world environment.

Liveness detection has been widely applied in face authentication systems to combat malicious attacks. However, existing methods purely depending on visual frames become vulnerable once visual perception is not reliable. The emerging face spoof and forge techniques urge the systems to exploit the defensive potential of non-visual modalities. To tackle this challenge, we introduce SonarGuard, a system combining ultrasonic and visual information to achieve robust liveness detection on mobile devices. More specifically, SonarGuard simultaneously extracts micro-doppler signatures from ultrasound reflections and motion trajectories from video frames both corresponding to the user’s lip movement. To further confirm the collected ultrasonic and visual information is not derived from malicious audio/video attacks, we consolidate the system via introducing a cross-modal matching mechanism, which demands the inherent consistency between these two modalities. Extensive experiments on a new dataset collected with existing mobile devices demonstrate that the proposed system could achieve average classification error rate of 0.91% under presentation attacks. This result indicates that SonarGuard can boost the security of face authenfication systems in real world usage without additional hardware modification.

Human gesture recognition using millimeter-wave (mmWave) signals provides attractive applications including smart home and in-car interfaces. While existing works achieve promising performance under controlled settings, practical applications are still limited due to the need of intensive data collection, extra training efforts when adapting to new domains, and poor performance for real-time recognition. In this paper, we propose DI-Gesture, a domain-independent and real-time mmWave gesture recognition system. Specifically, we first derive signal variations corresponding to human gestures with spatial-temporal processing. To enhance the robustness of the system and reduce data collecting efforts, we design a data augmentation framework for mmWave signals based on correlations between signal patterns and gesture variations. Furthermore, a spatial-temporal gesture segmentation algorithm is employed for real-time recognition. Extensive experimental results show DI-Gesture achieves an average accuracy of 97.92%, 99.18%, and 98.76% for new users, environments, and locations, respectively. We also evaluate DI-Gesture in challenging scenarios like real-time recogntion and sensing at extreme angles, all of which demonstrates the superior robustness and effectiveness of our system.

Fashion compatibility predictions have obtained a lot of attention recently. Mining the compatibility between fashion items in an outfit is different from learning the visual similarity, since this relationship is more delicate. Decomposing the outfit compatibility into pairwise item matching is a popular way to treat the problem. However, in most existing methods, the items are matched without considering the context, i.e, the remaining items in the outfit. Recent efforts have been made to learn the underlying high order relationships among items by treating the outfit as a whole. These models could be sensitive to the properties of different datasets, and the item representations in these models are not as compact as those in the pairwise models. In this paper, we propose a context conditioning embedding approach to learn compact representations that preserve the shared information among items under the existence of contextual items. We use two different spaces, the general and the contextual spaces, to embed items, where the representation in the contextual space contains information from the context. We employ mutual information maximization for model learning, which is shown to be more appropriate for the problem. With extensive experiments, we show that our model achieves superior performance than other state-of-the-art methods.

Fashion outfit recommendation has attracted lots of attention recently. The problem becomes even more interesting and challenging when considering users’ personalized fashion preferences. Although existing works have successfully improved the recommendation accuracy, the efficiency issue of computation and storage is still under-investigated and often ignored. In this paper, we propose a discrete content-based tensor factorization model that maps items and user to binary codes for efficient fashion recommendation. We introduce a probabilistic perspective for learning to hash, where the binary codes are sampled from a set of underlying Bernoulli variables. To demonstrate the effectiveness of our model, we collect a large-scale outfit dataset together with user label information from a fashion-focused social website. Extensive experiments on our dataset show that the proposed model outperforms other state-of-the-art methods.

In this paper, we propose a radio-assisted human detection framework by incorporating radio information into the state-of-the-art detection methods, including anchor-based one-stage detectors and two-stage detectors. We extract the radio localization and identifier information from the radio signals to assist the human detection, due to which the problem of false positives and false negatives can be greatly alleviated. For both detectors, we use the confidence score revision based on the radio localization to improve the detection performance. For two-stage detection methods, we propose to utilize the region proposals generated from radio localization rather than relying on region proposal network (RPN). Moreover, with the radio identifier information, a non-max suppression method with the radio localization constraint has also been proposed to further suppress the false detections and reduce miss detections. Experiments on the simulative Microsoft COCO dataset and Caltech pedestrian datasets show that the mean average precision (mAP) and the miss rate of the state-of-the-art detection methods can be improved with the aid of radio information. Finally, we conduct experiments in real-world scenarios to demonstrate the feasibility of our proposed method in practice.

Human silhouette segmentation, which is originally defined in computer vision, has achieved promising results for understanding human activities. However, the physical limitation makes existing systems based on optical cameras suffer from severe performance degradation under low illumination, smoke, and/or opaque obstruction conditions. To overcome such limitations, in this paper, we propose to utilize the radio signals, which can traverse obstacles and are unaffected by the lighting conditions to achieve silhouette segmentation. The proposed RFMask framework is composed of three modules. It first transforms RF signals captured by millimeter wave radar on two planes into spatial domain and suppress interference with the signal processing module. Then, it locates human reflections on RF frames and extract features from surrounding signals with human detection module. Finally, the extracted features from RF frames are aggregated with an attention based mask generation module. To verify our proposed framework, we collect a dataset containing804,760radio frames and402,380camera frames with human activities under various scenes. Experimental results show that the proposed framework can achieve impressive human silhouette segmentation even under the challenging scenarios (such as low light and occlusion scenarios) where traditional optical-camera-based methods fail. To the best of our knowledge, this is the first investigation towards segmenting human silhouette based on millimeter wave signals. We hope that our work can serve as a baseline and inspire further research that perform vision tasks with radio signals. The dataset and codes will be made in public.

This paper demonstrates human synthesis based on the Radio Frequency (RF) signals, which leverages the fact that RF signals can record human movements with the signal reflections off the human body. Different from existing RF sensing works that can only perceive humans roughly, this paper aims to generate fine-grained optical human images by introducing a novel cross-modal RFGAN model. Specifically, we first build a radio system equipped with horizontal and vertical antenna arrays to transceive RF signals. Since the reflected RF signals are processed as obscure signal projection heatmaps on the horizontal and vertical planes, we design a RF-Extractor with RNN in RFGAN for RF heatmap encoding and combining to obtain the human activity information. Then we inject the information extracted by the RF-Extractor and RNN as the condition into GAN using the proposed RF-based adaptive normalizations. Finally, we train the whole model in an end-to-end manner. To evaluate our proposed model, we create two cross-modal datasets (RF-Walk&RF-Activity) that contain thousands of optical human activity frames and corresponding RF signals. Experimental results show that the RFGAN can generate target human activity frames using RF signals. To the best of our knowledge, this is the first work to generate optical images based on RF signals.

The past years have witnessed increasing research interest in achieving passive human localization with commodity WiFi devices. However, due to the fundamental limited spatial resolution of WiFi signals, it is still very difficult to achieve accurate localization with existing commodity WiFi devices. To tackle this problem, in this paper, we propose to exploit the degree of freedom provided by the Intelligent Reflecting Surface (IRS), which is composed of a large number of controllable reflective elements, to modulate the spatial distribution of WiFi signals and thus break down the spatial resolution limitation of WiFi signals to achieve accurate localization. Specifically, in the single-person scenario, we derive the closed-form solution to optimally control the phase shift of the IRS elements. In the multi-person scenario, we propose a Side-lobe Cancellation Algorithm to eliminate the near-far effect to achieve accurate localization of multiple persons in an iterative manner. Extensive simulation results demonstrate that without any change to the existing WiFi infrastructure, the proposed framework can locate multiple moving persons passively with sub-centimeter accuracy under multipath interference and random noise.

Human gesture recognition using millimeter wave (mmWave) signals provides attractive applications including smart home and in-car interfaces. While existing works achieve promising performance under controlled settings, practical applications are still limited due to the need for intensive data collection, extra training efforts when adapting to new domains (i.e. environments, persons and locations) and poor performance for real-time recognition. In this paper, we propose DI-Gesture, a domain-independent and real-time mmWave gesture recognition system. Specifically, we first derive the signal variation corresponding to human gestures with spatial-temporal processing. To enhance the robustness of the system and reduce data collecting efforts, we design a data augmentation framework based on the correlation between signal patterns and gesture variations. Furthermore, we propose a dynamic window mechanism to perform gesture segmentation automatically and accurately, thus enabling real-time recognition. Finally, we build a lightweight neural network to extract spatial-temporal information from the data for gesture classification. Extensive experimental results show DI-Gesture achieves an average accuracy of 97.92%, 99.18% and 98.76% for new users, environments and locations, respectively. In real-time scenario, the accuracy of DI-Gesture reaches over 97% with an average inference time of 2.87ms, which demonstrates the superior robustness and effectiveness of our system.

The monitoring of blood pressure is critical for the prevention, diagnosis and treatment of cardiovascular diseases. However, existing methods require physical contact between human body and sensor, which are not suitable for long-term monitoring. In this paper, we propose a contactless blood pressure monitoring system, mmBP, using millimeter wave radar. Specifically, we first separate signals reflected from different spatial locations by coherently combining the signals on different antennas. Then, we locate and extract the arterial pulse using convolutional neural network (CNN) assisted template matching with location tracking. Finally, we design an encoder-decoder neural network to derive the blood pressure information from the extracted signal. Experimental results on 20 subjects show that the measurement deviation rate is 9.00% and 3.69% for systolic and diastolic blood pressure, which demonstrates the feasibility and effectiveness of the proposed system.

Fall is a severe health threat for elders’ health care. While existing systems could achieve promising performance under specific scenarios, the required computing resources are usually not affordable, which is not applicable for real-time detection. In this paper, we propose mmFall, a real time fall detection system using millimeter wave signal which can achieve impressive accuracy with low computation complexity. Specifically, we first extract the signal variation corresponding to human activity with spatial-temporal processing. To enhance the system performance and robustness, we perform data augmentation by shifting, flipping, extracting and interpolating the signal. Finally, we design a light-weight convolutional neural network to achieve real-time fall detection. Extensive experimental results demonstrate that the pro-posed system could achieve state-of-the-art performance with limited computation complexity.

Compared with optical imaging, radio frequency (RF) imaging enables object imaging in an all-weather, privacy-preserving, and cost-effective way. However, the existing heuristic solutions such as back projection (BP) and range migration (RM), suffer from the sidelobe interference due to limited antenna aperture. In this paper, we propose a super-resolution 3-D imaging algorithm to enhance imaging performance. Mathematically, the reconstruction problem is an inverse problem that can be formulated as an optimization problem. The low-rank property of the object is exploited to regularize the imaging problem by nuclear norm minimization. We evaluate the proposed algorithm with both simulations and measurements. With a frequency band range from 2.7-4.1 GHz and a 16×6 multiple-input-multiple-output (MIMO) antenna array, simulation results show high imaging quality with a median boundary keypoint precision of 2 cm, and experimental results validate the feasibility of the proposed algorithm in a real-world environment.

Radio-frequency (RF) based human sensing technologies, due to their great practical value in various applications and privacy-preserving nature, have gained tremendous attention in recent years. However, without fully exploiting the characteristics of radio signals, the performance of existing methods are still limited. First, RF features of the moving human body have different representations in dimensions such as channel and scale, which is challenging when performing feature fusion. Besides, the human body is specularly reflective with respect to the radar, which means the human body cannot be fully captured by a single RF snapshot. Therefore, the radar signal reflected by the human body is sparse and incomplete, which is difficult to extract high-quality features for 3D human pose estimation. In this paper, we present the RF-based Pose Machines (RPM), a novel framework which can generate 3D skeletons from RF signals. Considering the characteristics of RF signals, RPM includes several modules to overcome the challenges. Firstly, a Multidimensional Feature Fusion (MFF) backbone is designed to effectively fuse radio signals based on the channels' correlation and maintain high-quality feature via a multi-scale fusion block. A Spatio-Temporal Attention network is then designed to reconstruct 3D skeletons by modeling the non-local spatio-temporal relationships. To evaluate the performance of our RPM framework, we construct a large-scale dataset of synchronized 3D skeletons and RF signals, RFSkeleton3D. Our experimental results show that RPM locates 3D key points of the human body with an average error of 5.71cm and maintains its performance in new environments with occlusion or bad illumination. The dataset and codes will be made in public.

This paper tackles a new challenge: how to generate human pose images from wireless signals? Although the optical camera can capture optical images, it is easily restricted by bad lighting. The wireless signals do not rely on visible lights. However, the low-resolution characteristics make previous works can only generate a rough skeleton of human posture, missing a lot of detailed visual information, such as background, appearance, etc. Since the visual information usually maintains unchanged for a period and the wireless signals can capture the movements of the human, in this paper, we propose a framework to generate the target human pose images by combining the wireless signals with an initial optical image. We utilize multiple wireless devices to collect the WiFi signals and a camera to capture the initial optical image. Then a data preprocessing component is designed to preprocess the wireless and vision data. Finally, a deep learning model learns to generate the human pose images from the processed wireless signals and the initial optical image. We conduct experiments to evaluate our proposed framework and results show that it achieves higher accuracy than the state-of-the-art WiFi-based pose estimation method and better visual quality than the state-of-the-art human generation method.

By leveraging the mechanical motions on the body surface conducted by physiological activities, many works have achieved radio-frequency(RF)-based physiological sensing. However, previous works generally simplify the model on both the mechanism of physiological motion and the signal propagation around the human body, which leads to the loss of valuable information. In this paper, we introduce the concept of micro-motion(MM) camera to provide a more cognitive imaging modality to observe torso surface motion comprehensively so as to dynamically image the motions of the breath and cardiac activities. We develop a sub-6G MMCamera prototype system. The camera functionality is implemented to prove the concept novelty from the view of respiratory-cardiovascular system monitoring. Our result shows that the proposed system could provide fine-grid torso surface motion imaging with breath and cardiac activities distributed over the entire thorax and abdomen.

The major obstacle for learning-based RF sensing is to obtain a high-quality large-scale annotated dataset. However, unlike visual datasets that can be easily annotated by human workers, RF signal is non-intuitive and non-interpretable, which causes the annotation of RF signals time-consuming and laborious. To resolve the rapacious appetite of annotated data, we propose a novel unsupervised representation learning (URL) framework for RF sensing, RF-URL, to learn a pre-training model on large-scale unannotated RF datasets that can be easily collected. RF-URL utilizes a contrastive framework to mind the gap between signal-processing-based RF sensing and learning-based RF sensing. By constructing positive and negative pairs through different signal processing representations, RF-URL seamlessly integrates the existing RF signal processing algorithms into the learning-based networks. Moreover, the RF-URL is carefully designed to take into account the asymmetric characteristics of different RF signal processing representations. We show that RF-URL is universal to a variety of RF sensing tasks by evaluating RF-URL in three typical RF sensing tasks (human gesture recognition, 3D pose estimation and silhouette generation) based on two general RF devices (WiFi and radar). All experimental results strongly demonstrate that RF-URL takes an important step towards learning-based solutions for large-scale RF sensing applications.

Vibration is a widespread physical phenomenon that often carries important information such as the internal state of the devices. Thus, vibration measurement is of great importance in the field of modern engineering and has drawn much attention. While achieving promising performance, existing methods fail when the vibration amplitude is tiny, e.g, smaller than 50 um. To address such a challenge, in this paper, we propose a contactless method with deep learning, denoted as DeepVib, to sense the tiny vibration using millimeter wave radar. Specifically, DeepVib first makes full advantage of the physical characteristics of the vibrating object and combines Range-Doppler FFT to find the range bin of vibrating objects. Then, DeepVib trains a denoising neural network using a large amount of simulated data, which takes the noisy sample points as input and outputs the denoised data with better SNR. Finally, the vibration status is recovered through the phase variation of the extracted signal. Simulation results show that DeepVib achieves over 40% improvement in measuring um-level amplitudes with over 5x faster processing time, while real experimental results show that DeepVib achieves a mean amplitude error of 2.1 um for the 100um-amplitude vibration.

We observed that remarkable and impressive performance on image-based human pose estimation have been achieved by deep Convolutional Neural Networks (CNN). Nevertheless, directly applying these image-based models on videos is not only computionally intensive, but also may cause jitter and loss. The main reason is that the image-based models purely focus on the local features of individual frames and totally ignore the temporal information among adjacent frames. Some existing methods are proposed to address the temporal coherency issue. However, these methods need to be designed carefully and cannot be combined with existing image-based methods. In this paper, we propose a simple yet effective module to refine the estimated pose by exploiting the temporal coherency among the heatmaps of adjacent frames, which can be easily inserted into image-based networks as a plug-in. We show that the temporal coherency issue among the heatmap frames could be re-formulated as a graph path selection optimization problem. Moreover, to speed up the refinement process, we propose a hierarchical graph optimization to achieve the refinement from coarse to fine. Experimental results on two large-scale video pose estimation benchmarks show that our module can improve the performance with little speed loss when combined with image-based methods as an efficient plug-in.

Passive non-line-of-sight (NLOS) imaging has drawn great attention in recent years. However, all existing methods are in common limited to simple hidden scenes, low-quality reconstruction, and small-scale datasets. In this paper, we propose NLOS-OT, a novel passive NLOS imaging framework based on manifold embedding and optimal transport, to reconstruct high-quality complicated hidden scenes. NLOS-OT converts the high-dimensional reconstruction task to a low-dimensional manifold mapping through optimal transport, alleviating the ill-posedness in passive NLOS imaging. Besides, we create the first large-scale passive NLOS imaging dataset, NLOS-Passive, which includes 50 groups and more than 3,200,000 images. NLOS-Passive collects target images with different distributions and their corresponding observed projections under various conditions, which can be used to evaluate the performance of passive NLOS imaging algorithms. It is shown that the proposed NLOS-OT framework achieves much better performance than the state-of-the-art methods on NLOS-Passive. We believe that the NLOS-OT framework together with the NLOS-Passive dataset is a big step and can inspire many ideas towards the development of learning-based passive NLOS imaging. Codes and dataset are publicly available (https://github.com/ruixv/NLOS-OT).

The state-of-the-art radio-frequency (RF) devices limit the deployment of extremely high-order quadrature amplitude modulation (QAM) formats (e.g., 16384-QAM) to meet the high-capacity demand on microwave backhaul links. This paper turns to faster-than-Nyquist (FTN) transmission using lower-order constellations and lower-cost RF devices as a solution to the demand. To realize low-complexity interference cancellation, we pre-equalize the FTN-induced inter-symbol interference at the transmitter by using Tomlinson-Harashima precoding (THP), while at the receiver suppressing the phase noise (PHN) generated by the RF local oscillators with pilot symbol assisted approaches. However, the THP may distort the pilots, which degrades the performance of PHN compensation. To resolve this problem, we propose two pilot designs that are distortion-free to precisely estimate the PHN samples. Moreover, we derive a closed-form expression of the symbol detection signal-to-noise ratio (SNR), in terms of the THP-FTN waveform parameters. With the SNR expression, a waveform optimization procedure is developed to maximize the SNR and enhance the achievable FTN capacity. The proposed scheme is validated in the simulated platform of 4096-QAM microwave link. The results demonstrate that the FTN signaling achieves the system capacity equivalent to that of the 16384-QAM Nyquist signaling with an SNR gain of 5.8 dB.

The multimedia community has recently seen a tremendous surge of interest in the fashion recommendation problem. A lot of efforts have been made to model the compatibility between fashion items. Some have also studied users’ personal preferences for the outfits. There is, however, another difficulty in the task that hasn’t been dealt with carefully by previous work. Users that are new to the system usually only have several (less than 5) outfits available for learning. With such a limited number of training examples, it is challenging to model the user’s preferences reliably. In this work, we propose a new solution for personalized outfit recommendation that is capable of handling this case. We use a stacked self-attention mechanism to model the high-order interactions among the items. We then embed the items in an outfit into a single compact representation within the outfit space. To accommodate the variety of users’ preferences, we characterize each user with a set of anchors, i.e. a group of learnable latent vectors in the outfit space that are the representatives of the outfits the user likes. We also learn a set of general anchors to model the general preference shared by all users. Based on this representation of the outfits and the users, we propose a simple but effective strategy for the new user profiling tasks. Extensive experiments on large scale real-world datasets demonstrate the performance of our proposed method.

In this paper, we propose BreathTrack2.0, a contactless breath tracking system based on commodity WiFi devices. Breath-Track2.0 could track the detailed breath status through the phase variation of channel state information(CSI). To achieve this, BreathTrack2.0 first combines the signal on multiple antennas and subcarriers with a joint Angle of Arrival(AoA) and Time of Flight(ToF) beamformer to strengthen the signal from human. Then, it utilizes the fact that the phase offsets introduced by the hardware imperfection are the same among different propagation paths to cancel the phase offsets. Finally, the phase variation of the extracted human signal is utilized to estimate the breath rate and indicate the detailed breath status. Real world experiments demonstrate that the proposed system could estimate the breath rate with the median accuracy of over 99% and could track the detailed status of human breath.

Indoor human speed estimation is critical to in-home health monitoring of elderly people since it can provide the moving status of the human. Contactless indoor speed estimation with radio signals is challenging due to the complicated relationship between the speed of moving human and radio signals. In this article, we propose an indoor speed estimation framework, SpeedNet, to estimate the speed from the radio signals. Specifically, SpeedNet first extracts the dominant path signal reflected from the human through the beamforming technique. Then, SpeedNet obtains the doppler frequency shift (DFS) corresponding to the moving human by analyzing the short-time Fourier transform (STFT) spectrogram of the dominant path signal. Finally, SpeedNet trains a deep neural network composed of convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to utilize the spatial and temporal features of DFS to estimate the speed of moving human. The experimental results show that SpeedNet can estimate the human moving speed with an average accuracy of 96.33% in a typical indoor environment, which is better than the state-of-the-art approaches.

In this article, we propose a human sensing system with radio signals, MTrack, for in-home healthcare, which is capable of tracking the trajectories of moving persons and vital signs of static persons under the multiperson scenarios. To achieve this, we implement a multiantenna wideband system that can provide high-resolution Angle of Arrival (AoA) and Time of Flight (ToF). A 2-D beamformer is utilized to transform the raw radio signals into the AoA-ToF domain. To track the trajectories of moving persons, we leverage the movement of persons to cancel static multipaths and propose a path selection algorithm to estimate the locations of human and suppress the interferences from dynamic multipaths. To track the vital signs of static persons, we utilize the breath of static persons to eliminate static multipaths and propose a correlation-based algorithm to eliminate dynamic multipaths. Extensive experiments show that the proposed MTrack system is capable of tracking multiple moving persons with subdecimeter level accuracy, and can estimate the breath and heartbeat rate of static persons with the median accuracy of 99.8% and 98.46%, respectively.

In social network, the existence of malicious users can create lots of detrimental consequences. To diminish their negative influences, it is necessary for rational users to identify and interact with each neighbor carefully to protect themselves from malicious ones. Therefore, it is crucial to establish a rule for users’ interaction in order to mitigate malicious users’ influences. In this paper, we propose a smart evolution model based on evolutionary game theory by introducing the reputation mechanism. The model takes into account both current reputation and instant incentives during users’ decision-making process. On the basis of whether users share reputation values with others, we introduce schemes without reciprocity principle and with the indirect reciprocity principle respectively. With the social norm and reputation updating policy, we theoretically analyze the evolutionary dynamics and corresponding ESSs by explicitly considering the effects of malicious users. Finally, simulations based on synthetic networks and real-world data are conducted to validate the effectiveness of the proposed smart evolution model.

Indoor human speed estimation, which can provide the moving status of human, is attracting considerable critical attention, especially in the field of in-home health monitoring of elderly people. Since the relationship between the moving of human and radio signal is very complicated, indoor speed estimation via radio signals is non-trivial and challenging. To address the challenge, in this paper, we propose a SpeedNet framework to estimate the speed of moving human from the radio signals. Specifically, SpeedNet first utilizes the beamforming technique to extract the dominant path signal reflected from individuals. Then, with short time Fourier transform (STFT), SpeedNet analyzes the spectrogram of the dominant path signal and obtains the doppler frequency shift (DFS) that corresponds to the moving human. Finally, SpeedNet exploits the spatial and temporal features of the DFS through a deep neural network, which consists of convolutional neural networks (CNN) and long short-term memory networks (LSTM), to estimate the speed of moving human. Extensive experiments show that compared with the state-of-the-art approaches, SpeedNet can achieve much better speed estimation performance with a mean absolute percentage error (MAPE) of 3.67% in a typical indoor environment.

In social networks, to better understand the avalanche of information flow over networks and to investigate its impact on economy and our social life, it is of crucial importance to model and analyze the information diffusion process. To address the existence of "super users" in social networks who have higher social status and potentially larger influence, we propose a graphical evolutionary game theoretic framework to investigate the impact of such super users and their strategy update rules on information propagation. We analyze the evolutionary dynamics and the stable states. Simulation results are consistent with our theoretical analysis, and demonstrate that strategy update rule is the critical factor that influences the stable states of the information diffusion process.

Indoor human sensing, recognition, and detection, as key enablers of building smart environments, such as smart home, smart retail, and smart museum, have gained tremendous attention in recent years. Compared with traditional vision-based and wearable sensor-based solutions, radio-frequency (RF)-based approaches are more desirable with the contactless and nonline-of-sight nature. Among all RF-based approaches, WiFi-based approaches have been the focus of many researchers because of the ubiquitous availability and cost efficiency. In this article, we present a survey of recent advances in WiFi vision problems, i.e., sensing, recognition, and detection by utilizing the channel state information (CSI) of the commodity WiFi devices. We focus on nine key applications of smart environments, including WiFi imaging, vital sign monitoring, human identification, gesture recognition, gait recognition, daily activity recognition, fall detection, human detection, and indoor positioning. Such a survey can help readers have an overall understanding of sensing, recognition, and detection with commodity WiFi, and thus expedite the development of smart environments.

Multiuser video transmission, where the server transmits videos to multiple users that require different contents at the same time, becomes more and more popular with the development of wireless communication technology. One key problem in multiuser video transmission is how to optimally allocate system resources such as transmission power and channels to multiple users to achieve the best system performance. To resolve the problem, in this paper, we propose an uncoded multiuser video streaming system, which exploits diversities of video contents and channel conditions of multiple users. We first solve the channel assignment problem with known power allocation by taking into account the intra-block energy diffusion and inter-block energy aliasing. Then, with the obtained channel assignment, we derive a closed-form solution to the multiuser power allocation optimization problem. Finally, we conduct simulations to evaluate the proposed uncoded multiuser video streaming system by comparing with three other approaches, and the simulation results show that the proposed method can achieve the best system performance.

The various offsets existed in the commodity WiFi devices greatly limit the use of ubiquitous WiFi signals for indoor applications. In this paper, we focus on the estimation and compensation of the residual carrier frequency offset (CFO) for the commodity WiFi devices. Specifically, we consider a distorted channel state information (CSI) model by taking into consideration various CSI errors such as packet detection delay (PDD) and CFO. We propose a multiscale sparse recovery algorithm to get rid of the effect of PDD and extract the carrier frequency component out of CSI. Then, we formulate the residual CFO estimation as a spectrum estimation problem and utilize the MUSIC algorithm to estimate the residual CFO. Real experiments and numerical simulations are conducted to evaluate the performance of the proposed method. The experimental results and simulation results show that the residual CFO is time-varying, and compared with existing methods, the proposed method can better estimate and compensate the residual CFO, and thus achieve better results.

With the rapid growth of fashion-focused social networks and online shopping, intelligent fashion recommendation is now in great needs. Recommending fashion outfits, each of which is composed of multiple interacted clothing and accessories, is relatively new to the field. The problem becomes even more interesting and challenging when considering users' personalized fashion style. Another challenge in a large-scale fashion outfit recommendation system is the efficiency issue of item/outfit search and storage. In this paper, we propose to learn binary code for efficient personalized fashion outfits recommendation. Our system consists of three components, a feature network for content extraction, a set of type-dependent hashing modules to learn binary codes, and a matching block that conducts pairwise matching. The whole framework is trained in an end-to-end manner. We collect outfit data together with user label information from a fashion-focused social website for the personalized recommendation task. Extensive experiments on our datasets show that the proposed framework outperforms the state-of-the-art methods significantly even with a simple backbone.

Modeling and analysis of information diffusion over networks is of crucial importance to better understand the avalanche of information flow over social networks and to investigate its impact on economy and our social life. Different from prior works that study rational behavior in information diffusion, we focus on "irrational users e.g., those who always intentionally forward fake news even when they know it contains false information. We extend the graphical evolutionary game model for information diffusion, and analyze the impact of such irrational behavior on information propagation. Our simulation results on synthetic networks are consistent with our analytical results, and they show that even a few irrational users can significantly increase the number of users who adopt the forwarding strategy.

Multiuser video transmission, where the server transmits videos to multiple users that request different contents at the same time, becomes more and more popular with the development of wireless communication technology. One key problem in multiuser video transmission is how to optimally allocate the system resources such as transmission power and channels to multiple users to achieve the best system performance. To resolve the problem, in this paper, we propose an uncoded multiuser video streaming system, which exploits diversities of video contents and channel conditions of multiple users. We first solve the channel assignment problem with known power allocation by taking into account the intra-block energy diffusion and inter-block energy aliasing. Then, with the obtained channel assignment, we derive a closed-form solution to the multiuser power allocation optimization problem. Finally, we conduct simulations to evaluate the proposed uncoded multiuser video streaming system by comparing with three other approaches, and simulation results show that the proposed method can achieve the best system performance.

The various offsets existed on the commodity WiFi devices greatly limit the use of ubiquitous WiFi signals for indoor applications. In this paper, we focus on the estimation and compensation of the residual carrier frequency offset (CFO) for the commodity WiFi devices. Specifically, we introduce a distorted channel state information (CSI) model by taking into consideration various CSI errors such as packet detection delay (PDD) and CFO. We propose a multiscale sparse recovery algorithm to get rid of the effect of PDD and extract the carrier frequency component out of CSI. Then, we formulate the residual CFO estimation as a spectrum estimation problem and propose to utilize the MUSIC algorithm to estimate the residual CFO. Real experiments are conducted to evaluate the performance of the proposed method. The experimental results show that the residual CFO is time-varying, and compared with existing methods, the proposed method can better estimate and compensate the residual CFO, and thus achieve better results.

Fashion recommendation is the task of suggesting a fashion item that fits well with a given item. In this work, we propose to automatically synthesis new items for recommendation. We jointly consider the two key issues for the task, i.e., compatibility and personalization. We propose a personalized fashion design framework with the help of generative adversarial training. A convolutional network is first used to map the query image into a latent vector representation. This latent representation, together with another vector which characterizes user's style preference, are taken as the input to the generator network to generate the target item image. Two discriminator networks are built to guide the generation process. One is the classic real/fake discriminator. The other is a matching network which simultaneously models the compatibility between fashion items and learns users' preference representations. The performance of the proposed method is evaluated on thousands of outfits composited by online users. The experiments show that the items generated by our model are quite realistic. They have better visual quality and higher matching degree than those generated by alternative methods.

To overcome the difficulties of charging the wireless sensors in the wild with conventional energy supply, more and more researchers have focused on the sensor networks with renewable generations. Considering the uncertainty of the renewable generations, an effective energy management strategy is necessary for the sensors. In this paper, we propose a novel energy management algorithm based on the reinforcement learning. By utilizing deep deterministic policy gradient (DDPG), the proposed algorithm is applicable for the continuous states and realizes the continuous energy management. We also propose a state normalization algorithm to help the neural network initialize and learn. With only one day's real solar data and the simulative channel data for training, the proposed algorithm shows excellent performance in the validation with about 800 days length of real solar data. Compared with the state-of-the-art algorithms, the proposed algorithm achieves better performance in terms of long-term average net bit rate.

In this paper, we propose a contact-free breath tracking system, BreathTrack, to track the status of breath using the off-the-shelf WiFi devices. BreathTrack exploits the phase variation of the channel state information (CSI) to track human breath. To resolve the phase distortions introduced by the hardware imperfection of the commodity WiFi chips, BreathTrack utilizes both the hardware and software correction methods. The time-invariant PLL phase offset is calibrated by the hardware correction using cables and splitters, while the time-varying carrier frequency offset, sampling frequency offset and packet detection delay are removed by the software corrections using the phase difference between the CSI at the receiver antennas and that at the reference antenna connected from the transmitter. Moreover, BreathTrack utilizes the sparse recovery method to find the dominant path in the multipath indoor environment and derive the corresponding complex attenuation coefficient. Then, the phase variation of the complex attenuation coefficient is utilized to extract the detailed breath status and the breath rate. Extensive experiments are conducted to show that BreathTrack could estimate the breath rate with the median accuracy of over 99% in most scenarios, and could track the detailed status of breath directly using the raw phase variation.

Non-orthogonal multiple access (NOMA) has been considered as a significant candidate technique for the next generation wireless communication to support high throughput and massive connectivity. It allows different users to be multiplexed on one channel through applying superposition coding at the transmitter and successive interference cancellation (SIC) at the receiver. To fully utilize the benefit of the NOMA technique, the key problem is how to optimally allocate resources, such as power and channels, to users to maximize the system performance. There have been some existing works on the power allocation for the single-carrier NOMA system. However, how to optimally assign channels in the multi-carrier NOMA system is still unclear. In this paper, we propose a deep reinforcement learning framework to allocate resources to users in a near optimal way. Specifically, we exploit an attention-based neural network (ANN) to perform the channel assignment. Simulation results show that the proposed framework can achieve better system performance, compared with the state-of-the-art approaches.

Buffering techniques have been commonly used in mobile video streaming systems to handle the bandwidth fluctuation and to mitigate the impact of the stochastic characteristic of wireless channels on a mobile user's quality of experience. However, it has been shown by measurement study that users tend to abort when watching videos with mobile devices, which results in a significant wastage of the video data in the buffer. Therefore, one important problem in mobile video streaming is how to manage the buffer at each mobile user. On the other hand, mobile users generally share the wireless media to download the video data, i.e., mobile users compete with each other for the bandwidth to download the video data. Thus, another important problem in mobile video streaming is how to allocate bandwidth among mobile users. In this paper, we propose to optimize the resource management, i.e., to design buffer management strategy at each mobile user and bandwidth allocation strategy among mobile users, for the multiuser mobile video streaming systems. Specifically, we optimize the long-term average total cost of data wastage and quality of experience of mobile users with certain constraints. By introducing virtual queues and employing the Lyapunov optimization theory, we transform the original optimization problem into the drift-plus-penalty minimization problem. Then, we adopt the primal decomposition to decouple the relationship among different mobile users, which decomposes the problem into a master problem with multiple subproblems. A one-dimension full search algorithm is applied to find the global optimal solution to each subproblem, and the subgradient descent algorithm is utilized to update the solution to the master problem. Finally, simulations are conducted to show that the proposed algorithm is effective for the buffer management and bandwidth allocation in a multiuser mobile video streaming system.

Collaborative filtering is widely used in recommendation systems. A user can get high-quality recommendations only when both the user himself/herself and other users actively participate, i.e., provide sufficient ratings. However, due to the rating cost, rational users tend to provide as few ratings as possible. Therefore, there exists a tradeoff between the rating cost and the recommendation quality. In this paper, we model the interactions among users as a game in satisfaction form and study the corresponding equilibrium, namely satisfaction equilibrium (SE). Considering that accumulated ratings are used for generating recommendations, we design a behavior rule which allows users to achieve an SE via iteratively rating items. We theoretically analyze under what conditions an SE can be learned via the behavior rule. Experimental results on Jester and MovieLens data sets confirm the analysis and demonstrate that, if all users have moderate expectations for recommendation quality and satisfied users are willing to provide more ratings, then all users can get satisfying recommendations without providing many ratings. The SE analysis of the proposed game in this paper is helpful for designing mechanisms to encourage user participation.

In this paper, we propose a contact-free breath tracking system, BreathTrack, to track the status of breath using the off-the-shelf WiFi devices by exploiting the phase variation of the channel state information(CSI). BreathTrack utilizes a reference antenna connected from the transmitter to resolve the phase distortions introduced by the hardware imperfection. Moreover, BreathTrack utilizes the sparse recovery method to find the dominant path in the multipath indoor environment and derive the corresponding complex attenuation coefficient. Then, the phase variation of the complex attenuation coefficient is utilized to extract the detailed breath status and the breath rate. Extensive experiments are conducted to show that BreathTrack could estimate the breath rate with the median accuracy of over 99% in most scenarios, and could track the detailed status of breath directly using the raw phase variation.

Deal selection on Groupon is a typical social learning and decision making process, where the quality of a deal is usually unknown to the customers. The customers must acquire this knowledge through social learning from other social medias such as reviews on Yelp. Additionally, the quality of a deal depends on both the state of the vendor and decisions of other customers on Groupon. How social learning and network externality affect the decisions of customers in deal selection on Groupon is our main interest. We develop a data-driven game-theoretic framework to understand the rational deal selection behaviors cross social medias. The sufficient condition of the Nash equilibrium is identified. A value-iteration algorithm is proposed to find the optimal deal selection strategy. We conduct a year-long experiment to trace the competitions among deals on Groupon and the corresponding Yelp ratings. We utilize the dataset to analyze the deal selection game with realistic settings. Finally, the performance of the proposed social learning framework is evaluated with real data. The results suggest that customers do make decisions in a rational way instead of following naive strategies, and there is still room to improve their decisions with assistance from the proposed framework.

Energy harvesting (EH) wireless communications have become more and more popular due to its capability of effectively reducing the battery replacement time. In this paper, we focus on decode-and-forward-based cooperative wireless communications with an EH relay. The stochastic characteristics of the harvestable energy and the wireless channel make it difficult to optimally manage the energy harvested at the relay for better communication performance. We formulate such a problem as an optimization by minimizing the long-term average symbol error rate (SER) subject to a battery constraint. Based on the Lyapunov optimization theory, we utilize the virtual queue technique and transform the optimization problem into a drift-plus-penalty minimization. Then, we conduct theoretic analysis of the optimal energy strategy derived by the proposed scheme. Specifically, we show the convexity of the driftplus-penalty minimization problem, and prove that the virtual queue is bounded and the battery constraint is always satisfied. We also show that the proposed optimal energy management strategy is limited by an upper bound that is independent of the operation time index, derive the closed-form expression for the asymptotical average SER, and analyze the corresponding diversity order and EH gain. Finally, simulation results using the real solar irradiance data show that the proposed algorithm can achieve much better performance in terms of both average SER and diversity order, compared with the Markov decision process-based method.

With the development and popularity of the renewable energy harvesting devices, the energy harvesting wireless sensor communications that can make use of the energy harvested from the nearby environments have gained more and more attentions. One key problem in the energy harvesting wireless sensor communications is the transmission strategy management, i.e., how to manage the transmission strategy at each time slot to optimize the transmission performance. In this paper, we propose to use Lyapunov optimization theory to maximize the expected good bits per packet transmission for the source node in an energy harvesting wireless communication system. Considering the channel and battery states, we adapt the transmission power and modulation type to achieve such a goal. The problem is formulated as an optimization where the objective function is the long-term average good bits per packet transmission and the constraints are the bounded long-term average battery level and bit error rate. To solve the optimization, we introduce virtual queues and employ the Lyapunov optimization theory to transform the optimization with long-term average format into optimizing the drift-plus-penalty problem. The drift-plus-penalty is further upper bounded with variables only related to current time slot, which greatly simplifies the optimization problem. Theoretic analysis is also conducted to show that the optimal solution is limited by an upper bound that is independent of the operation time index. Finally, simulation results with real solar irradiance data show that the proposed algorithm can achieve much better performance than existing approaches based on Markov decision process and water-filling.

Time-reversal (TR) wideband communication systems enjoy the spatial-temporal focusing effect in a rich-scattering environment. However, the performance degrades in the presence of carrier frequency offset (CFO). The impact of CFO can be mitigated by compensating the estimated CFO values obtained using CFO estimators. Yet, CFO estimators in literature cannot work well in wideband TR systems due to the fact that the normalized CFO values are very small and thus cannot be estimated accurately using conventional schemes. To address this issue, we propose four CFO estimators which are capable of accurate CFO estimations for wideband TR systems. The theoretical performances of the proposed estimators are analyzed. Additionally, realizing that phase wrapping might introduce severe bias into CFO estimations, we present the conditions on the system parameters so that phase wrapping can be avoided. Extensive simulations and experimental results demonstrate the superiority of the proposed methods.

Due to the unique spatial and temporal focusing effects, time-reversal (TR) communication can be utilized in the cloud radio access network (C-RAN), where it creates “tunneling effects” such that the traffic load in the front-haul links can be alleviated in both downlink and uplink. Although the basic TR waveforms are simple to use, and they cannot provide the optimal performance in some cases. Since the C-RAN is usually expected to serve massive wireless devices, the severe inter-user interference will limit the performance of the system, especially in the high signal-to-noise ratio region where the interference power dominates the noise power. In this paper, we propose to optimize both downlink and uplink transmissions in the TR-based C-RAN so as to alleviate the interference. In the downlink transmission, an optimal content-aware waveform design is proposed, so that the baseband units (BBUs) are able to combine both the channel information and the content information to suppress the interference. In the uplink transmission, an optimal receiver design algorithm is proposed, such that the BBUs can detect the symbols transmitted by the terminal devices more accurately by leveraging the channel information. We study the bit error rate performance of the proposed algorithms based on extensive measurements of the wireless channel in a real-world environment. Numerical results demonstrate the significant performance improvement over the basic TR transmission techniques and the traditional waveform design techniques.

Uncoded linear video transmission has recently attracted people's attention due to its capacity to provide robust and scalable transmission. However, in reality, with the fluctuation of the wireless channels, the received quality may not be good enough. In such a case, the data may need to be transmitted multiple times to exploit both the time and frequency diversities to improve the received quality. Such a problem has never been investigated in the literature of uncoded video transmission. To resolve the problem, in this paper, we propose a framework, named MCast, to utilize the time and frequency diversities to achieve high-quality linear video transmission. We study how to optimally allocate the power and assign the channels at each time slot to the source data such that the overall performance is maximized. Specifically, we first derive a closed-form optimal power allocation solution for any given channel assignment. With the optimal power allocation, we then propose a suboptimal channel assignment scheme, where we sort the channels with their gains and assign the channels one-by-one to the corresponding block that can reduce the most reconstruction error. Finally, we compare MCast system with four other systems that are based on Softcast and Parcast, and simulation results show that MCast system can achieve better performance in terms of both PSNR performance and visual quality.

Deal selection on Groupon is a typical social learning and decision making process, where the quality of a deal is usually unknown to the customers. The customers must acquire this knowledge through social learning from other social medias such as reviews on Yelp. Additionally, the quality of a deal depends on both the state of the vendor and decisions of other customers on Groupon. How social learning and network externality affect the decisions of customers in deal selection on Groupon is our main interest. We develop a data-driven game-theoretic framework to understand the rational deal selection behaviors cross social medias. The sufficient condition of the Nash equilibrium is identified. A value-iteration algorithm is proposed to find the optimal deal selection strategy. We conduct a year-long experiment to trace the competitions among deals on Groupon and the corresponding Yelp ratings. We utilize the dataset to analyze the deal selection game with realistic settings. Finally, the performance of the proposed social learning framework is evaluated with real data. The results suggest that customers do make decisions in a rational way instead of following naive strategies, and there is still room to improve their decisions with assistance from the proposed framework.

With the emergence of big data computing and analysis, cloud computing services become more and more popular, which has recently drawn researchers' great attentions to develop various new applications and mechanisms. In this paper, we consider the on-demand mechanism design in the infrastructure as a service (IaaS), including resource allocation and pricing issues under dynamic scenarios. Most of existing works on mechanism design assumed static and independent individual utility, while the cloud computing services are provided in a dynamic environment. To solve such problems, we start with analyzing the Google cluster-usage dataset to draw the statistical and stochastic characteristics of the IaaS consumers and providers. Based on the characteristics mined from real data, we propose a stochastic matching algorithm with Markov Decision Process (MDP), which aims at optimizing the long-term system efficiency, with its online version using Q-learning method to address the imperfect model estimation problem. We further design an efficient (EF), incentive compatible (IC), individual rational (IR) auction mechanism, which is an extension of traditional Vickrey-Clarke-Groves (VCG) mechanism. The proposed mechanism is studied under two application scenario: quality sensitive services, where unilateral MDP-VCG auction is implemented; and quality insensitive services, where MDP-VCG double auction is implemented. To verify the performance of our proposed mechanism, we conduct experiment using the Google dataset and show that the proposed MDP-based VCG auction mechanism can achieve EF, IC and IR properties simultaneously.

Energy harvesting wireless cooperative communications have become more and more popular in recent years. In this paper, we consider a two-way relay cooperative network, where the relay is an energy harvesting node and uses decode-and-forward (DF) or amplify-and-forward (AF) cooperation protocol. We formulate the energy management problem in such a network as an optimization problem, which minimizes the long-term average outages with a long-term average battery constraint. We then apply Lyapunov optimization to transform the long-term optimization problem into the drift-plus-penalty. We also conduct theoretic analysis of the proposed energy management strategy. Specifically, we prove that the long-term average battery constraint can be guaranteed with the proposed strategy and derive an upper bound of the average outages with the proposed strategy. Furthermore, we analyze theoretically the diversity order and energy harvesting gain of the proposed strategy with the DF and AF protocols. Finally, simulation results using real-solar irradiance data measured by the solar site in Elizabeth City University show that the outage performance and the diversity order of our algorithms are better than the existing MDP-based method.

In the big data era, it is vital to allocate the vast amount of data to various users efficiently. However, the data agents (data owners, collectors and users) are selfish and seek to maximize their own utilities instead of the overall system efficiency. In this paper, the data trading problem of a data market with multiple data owners, collectors and users is formulated and an iterative auction mechanism is proposed to coordinate the data trading. The proposed mechanism guilds the selfish data agents to trade data efficiently and avoids direct access of the agents' private information. We theoretically prove that the proposed mechanism can achieve the socially optimal operation point. Moreover, we demonstrate that the mechanism satisfies appealing economic properties such as individual rationality and weakly balanced budget. Simulations as well as real data experiments validate the theoretical properties of the mechanism.

In this work, we propose a novel wireless time-reversal indoor events detection system (TRIEDS). By leveraging the time-reversal (TR) technique to capture the changes of channel state information (CSI) in the indoor environment, TRIEDS enables low-complexity single-antenna devices that operate in the ISM band to perform through-the-wall multiple events detection. In TRIEDS, each indoor event is detected by matching the instantaneous CSI to a multipath profile in a training database. To validate the feasibility of TRIEDS and to evaluate the performance, we build a prototype that works on ISM band with carrier frequency being 5.4 GHz and a 125 MHZ bandwidth. Experiments are conducted to monitor the states of the indoor wooden doors. Experimental results show that with a single receiver (AP) and transmitter (client), TRIEDS can achieve a detection rate higher than 96:92% and a false alarm rate smaller than 3:08% under either line-of-sight (LOS) or non-LOS transmission.

To overcome the hidden terminal problem a secondary user (SU) may encounter, cooperative spectrum sensing (CSS) is proposed and gained much attention in the last decades. However, due to the selfish nature, SUs may not cooperate unconditionally as most previous works have assumed. Therefore, how to stimulate SUs to play cooperatively is an important issue. In this paper, we propose a reputation-based CSS incentive framework, where the cooperation stimulation problem is modeled as an indirect reciprocity game. In the proposed game, SUs choose how to report their sensing results to the fusion center (FC) and gain reputations, based on which they can access a certain amount of vacant licensed channels in the future. For the proposed game, we derive theoretically the optimal action rule, according to which the SU will truthfully report its result when the estimated average energy is equal to or higher than the given threshold and vice versa. The decision accuracy of the FC thereby can be greatly improved. Moreover, we derive the condition under which the optimal action rule is evolutionarily stable. Finally, simulation results are shown to verify the effectiveness of the proposed scheme.

Indoor positioning systems (IPSs) are attracting more and more attention from the academia and industry recently. Among them, approaches based on WiFi techniques are more favorable since they are built upon the WiFi infrastructures available in most indoor spaces. However, due to the bandwidth limit in mainstream WiFi systems, the IPS leveraging WiFi can hardly achieve centimeter localization accuracy under strong nonline-of-sight (NLOS) conditions which is common for indoor environment. In this paper, to achieve the centimeter-level accuracy, we present a WiFi-based IPS that exploits the frequency diversity via frequency hopping. In the offline phase, the system collects channel frequency responses (CFRs) from multiple channels and from a number of locations-of-interest. Then, the CFRs are post-processed to mitigate the synchronization errors as well as interference from other WiFi networks. Then, using bandwidth concatenation, the CFRs from multiple channels are combined into location fingerprints which are stored into a local database. During the online phase, CFRs are formulated into the location fingerprint and is compared against the fingerprints in the database via the time-reversal resonating strength (TRRS). Finally, the IPS determines the location according to the TRRS. Extensive experiment results demonstrate a perfect centimeter-level accuracy in an office environment with strong NLOS using only one pair of single-antenna WiFi devices.

Channel frequency response (CFR) is a fine-grained location-specific information in WiFi systems that can be utilized in indoor positioning systems (IPSs). However, CFR-based IPSs can hardly achieve an accuracy at the centimeter level due to the limited bandwidth in WiFi systems. To achieve such accuracy using WiFi devices, we propose an IPS that fully harnesses the spatial diversity in multiple-input-multiple-output WiFi systems, which leads to a much larger effective bandwidth than the bandwidth of a WiFi channel. The proposed IPS obtains CFRs associated with locations-of-interest on multiple antenna links during the training phase. In the positioning phase, the IPS captures instantaneous CFRs from a location to be estimated and compares it with the CFRs acquired in the training phase via the time-reversal resonating strength with residual synchronization errors compensated. Extensive experiment results in an office environment with a measurement resolution of 5 cm demonstrate that, with a single pair of WiFi devices and an effective bandwidth of 321 MHz, the proposed IPS achieves detection rates of 99.91% and 100% with false alarm rates of 1.81% and 1.65% under the line-of-sight (LOS) and non-LOS (NLOS) scenarios, respectively. Meanwhile, the proposed IPS is robust against environment dynamics. Moreover, experiment results with a measurement resolution of 0.5 cm demonstrate a localization accuracy of 1-2 cm in the NLOS scenario.

In this paper, we propose a novel wireless indoor events detection system, TRIEDS. By leveraging the time-reversal technique to capture the changes of channel state information (CSI) in the indoor environment, TRIEDS enables low-complexity single-antenna devices that operate in the ISM band to perform through-the-wall indoor multiple events detection. The multipath phenomenon denotes that the electromagnetic signals undergo different reflecting and scattering paths in a rich-scattering environment. In TRIEDS, each indoor event is detected by matching the instantaneous CSI to a multipath profile in a training database. To validate the feasibility of TRIEDS and to evaluate the performance, we build a prototype that works on ISM band with carrier frequency being 5.4 GHz and 125 MHz bandwidth. Experiments are conducted to detect the states of the indoor wooden doors. Experimental results show that with a single receiver access point and transmitter (client), TRIEDS can achieve a detection rate higher than 96.92% and a false alarm rate smaller than 3.08% under either line-of-sight (LOS) or non-LOS transmission.

In this paper, we show the existence of human radio biometrics and present a human identification system that can discriminate individuals even through the walls in a non-line-of-sight condition. Using commodity Wi-Fi devices, the proposed system captures the channel state information (CSI) and extracts human radio biometric information from Wi-Fi signals using the time-reversal (TR) technique. By leveraging the fact that broadband wireless CSI has a significant number of multipaths, which can be altered by human body interferences, the proposed system can recognize individuals in the TR domain without line-of-sight radio. We built a prototype of the TR human identification system using standard Wi-Fi chipsets with 3 × 3 multi-in multi-out (MIMO) transmission. The performance of the proposed system is evaluated and validated through multiple experiments. In general, the TR human identification system achieves an accuracy of 98.78% for identifying about a dozen of individuals using a single transmitter and receiver pair. Thanks to the ubiquitousness of Wi-Fi, the proposed system shows the promise for future low-cost low-complexity reliable human identification applications based on radio biometrics.

In this paper, we consider an energy harvesting (EH) two-way (TW) dual-relay network, including one non-EH relay and one EH relay equipped with a finite-sized battery. In the network, a space-time transmission protocol with space-time network coding is designed, and an optimal transmission policy for the EH relay is proposed by using a stochastic solar EH model. In this optimal policy, the long-term paired-wise error probability (PEP) of the system is minimized by adapting the EH relay's transmission power to the knowledge of its current battery energy, channel fading status, and causal solar EH information. The designed problem is formulated as a Markov decision process framework, and the conditional capability of the contribution to PEP by the EH relay is adopted as the reward function. We uncover a monotonic and limited difference structure for the expected total discounted reward. Furthermore, a non-conservative property and a monotonic structure of the optimal policy are revealed. Based on the optimal policy and its special structures, the expectation, lower and upper bounds, and asymptotic approximation of the PEP are computed and an interesting result on the system diversity performance is revealed, i.e., the full diversity order can be achieved only if the EH capability index, a metric to quantify the EH node's capability of harvesting and storing energy, approaches to infinity; otherwise, the EH diversity order is only equal to one, and the coding gain of the network is increasing with the EH capability index at this time. Furthermore, a full diversity criterion for the EH TW dual-relay network is proposed. Finally, computer simulations confirm our theoretical analysis and show that our proposed optimal policy outperforms other compared policies.

Data sharing is one critical step to implement data fusion, and how to encourage sensors to share their data is an important issue. In this paper, we propose a reputation-based incentive framework, where the data sharing stimulation problem is modeled as an indirect reciprocity game. In the proposed game, sensors choose how to report their results to the fusion center and gain reputations, based on which they can obtain certain benefits in the future. Taking the sensing and fusion accuracy into account, reputation distribution is introduced in the proposed game, where we prove theoretically the Nash equilibrium of the game and its uniqueness. Furthermore, we apply the proposed scheme to the cooperative spectrum sensing. We show that within an appropriate cost-to-gain ration, the optimal strategy for the secondary users is to report when the average received energy is above a given threshold and keep silence otherwise. Such an optimal strategy is also proved to be a desirable evolutionarily stable strategy. Finally, simulation results are shown to verify the theoretical results and demonstrate that compared with the existing schemes, our proposed scheme achieves better operating characteristic curve and higher system throughput with convincing performance on fairness.

Due to the unique spatial and temporal focusing effects, time-reversal (TR) communication can be utilized in the cloud radio access network, where it creates a tunneling effect such that the traffic load in the front-haul links can be alleviated in both downlink and uplink. Although the basic TR waveforms are simple to use, they cannot provide the optimal performance in some cases. While the cloud radio access network (C- RAN) is usually expected to serve massive wireless devices, the severe inter-user interference (IUI) might limit the performance of the system. In this paper, we propose to optimize both downlink and uplink transmissions so as to alleviate the interference. In the downlink transmission, optimal content-aware waveform design is proposed so that the baseband units (BBUs) are able to combine both the channel information and the symbol information to suppress the interference. In the uplink transmission, an optimal receiver design algorithm is proposed such that the BBUs can detect the symbols transmitted by the terminal devices (TDs) more accurately by leveraging the channel information. We study the BER performance of the proposed algorithm based on extensive measurements of the wireless channel in a real- world environment. Numerical results demonstrate the significant performance improvement over basic TR transmission techniques.

Massive MIMO has shown the great potential in improving the achievable rate with a very large number of antennas. However, several critical challenges in designing the analog front-end and coordinating the large-scale antenna array have to be carefully addressed. Does there exist an alternative that can achieve similar system performance to massive MIMO with a simpler design? In this paper, we show that by using time-reversal approach, with a sufficiently large bandwidth, one can harvest massive multipaths naturally existing in the rich-scattering environment to form a large number of virtual antennas to achieve the desired massive MIMO effect with a single antenna. We analyze the expected achievable rate of the time- reversal system with MMSE waveform. Furthermore, the corresponding asymptotic achievable rate under a massive multipath setting is derived. Experiment result based on real channel measurements shows that, even with only a single antenna, the time- reversal wideband system can achieve comparable performance as the massive MIMO system in terms of expected achievable rate.

Real-world networks are often cluttered and hard to organize. Recent studies show that most networks have the community structure, i.e., nodes with similar attributes form a certain community, which enables people to better understand the constitution of the networks. Hitherto, various community detection methods have been proposed in the literature yet none of them takes the strategic interactions among nodes into consideration. Additionally, many real-world observations of networks are noisy and incomplete, i.e., with some missing links or fake links, due to either technology constraints or privacy regulations. In this work, a game-theoretic framework of community detection is established, where nodes interact and produce links with each other in a rational way based on mutual benefits. Given the proposed game-theoretic generative models for communities, we use expectation maximization (EM) algorithm to detect communities. Simulations on synthetic networks and experiments on real-world networks demonstrate that the proposed detection method outperforms the state-of-the-art.

Indoor positioning systems (IPS) based on Wi-Fi signals are gaining popularity recently. IPS based on Received Signal Strength Indicator (RSSI) could only achieve a precision of several meters due to the strong temporal and spatial variation of indoor environment. On the other hand, IPS based on Channel State Information (CSI) drive the precision into the sub-meter regime with several access points (AP). However, the performance degrades with fewer APs mainly due to the limit of bandwidth. In this paper, we propose a Wi-Fi-based time-reversal indoor positioning system (WiFi-TRIPS) using the location-specific fingerprints generated by CSIs with a total bandwidth of 1 GHz. WiFi-TRIPS consists of an offline phase and an online phase. In the offline phase, CSIs are collected in different 10 MHz bands from each location-of-interest and the timing and frequency synchronization errors are compensated. We perform a bandwidth concatenation to combine CSIs in different bands into a single fingerprint of 1 GHz. In the online phase, we evaluate the time-reversal resonating strength using the fingerprint from an unknown location and those in the database for location estimation. Extensive experiment results demonstrate a perfect 5cm precision in an 20cm × 70cm area in a non-line-of-sight office environment with one link measurement.

To verify the authenticity of easily manipulated multimedia content, forensic researchers have proposed many techniques to estimate the processing history of given multimedia content. When multiple operations may be applied on multimedia content, a complete processing history would involve the information of not only what manipulation operations have been applied, but also in what order they were applied. However, there are few works considering the problem of detecting the order of operations. Moreover, due to the interplay among operations, the order of operations may not always be detectable. This leads to a fundamental question of when we can or cannot detect the order of operations. In this paper, we propose an information theoretical framework to answer this question. Specifically, we formulate the problem of detecting the order of operations into a multiple hypotheses testing problem. Then, we propose an information theoretical framework to characterize the relationship between the true hypothesis and the detected hypothesis. Under this framework, we propose a mutual information based criterion to determine the detectability of the order of operations. Furthermore, conditional fingerprints are defined in this framework to understand why the order of operations is not always detectable. The detection of the order of resizing and blurring is examined in this paper, where the order detection scheme has been proposed and the effectiveness of our framework has been demonstrated by simulations.

With the pervasive presence of massive smart devices, Internet of Things (IoT) is enabled by wireless communication technology. The devices in IoT usually have very diverse bandwidth capabilities and thus in need of many communication standards. To facilitate communications between these heterogeneous bandwidths of devices, middlewares have often been developed. However, they are often not suitable for resource-constrained scenario due to their complexity. It leads us to ask is there a unified approach that can support the communication between the devices with heterogeneous bandwidths? In this paper, we propose the time-reversal (TR) approach to answer such a question. A novel TR-based heterogeneous system is proposed, which can address the bandwidth heterogeneity and maintain the benefit of TR at the same time. Although there is an increase in complexity, it concentrates mostly on the digital processing of the access point (AP), which can be easily handled with more powerful digital signal processor (DSP). Since there is no middleware in the proposed system and the additional physical layer complexity concentrates on the AP side, the proposed TR approach better satisfies the requirement of low complexity and energy efficiency for terminal devices (TDs). We further conduct the theoretical analysis of the interference in the proposed system. Simulations show the bit-error-rate (BER) performance can be significantly improved with appropriate spectrum allocation. Finally, Smart Homes is chosen as an example of IoT applications to evaluate the performance of the proposed system.

The explosion of mobile data traffic calls for new efficient 5G technologies. Massive multiple-input multiple-output (MIMO), which has shown the great potential in improving the achievable rate with a very large number of antennas, has become a popular candidate. However, the requirement of deploying a large number of antennas at the base station may not be feasible in indoor scenarios due to the high implementation complexity. Does there exist a good alternative that can achieve similar system performance to massive MIMO for indoor environment? In this paper, we show that by using time-reversal (TR) signal processing, with a sufficiently large bandwidth, one can harvest the massive multipaths naturally existing in a rich-scattering environment to form a large number of virtual antennas and achieve the desired massive multipath effect with a single antenna. We answer the above question by analyzing the TR massive multipath effect and the achievable rate with some waveforms. We also derive the corresponding asymptotic achievable rate under a massive multipath setting. Experiment results based on real indoor channel measurements show that the massive multipaths can be revealed with a sufficiently large bandwidth in a practical indoor environment. Moreover, based on our experiments with real indoor measurements, the achievable rate of the TR wideband system is evaluated.

In this paper, we propose an optimal relay transmission policy by using a stochastic energy harvesting (EH) model for the EH two-way relay network, wherein the relay is solar-powered and equipped with a finite-sized battery. In this policy, the long-term average outage probability is minimized by adapting the relay transmission power to the wireless channel states, battery energy amount, and causal solar energy states. The designed problem is formulated as a Markov decision process (MDP) framework, and conditional outage probabilities for both decode-and-forward (DF) and amplify-and-forward (AF) cooperation protocols are adopted as the reward functions. We uncover a monotonic and bounded differential structure for the expected total discounted reward, and prove that such an optimal transmission policy has a threshold structure with respect to the battery energy amount in sufficiently high SNRs. Finally, the outage probability performance is analyzed and an interesting saturated structure for the outage performance is revealed, i.e., the expected outage probability converges to the battery empty probability in high SNR regimes, instead of going to zero. Furthermore, we propose a saturation-free condition that can guarantee a zero outage probability in high SNRs. Computer simulations confirm our theoretical analysis and show that our proposed optimal transmission policy outperforms other compared policies.

As it is more and more convenient to manipulate multimedia content, the authenticity of multimedia content becomes questionable. While there are many forensic techniques developed to identify the use of a single manipulation operation, a few has considered the cases where multiple operations may be involved. In these cases, investigators not only need to identify the use of each operation, but also need to detect the order of these operations. However, due to the interplay among operations, the order of operations may not always be detectable. This leads to a fundamental question of when we can and cannot detect the order of operations. In this paper, we formulate the problem of detecting the order of operations as a multiple hypotheses testing problem. Then, we propose an information theoretical framework to model the relationship between the detected hypothesis and the true hypothesis. Under this framework, we propose a mutual information-based criterion to obtain the best detector and use it to determine whether we can or cannot detect the order of operations based on certain set of features. A case study of detecting the order of resizing and blurring has been examined to demonstrate the effectiveness of the proposed framework and criteria. In addition, two known forensic problems are considered in the simulations to show that the results obtained from the proposed framework and criteria match those of the existing works.

While more and more forensic techniques have been proposed to detect the processing history of multimedia content, one starts to wonder if there exists a fundamental limit on the capability of forensics. In other words, besides keeping on searching what investigators can do, it is also important to find out the limit of their capability and what they cannot do. In this paper, we explore the fundamental limit of operation forensics by proposing an information theoretical framework. In particular, we consider a general forensic system of estimating operations' hypotheses based on extracted features from the multimedia content. In this system, forensicability is defined as the maximum forensic information that features contain about operations. Then, due to its conceptual similarity with mutual information in an information theory, forensicability is measured as the mutual information between features and operations' hypotheses. Such a measurement gives the error probability lower bound of all practical estimators, which use these features to detect the operations' hypotheses. Furthermore, it can determine the maximum number of hypotheses that we can theoretically detect. To demonstrate the effectiveness of our proposed information theoretical framework, we apply this framework on a forensic example of detecting the number of JPEG compressions based on normalized discrete cosine transform (DCT) coefficient histograms. We conclude that, when subband (2, 3) is used in detection and the size of the testing database is <;20000, the maximum number of JPEG compressions that we can expectedly perfectly detect using normalized DCT coefficient histogram features is four. Furthermore, we obtain the optimal strategies for investigators and forgers based on the fundamental measurement of forensicability.

Device-to-device (D2D) communication is proposed as a vital technique to enhance system capacity in cellular networks, which requires efficient interference modeling and managements to improve spectral efficiency. In practice, even when two wireless connections share the same resources, the interference between them may not always exist if there is no conflict at packet-level transmissions. To explore the real effect of interference, in this paper, we establish a cross-layer model for D2D communications underlaying cellular network and derive the closed-form stable throughput region. Then, we formulate an optimization problem to obtain the maximal achievable packet rate for cellular link, which determines whether the D2D pair can share the same resources with a specific cellular link. By dividing the original optimization problem into several simplified subproblems, the optimal solution can be calculated with low complexity. Subsequently, our model is extended to a generalized scenario where multiple D2D pairs share the same resources with one cellular link. Due to the complexity of obtaining closed-form expressions on stable throughput regions, we propose an algorithm to determine whether the transmissions of the cellular link and the multiple D2D pairs can satisfy the QoS requirements simultaneously. Furthermore, a low-complexity dynamic admission control strategy is introduced to deal with the admission process for new D2D requests. As a consequence, the cellular spectrum can allow access of many more D2D pairs than what the conventional model can. The significant improvements are verified by numeral simulations.

The explosion of today's wireless traffic requires operators to deploy more access points (APs) and design efficient collaboration mechanism to alleviate the interference among them. However, the collaborative techniques cannot work efficiently due to the high latency and low bandwidth interface between the APs in traditional networks. To address this challenge, cloud radio access network (C-RAN) is proposed, where a pool of base band units (BBUs) are connected to the distributed remote radio heads (RRHs) via high bandwidth and low latency links (i.e., the front-haul) and are responsible for all the baseband processing. But the limited front-haul link capacity may prevent the C-RAN from fully utilizing the benefits made possible by the centralized baseband processing. As a result, the front-haul link capacity becomes a bottleneck. To address this challenge, in this work, we propose using the time-reversal (TR)-based communication as the air interface in C-RAN. Due to the unique spatial and temporal focusing effects of TR-based communications, multiple terminal devices (TDs) are naturally separated by their location-specific signatures. Such a property allows signals to be combined to deliver without demanding more bandwidth. Therefore, the TR-based communication in essence creates a “tunneling” effect such that the baseband signals for all the TDs can be efficiently combined and transmitted in the front-haul. We study the performance of the proposed C-RAN architecture in terms of spectral efficiency and front-haul rate, based on extensive measurements of the wireless channel in a real-world environment. It is shown that with nearly the same amount of traffic load in the front-haul, more information can be transmitted when there are more TDs. The proposed TR tunneling effect can help deliver more information in the C-RAN and alleviate the burden of the front-haul caused by network densification.

In a time-reversal (TR) communication system, the signal-to-noise ratio (SNR) is boosted and the inter-user interference (IUI) is suppressed due to the spatial-temporal resonances, commonly known as the focusing effects, of the TR technique when implemented in a rich scattering environment. However, since the spatial-temporal resonances highly depend on the location-specific multipath profile, there exists a strong-weak spatial-temporal resonances effect. In the TR uplink system, different users at different locations enjoy different strengths of spatial-temporal resonances, i.e., the received signal-to-interference-noise ratios (SINRs) for different users vary, and the weak ones can be blocked from correct detection in the presence of strong ones. In this paper, we formulate the strong-weak spatial-temporal resonances in the multiuser TR uplink system as a max-min weighted SINR balancing problem by joint power control and signature design. Then, a novel two-stage adaptive algorithm that can guarantee the convergence is proposed. In stage I, the original nonconvex problem is relaxed into a Perron Frobenius eigenvalue optimization problem and an iterative algorithm is proposed to obtain the optimum efficiently. In stage II, the gradient search method is applied to update the relaxed feasible set until the global optimum for the original optimization problem is obtained. Numerical results show that our algorithm converges quickly, achieves a high energy-efficiency, and provides a performance guarantee to all users.

The explosion in demand for wireless data services in recent years has triggered pervasive deployment of wireless networks. How to associate to one of the wireless networks in the best interest of a user is an essential problem to mobile computing. In this paper, we analyze a data set of wireless LAN traces collected from campus networks, from which we observe that the user arrival distribution is approximately Poisson distributed; the session time and the waiting time to switch network can be approximated by exponential distributions. Based on the data analysis, we formulate a wireless access network association game as a multidimensional Markov decision process with negative network externality, where the best response strategy is an approximate Nash equilibrium. A modified value iteration algorithm is proposed to search the best response strategy profile. Applying the proposed algorithm to the data-driven stochastic model, the best response strategy is shown to achieve a better individual expected utility while satisfying the individual rationality, and attain a near-optimal social welfare performance compared to other strategies such as the centralized method and the greedy algorithm.

With the emergence of large scale data processing systems and big data analysis, cloud computing has become more and more popular. In this paper, we focus on the mechanism design in the infrastructure as a service (IaaS) cloud computing service market. Most of existing works on mechanism design assume static and independent individual utility, while in practice the cloud service is provided in a dynamic environment. To solve such problems, we propose a stochastic matching algorithm based on Markov Decision Process (MDP), which aims at optimizing the long-term system efficiency by considering the opportunity cost in the future. Based on the MDP formulation, we further design an efficient (EF), incentive compatible (IC), individual rational (IR) auction mechanism. Finally, we conduct experiment using Google cluster-usage traces dataset and show that the proposed MDP-based VCG auction mechanism can achieve EF, IC and IR properties simultaneously.

Energy harvesting from the surroundings is a promising solution to perpetually power-up wireless sensor communications. This paper presents a data-driven approach of finding optimal transmission policies for a solar-powered sensor node that attempts to maximize net bit rates by adapting its transmission parameters, power levels and modulation types, to the changes of channel fading and battery recharge. We formulate this problem as a discounted Markov decision process (MDP) framework, whereby the energy harvesting process is stochastically quantized into several representative solar states with distinct energy arrivals and is totally driven by historical data records at a sensor node. With the observed solar irradiance at each time epoch, a mixed strategy is developed to compute the belief information of the underlying solar states for the choice of transmission parameters. In addition, a theoretical analysis is conducted for a simple on-off policy, in which a predetermined transmission parameter is utilized whenever a sensor node is active. We prove that such an optimal policy has a threshold structure with respect to battery states and evaluate the performance of an energy harvesting node by analyzing the expected net bit rate. The design framework is exemplified with real solar data records, and the results are useful in characterizing the interplay that occurs between energy harvesting and expenditure under various system configurations. Computer simulations show that the proposed policies significantly outperform other schemes with or without the knowledge of short-term energy harvesting and channel fading patterns.

The use of energy harvesting cooperative relays is a promising solution to battery-limited wireless networks. In this paper, we consider a cooperative system in which one source node transmits data to one destination with the assistance of an energy harvesting decode-and-forward (DF) relay node. Our objective is to minimize the long-term average symbol error rate (SER) performance through a Markov decision process (MDP) framework. By doing so, we find the optimal stochastic power control at the relay that adapts the transmission power to the changes of energy harvesting, battery, channel, and decoding states. We derive a finite-integral expression for the exact average SER of the cooperative system. Further insights are gained by analyzing the asymptotic average SER and its lower and upper bounds at high signal-to-noise ratio (SNR), and the performance is eventually characterized by the occurrence probability of the relay's actions at the worst channel states in the MDP. We also show that the optimal cooperative policy at asymptotically high SNR follows a threshold-type structure, i.e., the relay spends the harvested energy only when the signal is successfully decoded and the source is faced with the worst channel condition in its direct link. Using these observations to quantify the diversity gain and the energy harvesting gain, we reveal that full diversity is guaranteed if and only if the probability of harvesting zero energy quantum is zero, which can be achieved by reducing the energy quantum size or increasing the energy harvesting capability. Finally, we present several numerical examples to validate the analytical findings.

How to enhance the network capacity is one of the most important issues. To achieve this, the existing works have focused on improving either the network structure or routing strategies with a common assumption of uniformly distributing the replicas of information among the nodes in the network. The nodes associated with information replicas are considered as source nodes (or server). However, for many networks such as the Internet, some nodes have much more traffics than the others, exhibiting an asymmetric phenomenon. In this letter, we study the optimal source selection strategy to enhance the network capacity, where an optimization model is proposed to find the optimal source selection probability distribution. Simulation results show that in homogeneous networks, most of the nodes can be the sources. While in heterogeneous networks such as the scale-free networks, only a small number of the nodes can be the sources. Moreover, an interesting phenomenon is observed that the optimal proportion of source nodes in Erdös-Rényi random network and Barabási-Albert scale-free network exhibits a power law relationship with the network size.

An optimal transmission policy is considered for energy harvesting (EH) wireless point-to-point communications, wherein the source node is solar-powered and equipped with a finite-sized battery. The long-term outage probability is minimized by adapting the transmission power to the causal energy arrival information, battery energy amount and channel fading through a Markov decision process (MDP) framework. We reveal an interesting saturated structure of the expected outage probability for which it eventually converges to a battery empty probability in high signal-to-noise power ratio (SNR). This phenomenon that links outage probability with EH capability is derived based on a monotonic and bounded differential structure of the long-term reward and a threshold structure of the optimal policy. Furthermore, a saturation-free condition on the outage performance is presented as well. Simulations confirm the theoretical analysis and the superiority of the proposed policy.

While microtask crowdsourcing provides a new way to solve large volumes of small tasks at a much lower price compared with traditional inhouse solutions, it suffers from quality problems due to the lack of incentives. On the other hand, providing incentives for microtask crowdsourcing is challenging since verifying the quality of submitted solutions is so expensive that it will negate the advantage of microtask crowdsourcing. We study cost-effective incentive mechanisms for microtask crowdsourcing in this paper. In particular, we consider a model with strategic workers, where the primary objective of a worker is to maximize his own utility. Based on this model, we first analyze two basic mechanisms and show their limitations in collecting high-quality solutions with low cost. Then, we propose a cost-effective mechanism that employs quality-aware worker training as a tool to stimulate workers to provide high-quality solutions. We prove theoretically that the proposed mechanism can be designed to obtain high-quality solutions from workers and ensure the budget constraint of the requester at the same time. Beyond its theoretical guarantees, we further demonstrate the effectiveness of our proposed mechanisms through a set of behavioral experiments.

A signal's compression history is of particular forensic significance because it contains important information about the origin and authenticity of a signal. Because of this, antiforensic techniques have been developed that allow a forger to conceal manipulation fingerprints. However, when antiforensic techniques are applied to multimedia content, distortion maybe introduced, or the data size may be increased. Furthermore,when compressing an antiforensically modified forgery, a tradeoff between the rate and distortion is introduced into the system. As a result, a forger must balance three factors, such as how much the fingerprints can be forensically concealed, the data rate, and the distortion, are interrelated to form a 3D tradeoff. In this paper, we characterize this tradeoff by defining concealability and using it to measure the effectiveness of an antiforensic attack. Then, to demonstrate this tradeoff in a realistic scenario, we examine the concealability-rate-distortion tradeoff in double JPEG compression antiforensics. To evaluate this tradeoff, we propose flexible antiforensic dither as an attack in which the forger can vary the strength of antiforensics. To reduce the time and computational complexity associated with decoding a JPEG file, applying antiforensics, and recompressing, we propose anantiforensic transcoder to efficiently complete these tasks in one step. Through simulation, two surprising results are revealed. One is that if a forger uses a lower quality factor in the second compression, applying antiforensics can both increase concealability and decrease the data rate. The other is that for any pairing of concealability and distortion values, achieved using a higher secondary quality factor, can also be achieved using a lower secondary quality factor at a lower data rate. As a result, the forger has an incentive to always recompress using a lower secondary quality factor.

In a dynamic system, how to perform learning and make decisions are becoming more and more important for users. Although there are some works in social learning-related literature regarding how to construct belief for an uncertain system state, few studies have been conducted on incorporating social learning with decision making. Moreover, users may have multiple concurrent options on different objects/resources and their decisions usually negatively influence each other's utility, which makes the problem even more challenging. In this paper, we propose an Indian Buffet Game to study how users in a dynamic system learn about the uncertain system state and make multiple concurrent decisions by not only considering the current myopic utility, but also the influence of subsequent users' decisions. We analyze the proposed Indian Buffet Game under two different scenarios: 1) on customers requesting multiple dishes without budget constraint and 2) with budget constraint. For both cases, we design recursive best response algorithms to find the subgame perfect Nash equilibrium (NE) for customers and characterize special properties of the NE profile under homogeneous setting. Moreover, we introduce a non-Bayesian social learning algorithm for customers to learn the system state, and theoretically prove its convergence. Finally, we conduct simulations to validate the effectiveness and efficiency of the proposed algorithms.

In this paper, we investigate the optimal spectrum procurement and pricing from the perspective of a cognitive mobile virtual network operator (C-MVNO), which is a second market between the spectrum owner and the secondary users (SUs). The spectrum procurement consists of spectrum leasing and spectrum sensing, where the latter has an uncertain outcome. The SUs are assumed to be heterogeneous in their valuations and demands of the spectrum, which is generally the case in reality. Hence, we use differentiated pricing among the heterogeneous SUs to improve the profit of the C-MVNO and allow the C-MVNO to perform necessary admission control. Modeling the spectrum procurement and trading procedure as a five-stage Stackelberg game, we analyze the optimal decisions for the C-MVNO by using backward induction. The optimal decisions of spectrum sensing, spectrum leasing, admission control, and differentiated pricing are derived, and an algorithm is proposed to compute those optimal decisions efficiently. Our theoretical results are also corroborated by numerical experiments, and a threshold structure of the solution is observed.

Heterogeneous multimedia content delivery over wireless networks is an important yet challenging issue. One of the challenges is maintaining the quality of service due to scarce resources in wireless communications and heavy loadings from heterogeneous demands. A promising solution is combining multicasting and scalable video coding (SVC) techniques via cross-layer design, which has been shown to effectively enhance the quality of multimedia content delivery service in the literature. Nevertheless, most existing works on SVC multicasting system focus on the static scenarios, where a snapshot of user demands is given and remains the same. In addition, the economic value of the SVC multicasting system, which is an important issue from the service provider's perspective, has seldom been explored. In this paper, we study a subscription-based SVC multicasting system with stochastic user arrival and heterogeneous user preferences. A stochastic framework based on the multidimensional Markov decision process (M-MDP) is proposed to study the negative network externality existing in the proposed system and theoretically evaluate the corresponding system efficiency. A game-theoretic analysis is conducted to understand the rational demands from heterogeneous users under different subscription pricing schemes. By transforming the original dynamic and complex M-MDP revenue optimization problem into a traditional average-reward MDP problem, we show that the optimal pricing strategy that maximizes the expected revenue of the service provider can be derived efficiently. Moreover, the overall user's valuation on the system, e.g., social welfare, is maximized under such an optimal pricing strategy. Finally, the efficiency of the proposed solutions is evaluated through simulations.

In heterogenous networks, intercell interference coordination (ICIC) is a big challenge. This paper presents an analytical framework to evaluate power control and time mute schemes in closed access femto and macro coexistent networks. We use stochastic geometry to model the downlink scenario and derive the coverage probability of indoor macro users and femto users. The optimal operating parameters for altruistic power control and time mute schemes are achieved. Considering the selfishness of the owners of femtos, we formulate the two-tier interference coordination as pricing games and obtain the closed-form of Nash equilibria. Simulation results demonstrate the influence of different parameters on the performance of ICIC schemes and show that when target SINR ≥ 3 dB, the time mute scheme outperforms the power control scheme in handling the indoor macro user coverage problem.

Nowadays, social networks are extremely large-scale with tremendous information flows, where understanding how the information diffuse over social networks becomes an important research issue. Most of the existing works on information diffusion analysis are based on either network structure modeling or empirical approach with dataset mining. However, the information diffusion is also heavily influenced by network users' decisions, actions and their socio-economic connections, which is generally ignored by existing works. In this paper, we propose an evolutionary game theoretic framework to model the dynamic information diffusion process in social networks. To verify our theoretical analysis, we conduct experiments by using Facebook network and real-world information spreading dataset of Memetracker. Experiment results show that the proposed game theoretic framework is effective and practical in modeling the social network users' information forwarding behaviors.

Cognitive femtocell has been envisioned as a promising technology for covering indoor environment and assisting heavy-loaded macrocell network. Although lots of technical issues of it have been studied, e.g., spectrum sharing, interference mitigation, etc, the economic issues that are very important for practical femtocell deployment have not been well investigated in the literatures. In this paper, we focus on the pricing issues in cognitive femtocell network, and propose a two-tier pricing game theoretic framework with a dynamic pricing model. We first model the cognitive users' network access behavior as a 2-dimensional Markov decision process and propose a modified value iteration algorithm to find the best strategy profiles for cognitive users. Based on the analysis of users' behavior, we further design an iterative gradient descent algorithm to find the Nash equilibrium pricing strategies for both macrocell and femtocell operators. Simulation results verify our theoretic analysis and show that the proposed algorithm can quickly converge to the Nash equilibrium prices.

Multimedia forensics concerns on extracting forensic information from suspicious multimedia contents. This information was embedded into the content inadvertently whenever an operation happened. Investigators may estimate the possible operations by obtaining features from the multimedia content and applying detection algorithms based on the statistics. While most existing works focus on improving detection performance and finding what more we can do, understanding the fundamental limit on the forensic information that we can obtain from the extracted features is also important. It enables us to understand the limit of forensicability. In this paper, we explore the fundamental limit of forensicability by introducing an information theoretical framework for multimedia forensics. We use mutual information as the measure of forensic information conveyed by features to investigators. To show the analytical process, we take the case of multiple JPEG compression forensics as an example. We claim that, under typical circumstances, the maximum number of compressions that we can detect by examine DCT coefficients is up to 4, in an expected sense. In addition, we also find the patterns of compression quality factors that contain the most and least forensic information.

Information diffusion over social networks becomes a hot topic recently. Most of the existing works are based on the machine learning method with social network structure analysis and empirical data mining. However, the results learned from some specific dataset may not apply to the future networks, since the social network structure is in a highly dynamic environment. Moreover, the dynamics of information diffusion are also heavily influenced by network users' decisions, actions and their socio-economic interactions, which is generally ignored by existing works. In this paper, we propose an evolutionary game theoretic framework to model the dynamic information diffusion process in social networks, which focuses on the users' behavior analysis from a microeconomics points of view. We also conduct experiments by using real-world Twitter information diffusion dataset, which shows that the proposed evolutionary game theoretic model is effective and practical in modeling the social network users' information diffusion dynamics.

As the nanotechnology becomes more and more mature, the concept of nano communication emerged and attracted lots of researchers' attention. To implement the nano communication system, the diffusion-based molecular communication is considered as a promising bio-inspired approach, where the nano transmitter emits molecules into the medium to transmit data and the nano receiver absorbs molecules from the medium to receive data. Since all the nano machines share the same propagation medium and the molecules are identical, the interference among nano transmitters are unavoidable. In this paper, we analyze the inter-symbol and inter-user interference in the diffusion-based molecular communication systems. Based on the interference analysis, we further study the bit error rate performance and derive the optimal decision threshold for the nano receiver. Simulation results are shown to verify our analysis.

Equipped with renewable energy generators, pro-sumers produce, store and consume energy and become an important entity in smart grids. With time-variant power demands and generation capacities, prosumers can exchange energy via local power lines with less transmission loss than the long-distance power lines to the traditional power plants. In this paper, we develop a local power exchange game and address the cheating problem that seller prosumers actually supply insufficient energy instead of the promised amount in the trade. We apply the indirect reciprocity principle to build an anti-cheating local energy exchange system that maintains reputations for the prosumers according to their selling histories. By supplying less energy to the buyers who cheated in previous trades, this system motivates the autonomous prosumers to cooperate instead of cheating. Simulation results have shown that this strategy significantly reduces the number of cheating prosumers and improves the overall system utility. Compared with the direct reciprocity-based counterpart, this system reaches the desirable equilibrium much faster and requires less energy from the traditional energy plants.

In this paper, we present an overview of the time-reversal (TR) wireless paradigm for green Internet of Things (IoT). It is shown that the TR technique is a promising technique that focuses signal waves in both time and space domains. The unique asymmetric architecture significantly reduces the cost of the terminal devices, the total number of which is expected to be very large for IoT. The focusing effect of the TR technique can harvest the energy of all the multi-paths at the receiver, which improves the energy efficiency of the wireless transmission and thus the battery life of terminal devices in IoT. Facilitated by the high-resolution spatial focusing, the TR division multiple access scheme leverages the uniqueness of the multi-path profiles in the rich-scattering environment and maps them into location-specific signatures, so that spatial multiplexing can be achieved for multiple users operating on the same spectrum. In addition, the TR system can easily support heterogeneous terminal devices by providing various quality-of-service (QoS) options through adjusting the waveform and rate backoff factor. Finally, the unique location-specific signature in TR system can provide additional physical-layer security and thus can enhance the privacy and security of customers in IoT. All the advantages show that the TR technique is a promising paradigm for IoT.

To meet the dramatically increasing demands for vehicular communications, cognitive vehicular networks have been proposed to broaden the vehicular communication bandwidth by using cognitive radio technology. Meanwhile, the nationwide Super Wi-Fi project that allows the TV white space frequencies to be used for free, makes the concept of cognitive vehicular networks realistic. Recently, lots of technical issues of cognitive vehicular networks have been studied from the network designers' perspective, e.g., vehicular spectrum sensing and access, applications with different vehicular QoS, etc. Different from the existing works, in this paper, we consider from the vehicular users' perspective by optimizing throughput via route selection in cognitive vehicular networks using TV white space. By employing the attainable data rate as route selection metric, we propose two schemes: instantaneous route selection and long-term route selection. To evaluate the expected data rate on the route, we analyze the cognitive vehicular network throughput under two spectrum sharing models: spectrum overlay and spectrum underlay. In the experiments, we use Google spectrum dataset to estimate the intensity of TV base stations in the United States and evaluate the cognitive vehicular network throughput performance, which shows that the spectrum overlay model is more suitable for most of states in current United States, except New Jersey, Delaware and Utah. Moreover, we conduct a case study regarding the route I-88E and I-90E selection between Cortland and Schenectady in New York State. The traffic intensities and traffic intensity transition probabilities of these two routes are estimated using the real-world traffic volume dataset of New York State. Based on the estimated traffic information, we calculate the attainable instantaneous and long-term data rates of each vehicular user, which shows that route I-88E is preferable to route I-90E in most cases.

In a distributed cognitive radio network, due to negative network externality, rational secondary users tend to avoid accessing the same vacant primary channels with others. Moreover, they usually need to make their channel access decisions in a sequential manner to avoid collisions. The characteristic of negative network externality and the structure of sequential decision making make the multi-channel sensing and access problem challenging, which has not been well studied by the existing literatures. To solve these problems, in this paper, we propose a multi-channel sensing and access game, which not only considers the negative network externality in secondary users' decision making, but also takes into account their sequential decision making structure. We solve the multi-channel sensing problem using Bayesian learning method and design a cooperative learning rule for secondary users to accurately estimate the channel state. We study the multi-channel access problem under two scenarios: with and without resource constraint, respectively. For both scenarios, we design recursive best response algorithms for secondary users to find the subgame perfect Nash equilibria. Specifically, we analyze the homogenous case of the scenario without resource constraint and find that the Nash equilibrium profile exhibits a threshold structure. Finally, we conduct simulations to validate the effectiveness and efficiency of the proposed methods.

Cognitive femtocell has been envisioned as a promising technology for covering indoor environment and assisting heavy-loaded macrocell network. Although lots of technical issues of cognitive femtocell network have been studied, e.g., spectrum sharing, interference mitigation, etc., the economic issues that are very important for practical femtocell deployment have not been well investigated in the literatures. In this paper, we focus on the pricing issues in the cognitive femtocell network and propose a two-tier pricing game theoretic framework with two models: static and dynamic pricing models. In the static pricing model, we derive the closed-form expressions for pricing and demand functions, as well as the Nash equilibrium pricing strategies for both macrocell and femtocell operators. In the dynamic pricing model, we first model the cognitive users' network access behavior as a two-dimensional Markov decision process and propose a modified value iteration algorithm to find the best strategy profiles for cognitive users. Based on the analysis of users' behavior, we further design an iterative gradient descent algorithm to find the Nash equilibrium pricing strategies for both macrocell and femtocell operators. Simulation results verify our theoretic analysis and show that the proposed algorithm in the dynamic pricing model can quickly converge to the Nash equilibrium prices.

Users in a social network are usually confronted with decision making under uncertain network state. While there are some works in the social learning literature on how to construct belief on an uncertain network state, few study has been made on integrating learning with decision making for the scenario where users are uncertain about the network state and their decisions influence with each other. Moreover, the population in a social network can be dynamic since users may arrive at or leave the network at any time, which makes the problem even more challenging. In this paper, we propose a Dynamic Chinese Restaurant Game to study how a user in a dynamic social network learns the uncertain network state and make optimal decision by taking into account not only the immediate utility but also subsequent users' negative influence. We introduce a Bayesian learning based method for users to learn the network state, and propose a Multi-dimensional Markov Decision Process based approach for users to achieve the optimal decisions. Finally, we apply the Dynamic Chinese Restaurant Game to cognitive radio networks and demonstrate from simulations to verify the effectiveness and efficiency of the proposed scheme.

This paper considers the cooperative optimization of mutual information in the MIMO Gaussian interference channel in a fully distributed manner via game theory. Null shaping constraints are enforced in the design of transmit covariance matrices to enable interference mitigation among links. The transmit covariance matrices leading to the Nash Equilibrium (NE) are derived, and the existence and uniqueness of the NE is analyzed. The formation of the cooperative sets, that represent the cooperation relationship among links, is considered as coalition games and network formation games. We prove that the proposed coalition formation (CF) and coalition graph formation (CGF) algorithms are Nash-stable, and the proposed network formation (NF) algorithm converges to a Nash Equilibrium. Simulation results show that the proposed CF and CGF algorithms have significant advantages when the antennas at the transmitters is large, and the proposed NF algorithm enhances the sum rate of the system apparently even at low signal-to-noise ratio region and/or with small number of transmit antennas.

Many spectrum sensing and access algorithms have been proposed to improve secondary users' (SUs') opportunities of utilizing primary spectrums. However, most of them have separated the analysis of spectrum sensing and access. In this paper, we propose to integrate the design of spectrum sensing and access algorithms by taking into account the mutual influence of them. Due to selfish natures, SUs tend to access the primary channel without contribution to the spectrum sensing. Moreover, they may take out-of-equilibrium strategies because of the uncertainty of others' strategies. To model the complicated interactions among SUs, we formulate the joint spectrum sensing and access problem as an evolutionary game and derive the evolutionarily stable strategy (ESS) that no one will deviate from. Furthermore, we design a distributed learning algorithm for SUs to converge to the ESS. With the proposed algorithm, each SU senses and accesses the primary channel with the probabilities learned purely from its own past utility history, and finally achieves the desired ESS. Simulation results shows that our system can quickly converge to the ESS and such an ESS is robust to the sudden unfavorable deviations of selfish SUs.

This paper considers the cooperative maximization of mutual information in the MIMO Gaussian interference channel in a fully distributed manner via game theory. Null shaping constraints are enforced in the design of transmit covariance matrices to enable interference mitigation among links. The transmit covariance matrices leading to the Nash Equilibrium (NE) are derived, and the existence and uniqueness of the NE is analyzed. The formation of the cooperative sets, that represent the cooperation relationship among links, is considered as network formation games. We prove that the proposed network formation (NF) algorithm converges to a Nash Equilibrium. Simulation results show that the proposed NF algorithm enhances the sum rate of the system apparently even at low signal-to-noise ratio region and/or with small number of transmit antennas.

Due to the ease with which digital multimedia content can be modified, a number of techniques to forensically detect forgeries have been developed. Meanwhile, anti-forensic operations have been developed to defeat forensic techniques. When anti-forensics is applied, a forger must balance between the amount that editing fingerprints have been concealed and the distortion introduced to the content. Additionally, the forger may compress the forgery for storage or transmission, which introduces a tradeoff between data rate and distortion. In this paper, we define a measure of an anti-forensic technique's effectiveness which we call concealability and examine the tradeoff between concealability, rate, and distortion. We then characterize the concealability-rate-distortion (C-R-D) surface for double JPEG compression anti-forensics. To do this, we propose a new technique known as flexible anti-forensic dither to hide double JPEG fingerprints. From our experiments, we identify two surprising results related to the C-R-D surface.

With the foreseeable large scale deployment of electric vehicles (EVs) and the development of vehicle-to-grid (V2G) technologies, it is possible to provide ancillary services to the power grid in a cost efficient way, i.e., through the bidirectional power flow of EVs. A key issue in such kind of schemes is how to stimulate a large number of EVs to act coordinately to achieve the service request. This is challenging since EVs are self-interested and generally have different preferences toward charging and discharging based on their own constraints. In this paper, we propose a contract-based mechanism to tackle this challenge. Through the design of an optimal contract, the aggregator can provide incentives for EVs to participate in ancillary services to power grid, match the aggregated energy rate with the service request and maximize its own profits. We prove that under mild conditions, the optimal contract-based mechanism takes a very simple form, i.e., the aggregator only needs to publish an optimal unit price to EVs, which is determined based on the statistical distribution of EVs' preferences. We then consider a more practical scenario where the aggregator has no prior knowledge regarding the statistical distribution and study how should the aggregator learn the optimal unit price from its interactions with EVs. Simulation results are shown to verify the effectiveness of the proposed contract-based mechanism.

In a cognitive radio network with mobility, secondary users can arrive at and leave the primary users' licensed networks at any time. After arrival, secondary users are confronted with channel access under the uncertain primary channel state. On one hand, they have to estimate the channel state, i.e., the primary users' activities, through performing spectrum sensing and learning from other secondary users' sensing results. On the other hand, they need to predict subsequent secondary users' access decisions to avoid competition when accessing the ”spectrum hole”. In this paper, we propose a Dynamic Chinese Restaurant Game to study such a learning and decision making problem in cognitive radio networks. We introduce a Bayesian learning based method for secondary users to learn the channel state and propose a Multi-dimensional Markov Decision Process based approach for secondary users to make optimal channel access decisions. Finally, we conduct simulations to verify the effectiveness and efficiency of the proposed scheme.

Heterogeneous multimedia content delivery over wireless networks is an important yet challenging issue. A promising solution is combining multicasting and scalable video coding (SVC) techniques via cross-layer design which has been shown to be effectively solution in the literature. Nevertheless, most existing works on SVC multicasting system focus on the static scenarios. In addition, the economic value of SVC multicasting system has seldom been explored. In this work, we study a subscription-based SVC multicasting system with stochastic user arrival and heterogeneous user preferences. A stochastic framework based on Multi-dimensional Markov Decision Process (M-MDP) is proposed to study the negative network externality existing in the proposed system. A game-theoretic analysis is conducted to understand the rational demands from heterogeneous users the subscription economic model. We show that the optimal pricing strategy which maximizes the expected revenue of the service provider can be derived through dynamic iterative updating techniques. Moreover, the overall user's valuation on the system is maximized under such an optimal pricing strategy. Finally, the solution efficiency is evaluated through simulations.

Dynamic spectrum access in cognitive radio networks can greatly improve the spectrum utilization efficiency. Nevertheless, interference may be introduced to the Primary User (PU) when the Secondary Users (SUs) dynamically utilize the PU's licensed channels. If the SUs can be synchronous with the PU's time slots, the interference is mainly due to their imperfect spectrum sensing of the primary channel. However, if the SUs have no knowledge about the PU's exact communication mechanism, additional interference may occur. In this paper, we propose a dynamic spectrum access protocol for the SUs confronting with unknown primary behavior and study the interference caused by their dynamic access. Through analyzing the SUs' dynamic behavior in the primary channel which is modeled as an ON-OFF process, we prove that the SUs' communication behavior is a renewal process. Based on the Renewal Theory, we quantify the interference caused by the SUs and derive the corresponding closed-form expressions. With the interference analysis, we study how to optimize the SUs' performance under the constraints of the PU's communication quality of service (QoS) and the secondary network's stability. Finally, simulation results are shown to verify the effectiveness of our analysis.

With the advance of semiconductor technologies, the performance of analog-to-digital converter (ADC) has been improved a lot during the past decade in terms of both sampling rate and resolution. The cost has gone down dramatically that makes the wideband communication much affordable. Under such circumstances, a natural question to ask is: Is there a low complexity high energy-efficient solution to high throughput wideband communications? In this letter, we explore this question by studying time-reversal communications. We find that compared with that of OFDM system, the computational complexity of a time-reversal system at the transmitter side is lower since it requires similar additions but no multiplications. Furthermore, the computational complexity of a time-reversal system at the receiver side is negligible since only onetap detection is performed, which means that the overall computational complexity of time-reversal system is much lower than that of OFDM system. Moreover, we find that when the bandwidth is large enough, time-reversal system can achieve much higher achievable rate than OFDM system. Therefore, time-reversal technique is a desired solution to low complexity high throughput wideband communications when more bandwidth is available.

In this letter, we propose a non-cooperative feedback control game for secondary transmitter, where we assume that secondary users are rational and thus selfish, i.e., they will choose the feedback rate to maximize their own utility functions. The utility function of each user is defined as the downlink data-rate minus a linear price function of the channel state information (CSI) feedback rate. The existence of the Nash-equilibrium of the proposed game is analyzed. Also, in order to satisfy the primary user's interference threshold, power control algorithms are proposed. Simulation results show that the sum-rate of the proposed game is better than that of the equally distributed feedback-size scheme.

Based on the observation that every small window in a natural image has many similar windows in the same image, the nonlocal denoising methods perform denoising by weighted averaging all the pixels in a nonlocal window and have achieved very promising denoising results. However, the use of fixed parameters greatly limits the denoising performance. Therefore, an important issue in pixel-domain image denoising algorithms is how to adaptively choose optimal parameters. While the Stein's principle is shown to be able to estimate the true mean square error (MSE) for determining the optimal parameters, there exists a tradeoff between the accuracy of the estimate and the minimum of the true MSE. In this paper, we study the impact of such a tradeoff and formulate the image denoising problem as a coalition formation game. In this game, every pixel/block is treated as a player, who tries to seek partners to form a coalition to achieve better denoising results. By forming a coalition, every player in the coalition can obtain certain gains by improving the accuracy of the Stein's estimate, while incurring some costs by increasing the minimum of the true MSE. Moreover, we show that the traditional approaches using same parameters for the whole image are special cases of the proposed game theoretic framework by choosing the utility function without a cost term. Finally, experimental results demonstrate the efficiency and effectiveness of the proposed game theoretic method.

Many spectrum sensing methods and dynamic access algorithms have been proposed to improve the secondary users' opportunities of utilizing the primary users' spectrum resources. However, few of them have considered to integrate the design of spectrum sensing and access algorithms together by taking into account the mutual influence between them. In this paper, we propose to jointly analyze the spectrum sensing and access problem by studying two scenarios: synchronous scenario where the primary network is slotted and non-slotted asynchronous scenario. Due to selfish nature, secondary users tend to act selfishly to access the channel without contribution to the spectrum sensing. Moreover, they may take out-of-equilibrium strategies because of the uncertainty of others' strategies. To model the complicated interactions among secondary users, we formulate the joint spectrum sensing and access problem as an evolutionary game and derive the evolutionarily stable strategy (ESS) that no one will deviate from. Furthermore, we design a distributed learning algorithm for the secondary users to converge to the ESS. With the proposed algorithm, each secondary user senses and accesses the primary channel with the probabilities learned purely from its own past utility history, and finally achieves the desired ESS. Simulation results shows that our system can quickly converge to the ESS and such an ESS is robust to the sudden unfavorable deviations of the selfish secondary users.

Network service acquisition in a wireless environment requires the selection of a wireless access network. A key problem in wireless access network selection is to study the rational strategy considering the negative network externality, i.e, the influence of subsequent users' decisions on an individual's throughput due to the limited available resources. In this work, we formulate the wireless network selection problem as a stochastic game with negative network externality and show that finding the optimal decision rule can be modelled as a multi-dimensional Markov Decision Process (MDP). A modified value iteration algorithm is proposed to efficiently obtain the optimal decision rule with a simple threshold structure, which enables us to reduce the storage space of the strategy profile. We further investigate the mechanism design problem with incentive compatibility constraints, which enforce the networks to reveal the truthful state information. The formulated problem is a mixed integer programming problem which in general lacks an efficient solution. Exploiting the optimality of substructures, we propose a dynamic programming algorithm that can optimally solve the problem in the two-network scenario. For the multi-network scenario, the proposed algorithm can outperform the heuristic greedy approach in a polynomial-time complexity. Finally, simulation results are shown to validate the analysis and demonstrate the effectiveness of the proposed algorithms.

Dynamic spectrum access in cognitive radio networks can greatly improve the spectrum utilization efficiency. Nevertheless, interference may be introduced to the Primary User (PU) when the Secondary Users (SUs) dynamically utilize the licensed channel. If the SUs can be synchronous with the PUs, the interference is mainly due to their imperfect spectrum sensing of the primary channel. However, if the SUs have no knowledge about the PU's communication mechanism, additional interference may occur. In this paper, we propose a renewal theoretical framework to study the situation when SUs confronting with unknown primary behavior. We quantify the interference caused by the SUs and derive the corresponding close-form expressions. With the interference analysis, we study how to optimize the SUs' performance under the constraints of the PU's communication quality of service (QoS). Finally, simulation results are shown to verify the effectiveness of our analysis.

In cognitive radio networks, secondary users (SUs) are allowed to opportunistically exploit the licensed channels by sensing primary users' (PUs) activities. Once finding the spectrum holes, SUs generally need to share the available licensed channels. Therefore, one of the critical challenges for fully utilizing the spectrum resources is how the SUs obtain accurate information about the PUs' activities and make right decisions of accessing channels to avoid competition from other SUs. In this paper, we formulate SUs' learning and decision making process as a Chinese Restaurant Game by considering the scenario where SUs sense channels simultaneously and make access decisions sequentially. In the proposed game, SUs build the knowledge of the PUs' activities by their own sensing and learning the information from other SUs. They also predict their subsequent SUs' decisions to maximize their own utilities. We analyze the interactions among SUs in the proposed game and study specifically the impact of SUs' prior belief and sensing accuracy on their decisions. We also derive the theoretic results for the two-user two-channel case. Finally, we demonstrate the effectiveness and efficiency of the proposed scheme through simulations.

Cooperative peer-to-peer (P2P) streaming model, which enables cooperation among peers with large intra-group bandwidths, has been shown as a promising approach for video streaming applications. However, due to the inherent conflict between the individual peer's utility and the social optimality, designing an effective cooperative P2P streaming system becomes challenging. In this work, we first formulate the interactions among group peers as a cooperative P2P streaming game, which takes into account the unique characteristics of video. Then, a cheat-proof strategy is proposed to enforce rational group peers to achieve the optimal social welfare co-operatively. In particular, we prove theoretically that the proposed strategy can enforce truth-telling as an equilibrium, satisfy individual rationality and guarantee the budget balance of the system in an average sense. Finally, simulation results are shown to verify the effectiveness of the proposed scheme.

The viability of cooperative communications largely depends on the willingness of users to help. However, in future wireless networks where users are rational and pursue different objectives, they will not help relay information for others unless this can improve their own utilities. Therefore, it is very important to study the incentive issues when designing cooperative communication systems. In this paper, we propose a cooperation stimulation scheme for multiuser cooperative communications using indirect reciprocity game. By introducing the notion of reputation and social norm, rational users who care about their future utilities get the incentive to cooperate with others. Different from existing works on reputation based schemes that mainly rely on experimental verifications, we prove theoretically that cooperating with users having good reputation can be sustained as an equilibrium. Moreover, by modeling the action spreading as an evolutionary game, we show through simulations that the equilibria we found are evolutionarily stable and can be reached with proper initial conditions. Finally, simulation results are shown to verify the efficiency and effectiveness of the proposed scheme.

While demand response has achieved promising results on making the power grid more efficient and reliable, the additional dynamics and flexibility brought by demand response also increase the uncertainty and complexity of the centralized load forecast. In this paper, we propose a game theoretic demand response scheme that can transform the traditional centralized load prediction structure into a distributed load prediction system by the participation of customers. Moreover, since customers are generally rational and thus naturally selfish, they may cheat if cheating can improve their payoff. Therefore, enforcing truth-telling is crucial. We prove analytically and demonstrate with simulations that the proposed game theoretic scheme is cheat-proof, i.e., all customers are motivated to report and consume their true optimal demands and any deviation will lead to a utility loss. We also prove theoretically that the proposed demand response scheme can lead to the solution that maximizes social welfare and is proportionally fair in terms of utility function. Moreover, we propose a simple dynamic pricing algorithm for the power substation to control the total demand of all customers to meet the target demand curve. Finally, simulations are shown to demonstrate the efficiency and effectiveness of the proposed game theoretic algorithm.

The rising cost of energy and increased environmental awareness have sparked a keen interest in the development and deployment of energy-efficient communication technologies. The energy efficiency of cellular networks can be increased significantly by selectively switching off some of the base stations (BSs) during periods of low traffic load. In this paper, we propose a scalable BS switching strategy and use cooperative communications and power control to extend network coverage to the service areas of the switched-off BSs. The outage probability and the achievable power savings of the proposed scheme are analyzed, both analytically and numerically, and a potential of up to 50% power saving is observed in the numerical results.

One critical issue in dynamic spectrum access of cognitive radio networks is the analysis of interference caused by Secondary Users (SUs). Most of the current works focus on mitigating the aggregated interference effects of SUs at Primary Users (PUs) in the physical layer. However, the interference is also dynamically related to the communication behaviors between PUs and SUs. In this paper, we analyze the interference caused by SUs in the MAC layer by taking into account the dynamic behaviors between PUs and SUs. Based on the ON-OFF primary channel state model, we derive the close-form expressions for the probability of interference caused by SUs and quantify the interference effect in two scenarios: slotted secondary network and non-slotted secondary network. We also discuss how to control SUs' access behavior such that the normal communication of PUs can be guaranteed. Finally, simulation results are shown to verify the effectiveness of our analysis.

We formulate the wireless security problem as an indirect reciprocity game, and propose a security mechanism that applies the indirect reciprocity principle to suppress attacks in wireless networks. In this system, a large number of nodes cooperate to reject the network access requests from attackers during the punishment periods. If the punishment time is so long that the cost due to the loss of network services exceeds the illegal security gains of the attack, rational nodes do not have incentive to attack, and hence our system can reduce the attacking probability in the network. We develop a social norm and reputation updating process to build such an indirect reciprocity mechanism for the network. We evaluate the evolutionarily stable strategy (ESS) in the game, and provide the optimal action strategy and its corresponding stationary reputation distribution. Our system is robust against collusion attacks, and can significantly reduce the attacking rate for a wide range of attacks. Simulation results show that our system has much better security performance than the direct reciprocity mechanism, especially in the large-scale wireless network with terminal mobility. Our system can be applied to many wireless networks including cognitive radio networks to improve their security performance.

In this paper, we propose a spectrum access framework to address the efficient allocation of channels of a base station (BS) by stimulating cooperation between primary users (PUs) and secondary users (SUs). We model the cooperation stimulation problem as an indirect reciprocity game, where SUs help PUs relay information and gain reputations to access the vacant channels in the future. We design a reputation updating policy under time-varying channels and prove the existence of stationary reputation distribution. We further formulate the decision making of an SU as a Markov Decision Process (MDP) and use a modified value iteration algorithm to find the optimal action rule. Moreover, we theoretically derive the condition under which the optimal action rule is an evolutionarily stable strategy (ESS). Finally, simulation results are shown to verify the effectiveness of the proposed scheme.

In this letter, by introducing the strategic decision making into the Chinese restaurant process, we propose a new game, called Chinese Restaurant Game, as a new general framework for analyzing the individual decision problem in a network with negative network externality. Our analysis shows that a balance in utilities among the customers in the game will eventually be achieved under the strategic decision making process. The equilibrium grouping is defined to describe the predicted outcome of the proposed game, which can be found by a simple algorithm. The simulation results confirm that the rational customers in Chinese restaurant game automatically achieve a balance in loading in order to reduce the impact from the negative network externality.

The viability of cooperative communications largely depends on the willingness of users to help. However, in future wireless networks where users are rational and pursue different objectives, they will not help relay information for others unless this can improve their own utilities. Therefore, it is very important to study the incentive issues when designing cooperative communication systems. In this paper, we propose a cooperation stimulation scheme for multiuser cooperative communications using indirect reciprocity game. By introducing the notion of reputation and social norm, rational users who care about their future utilities get the incentive to cooperate with others. Different from existing works on reputation based schemes that mainly rely on experimental verifications, we theoretically demonstrate the effectiveness of the proposed scheme in two steps. First, we conduct steady state analysis of the game and show that cooperating with users having good reputation can be sustained as an equilibrium when the cost-to-gain ratio is below a certain threshold. Then, by modeling the action spreading at transient states as an evolutionary game, we show that the equilibria we found in the steady state analysis are stable and can be reached with proper initial conditions. Moreover, we introduce energy detection to handle possible cheating behaviors of users and study its impact to the proposed indirect reciprocity game. Finally, simulation results are shown to verify the effectiveness of the proposed scheme.

Radio nodes can obtain illegal security gains by performing attacks, and they are motivated to do so if the illegal gains are larger than the resulting costs. Most existing direct reciprocity-based works assume constant interaction among players, which does not always hold in large-scale networks. In this paper, we propose a security system that applies the indirect reciprocity principle to combat attacks in wireless networks. Because network access is highly desirable for most nodes, including potential attackers, our system punishes attackers by stopping their network services. With a properly designed social norm and reputation updating process, the aim is to incur a cost due to the loss of network access to exceed the illegal security gain. Thus rational nodes are motivated to abandon adversary behavior for their own interests. We derive the optimal strategy and the corresponding stationary reputation distribution, and evaluate the stability condition of the optimal strategy using the evolutionarily stable strategy concept. This security system is robust against collusion attacks and can significantly reduce the attacker population for a wide range of attacks when the stability condition is satisfied. Simulation results show that the proposed system significantly outperforms the existing direct reciprocity-based systems, especially in the large-scale networks with terminal mobility. This technique can be extended to many wireless networks, including cognitive radio networks, to improve their security performance.

A portable device such as a digital camera with a single sensor and Bayer color filter array (CFA) requires demosaicing to reconstruct a full color image. To display a high resolution image on a low resolution LCD screen of the portable device, it must be down-sampled. The two steps, demosaicing and down-sampling, influence each other. On one hand, the color artifacts introduced in demosaicing may be magnified when followed by down-sampling; on the other hand, the detail removed in the down-sampling cannot be recovered in the demosaicing. Therefore, it is very important to consider simultaneous demosaicing and down-sampling.

Cooperation is a promising approach to simultaneously achieve efficient utilization of spectrum resource and improve the quality of service of primary users in dynamic spectrum access networks. However, due to the selfish nature, secondary users may not act as cooperatively as primary users have expected. Therefore, how to stimulate the secondary users to play cooperatively is an important issue. In this paper, we propose a reputation-based spectrum access framework, where the cooperation stimulation problem is modeled as an indirect reciprocity game. In the proposed game, secondary users choose how to help primary users relay information and gain reputations, based on which they can access a certain amount of vacant licensed channels in the future. By formulating a secondary user's decision making as a Markov decision process, we obtain the optimal action rule, according to which the secondary user will use maximal power to help primary user relay data if the channel is not in an outage, and thus greatly improve the primary user's quality of service as well as the spectrum utilization efficiency. Moreover, we prove the uniqueness of stationary reputation distribution and theoretically derive the condition under which the optimal action rule is evolutionarily stable. Finally, simulation results are shown to verify the effectiveness of the proposed scheme.

In this paper, we study the image interpolation from the game theoretic perspective and formulate the image interpolation problem as an evolutionary game. In this evolutionary game, the players are the unknown high resolution pixels and the pure strategies of the players are the corresponding low resolution neighbors. By regarding the non-negative weights of the low resolution pixels as the probabilities of selecting the pure strategies, the problem of estimating the high resolution pixels becomes finding the evolutionarily stable strategies for the evolutionary game. Experimental results show that the proposed game theoretical approach can achieve better performance than the state-of-the-art image interpolation methods in terms of both PSNR and visual quality.

In cognitive networks, since nodes generally belong to different authorities and pursue different goals, they will not cooperate with others unless cooperation can improve their own performance. Thus, how to stimulate cooperation among nodes in cognitive networks is very important. However, most of existing game-theoretic cooperation stimulation approaches rely on the assumption that the interactions between any pair of players are long-lasting. When this assumption is not true, according to the well-known Prisoner's Dilemma and the backward induction principle, the unique Nash equilibrium (NE) is to always play non-cooperatively. In this paper, we propose a cooperation stimulation scheme for the scenario where the number of interactions between any pair of players are finite. The proposed algorithm is based on indirect reciprocity game modelling where the key concept is "I help you not because you have helped me but because you have helped others". We formulate the problem of finding the optimal action rule as a Markov Decision Process (MDP) and propose a modified value iteration algorithm to find the optimal action rule. Using the packet forwarding game as an example, we show that with an appropriate cost-to-gain ratio, the strategy of forwarding the number of packets that is equal to the reputation level of the receiver is an evolutionarily stable strategy (ESS). Finally, simulations are shown to verify the efficiency and effectiveness of the proposed algorithm.

In cognitive networks, since nodes generally belong to different authorities and pursue different goals, they will not cooperate with others unless cooperation can improve their own performance. Thus, how to stimulate cooperation among nodes in cognitive networks is very important. However, most of existing game-theoretic cooperation stimulation approaches rely on the assumption that the interactions between any pair of players are long-lasting. When this assumption is not true, according to the well-known Prisoner's Dilemma and the backward induction principle, the unique Nash equilibrium (NE) is to always play non-cooperatively. In this paper, we propose a cooperation stimulation scheme for the scenario where the number of interactions between any pair of players are finite. The proposed algorithm is based on indirect reciprocity game modelling where the key concept is ``I help you not because you have helped me but because you have helped others''. We formulate the problem of finding the optimal action rule as a Markov Decision Process (MDP). Using the packet forwarding game as an example, we show that with an appropriate cost-to-gain ratio, the strategy of forwarding the number of packets that is equal to the reputation level of the receiver is an evolutionarily stable strategy (ESS). Finally, simulations are shown to verify the efficiency and effectiveness of the proposed algorithm.

Recently, many works have been proposed in the area of cognitive radio to efficiently utilize the spectrum for data communication. However, little effort has been made in content-aware multimedia applications over cognitive radio networks. In this paper, we study the multimedia streaming problem over cognitive radio networks. The uniquely scalable and delay-sensitive characteristics of multimedia data and the resulting impact on users' viewing experiences of multimedia content are explicitly involved in the utility functions, due to which the primary user and the secondary users can seamlessly switch among different quality levels to achieve the greatest utilities. Then, we formulate the spectrum allocation problem as an auction game and propose a distributively auction-based spectrum allocation scheme, which is spectrum allocation using Alternative Ascending Clock Auction (ACA-A). We prove that ACA-A is cheat-proof and can maximize the social welfare. Finally, simulation results are presented to demonstrate the efficiency of the proposed algorithms.

How to adaptively choose optimal neighborhoods is very important to pixel-domain image denoising algorithms since too many neighborhoods may cause over-smooth artifacts and too few neighborhoods may not be able to efficiently remove the noise. While the Stein's principle is shown to be able to estimate the true mean square error (MSE) for determining the optimal neighborhoods, there exists a trade-off between the accuracy of the estimate and the minimum of the true MSE. In this paper, we study the impact of this trade-off and formulate the image denoising problem as a coalition formation game. In the game, every pixel is treated as a player, who tries to seek partners to form a coalition to achieve better denoising results. By forming a coalition, every player in the coalition can obtain a gain of improving the accuracy of the Stein's estimate while incurring a cost of increasing the minimum of the true MSE. We also propose a heuristically distributed approach for coalition formation. Finally, experimental results show that the proposed game theoretical approach can achieve better performance than the nonlocal method in terms of both PSNR and visual quality.

The wide-spread use of P2P video streaming systems have introduced a large number of unnecessary traverse links leading to substantial network inefficiency. To address this problem and achieve better streaming performance, we propose to enable cooperation among group peers, which are geographically neighboring peers with large intra-group upload and download bandwidths. Considering the peers' selfish nature, we formulate the cooperative streaming problem as an evolutionary game and derive the evolutionarily stable strategy (ESS) for every peer. Moreover, we propose a simple and distributed learning algorithm for the peers to converge to the ESSs. Compared to the traditional non-cooperative P2P schemes, the proposed cooperative scheme achieves much better performance in terms of social welfare and probability of real-time streaming.

Cognitive radio technologies have become a promising approach to efficiently utilize the spectrum. Although many works have been proposed recently in the area of cognitive radio for data communications, little effort has been made in content-aware multimedia applications over cognitive radio networks. In this paper, we study the multimedia streaming problem over cognitive radio networks, where there is one primary user and N secondary users. The uniquely scalable and delay-sensitive characteristics of multimedia data and the resulting impact on users' viewing experiences of multimedia content are explicitly involved in the utility functions, due to which the primary user and the secondary users can seamlessly switch among different quality levels to achieve the largest utilities. Then, we formulate the spectrum allocation problem as an auction game and propose three distributively auction-based spectrum allocation schemes, which are spectrum allocation using Single object pay-as-bid Ascending Clock Auction (ACA-S), spectrum allocation using Traditional Ascending Clock Auction (ACA-T), and spectrum allocation using Alternative Ascending Clock Auction (ACA-A). We prove that all three algorithms converge in a finite number of clocks. We also prove that ACA-S and ACA-A are cheat-proof while ACA-T is not. Moreover, we show that ACA-T and ACA-A can maximize the social welfare while ACA-S may not. Therefore, ACA-A is a good solution to multimedia cognitive radio networks since it can achieve maximal social welfare in a cheat-proof way. Finally, simulation results are presented to demonstrate the efficiency of the proposed algorithms.

While peer-to-peer (P2P) video streaming systems have achieved promising results, they introduce a large number of unnecessary traverse links, which consequently leads to substantial network inefficiency. To address this problem and achieve better streaming performance, we propose to enable cooperation among “group peers,” which are geographically neighboring peers with large intra-group upload and download bandwidths. Considering the peers' selfish nature, we formulate the cooperative streaming problem as an evolutionary game and derive, for every peer, the evolutionarily stable strategy (ESS), which is the stable Nash equilibrium and no one will deviate from. Moreover, we propose a simple and distributed learning algorithm for the peers to converge to the ESSs. With the proposed algorithm, each peer decides whether to be an agent who downloads data from the peers outside the group or a free-rider who downloads data from the agents by simply tossing a coin, where the probability of being a head for the coin is learned from the peer's own past payoff history. Simulation results show that the strategy of a peer converges to the ESS. Compared to the traditional non-cooperative P2P schemes, the proposed cooperative scheme achieves much better performance in terms of social welfare, probability of real-time streaming, and video quality (source rate).

Copyright protection is a key issue for video sharing over public networks. To protect the video content from unauthorized redistribution, digital fingerprinting is commonly used. To develop an efficient collusion-resistant fingerprinting scheme, it is very important for the system designer to understand how the behavior dynamics of colluders affect the performance of collusion attack. In the literature, little effort has been made to explicitly study the relationship between risk, e.g., the probability of the colluders to be detected, and the distortion of the colluded signal. In this paper, we investigate the risk-distortion relationship for the linear video collusion attack with Gaussian fingerprint. We formulate the optimal linear collusion attack as an optimization problem of finding the optimal collusion parameters to minimize the distortion subject to a risk constraint. By varying the risk constraint and solving the corresponding optimization problem, we can derive the optimal risk-distortion curve. Moreover, based upon the observation that the detector/attacker can each improve the detection/attack performance with the knowledge of his/her opponent's strategy, we formulate the attack and detection problem as a dynamic mouse and cat game and study the optimal strategies for both the attacker and detector. We show that if the detector uses a fixed detection strategy, the attacker can estimate the detector's strategy and choose the corresponding optimal strategy to attack the fingerprinted video with a small distortion. However, if the detector is powerful, i.e., the detector can always estimate the attacker's strategy, the best strategy for the attacker is the min-max strategy. Finally, we conduct several experiments to verify the proposed risk-distortion model using real video data.

Collusion attack is a cost-effective attack against digital fingerprint. To develop an efficient collusion-resistant fingerprint scheme, it is very important for the detector to study the behavior of the colluders and the performance of collusion attack. Although several prior works have been proposed in the literature to analyze the performance of collusion attack, few effort has been made to explicitly study the relationship between risk, i.e., the probability of the colluders to be detected, and distortion of collusion attack. In this paper, we investigate the risk-distortion relationship of the linear video collusion attack with Gaussian fingerprint. We formulate the optimal linear collusion attack as an optimization problem, where the colluders try to minimize the distortion subject to a risk constraint. For any fixed risk constraint, the optimal distortion can be found using numerical optimization methods. By varying the risk constraint, we can obtain the risk-distortion model. We also conduct experiments to verify the proposed risk-distortion model using real video data.

How to efficiently and fairly allocate data rate among different users is a key problem in the field of multiuser multimedia communication. However, most of the existing optimization-based methods, such as minimizing the weighted sum of the distortions or maximizing the weighted sum of the PSNRs, have their weights heuristically determined. Moreover, those approaches mainly focus on the efficiency issue while ignoring the fairness issue. In this paper, we address this problem by proposing a game-theoretic framework, in which the utility/payoff function of each user/player is jointly determined by the characteristic of the transmitted video sequence and the allocated bitrate. We show that with the proportional fairness criterion, the game has a unique Nash equilibrium, according to which the controller can efficiently and fairly allocate the available network bandwidth to the users. Finally, we show several experimental results on real video data to verify the proposed method.

In this paper, a simultaneous MAP-based video denoising and rate-distortion optimized video encoding algorithm is proposed. We begin with formulating the denoising problem as a maximumaposteriori(MAP) estimate problem. Then, according to the Bayes rule, we show that the MAP estimate is determined by two terms: noise conditional density model andprioriconditional density model. Based on the assumptions that the noise satisfies Gaussian distribution and the priori model is measured by the bit-rate, the MAP estimate can be expressed as a rate distortion optimization problem. With this, we are able to simultaneously perform MAP-based video denoising and rate-distortion optimized video encoding under some assumptions. Moreover, we describe in details how to select suitable coding parameters, i.e., quantization parameter, mode, motion vector, reference index, and regularization parameter. Finally, we conduct several experiments to verify our proposed algorithm.

How to efficiently and fairly allocate data rate among different users is a key problem in the field of multiuser multimedia communication. However, most of the existing optimization-based methods, such as minimizing the weighted sum of the distortions or maximizing the weighted sum of the peak signal-to-noise ratios (PSNRs), have their weights heuristically determined. Moreover, those approaches mainly focus on the efficiency issue while there is no notion of fairness. In this paper, we address this problem by proposing a game-theoretic framework, in which the utility/payoff function of each user/player is jointly determined by the characteristics of the transmitted video sequence and the allocated bit-rate. We show that a unique Nash equilibrium (NE), which is proportionally fair in terms of both utility and PSNR, can be obtained, according to which the controller can efficiently and fairly allocate the available network bandwidth to the users. Moreover, we propose a distributed cheat-proof rate allocation scheme for the users to converge to the optimal NE using alternative ascending clock auction. We also show that the traditional optimization-based approach that maximizes the weighted sum of the PSNRs is a special case of the game-theoretic framework with the utility function defined as an exponential function of PSNR. Finally, we show several experimental results on real video data to demonstrate the efficiency and effectiveness of the proposed method.

In this paper, we propose a simultaneous rate control and video de-noising algorithm based on rate distortion optimization. According to our previous works, video de-noising can be performed by using rate distortion optimization with a lower bound quantization parameter (QP) constraint, where the lower bound QP is determined by the noise variance. Then, we find that the macroblock level rate control method in H.264 can be seen as an approximate solution of a rate distortion optimization problem with a specified rate distortion function. Based on these two studies, we integrate the video de-noising problem and rate control problem to a rate distortion optimization problem. We show the convexity of the problem and derive the optimal solution. To reduce the complexity, we propose to use a suboptimal solution based on simply thresholding. Some experiments are conducted to demonstrate the efficiency and effectiveness of the proposed method.

Inter frame prediction technique significantly improves the compression efficiency in the hybrid video coding schemes. However, this technique causes the decoding dependency of each inter frame on all of its reference frames. This dependency complicates the reverse play operation which is the most common video cassette recording (VCR) functions. This dependency also causes error propagation when the video is transmitted over error prone channel. In this paper, we propose a novel bidirectionally decodable Wyner-Ziv video coding scheme which relaxes this inter frame dependency. The proposed bidirectionally decodable Wyner-Ziv frame can be decoded by using whether forward prediction or backward prediction as side information at the decoder, i.e. the proposed stream supports forward decoding and backward decoding simultaneously. Compared with the other schemes which support reverse playback, our scheme requires much lower bandwidth and smaller storage space. In error resilient test, our scheme outperforms H.264 up to 4dB at same bitrate. Our proposed frames also support video splicing and stream switching at arbitrary time point like I-frames.

Error concealment techniques are very important for video communication since compressed video sequences may be corrupted or lost when transmitted over error-prone networks. In this paper, we propose a novel two-stage error concealment scheme for erroneously received video sequences. In the first stage, we propose a novel spatio-temporal boundary matching algorithm (STBMA) to reconstruct the lost motion vectors (MV). A well defined cost function is introduced which exploits both spatial and temporal smoothness properties of video signals. By minimizing the cost function, the MV of each lost macroblock (MB) is recovered and the corresponding reference MB in the reference frame is obtained using this MV. In the second stage, instead of directly copying the reference MB as the final recovered pixel values, we use a novel partial differential equation (PDE) based algorithm to refine the reconstruction. We minimize, in a weighted manner, the difference between the gradient field of the reconstructed MB in current frame and that of the reference MB in the reference frame under given boundary condition. A weighting factor is used to control the regulation level according to the local blockiness degree. With this algorithm, the annoying blocking artifacts are effectively reduced while the structures of the reference MB are well preserved. Compared with the error concealment feature implemented in the H.264 reference software, our algorithm is able to achieve significantly higher PSNR as well as better visual quality.

In this paper, a maximum a posteriori based (MAP-based) video denoising algorithm is proposed. According to the Bayes rule, the MAP estimate is determined by two terms: noise conditional density model and priori conditional density model. Based on the assumptions that the noise satisfies Gaussian distribution and the priori model is measured by the bit rate, the MAP estimate can be expressed as a rate distortion optimization problem. In order to find a suitable Lagrangian parameter, we re-write the problem as a constraint minimization problem by setting the rate as an objective function and the distortion as a constraint. In this way, we find that the Lagrangian parameter is determined by the distortion constraint. Fixing the distortion constraint, we can get the optimal Lagrangian parameter, which leads to the optimal denoising result. Some experiments are conducted to demonstrate the efficiency and effectiveness of the proposed method.

In Wyner-Ziv video coding system, motion estimation efficiency is much lower than that in conventional video coding system because current frame is not available when doing motion estimation. In this paper, we propose a successive resolution refinement algorithm to improve motion estimation efficiency. Based on our rate distortion analysis, we derive optimal down-sample ratio for two-stage successive resolution refinement system. We also analyze the performance of multistage case, and find that it approaches the performance of ideal motion compensated Wyner-Ziv video coding system, with at most 2.17 dB loss in PSNR. Experimental results demonstrate the correctness of the analysis and show that the proposed method out-performs original bit-plane refinement scheme up to 2.5 dB, with much lower complexity.

This letter addresses the security issues of the multimedia encryption schemes using multiple Huffman table (MHT). A known-plaintext attack is presented to show that the MHTs used for encryption should be carefully selected to avoid the weak keys problem. We then propose chosen-plaintext attacks on the basic MHT algorithm as well as the enhanced scheme with random bit insertion. In addition, we suggest two empirical criteria for Huffman table selection, based on which we can simplify the stream cipher integrated scheme, while ensuring a high level of security.

In this paper, we propose a novel framework for image inpainting, named sketch-guided texture-based image inpainting. Inspired by the well-known primal sketch model, we present a penetrating perspective into the process of image formation, where each image is seen as a variety of texture organized by some underlying structure. Based on this conceptual foundation, our approach of image inpainting integrates two unified stages: it first reconstructs the image structure with the sketch model, and then guided by the structure, it restores the missing region by patch-based texture synthesis. The major superiority of the framework over other ones consists in its capability of simultaneously recovering the structure and texture in the missing regions. Comprehensive experiments are performed to compare our method with other state-of- the-art ones; the encouraging results obtained convincingly demonstrate the effectiveness of our method.

In this paper, a novel spatial error concealment algorithm, which adaptively switches between directional interpolation and region matching, is proposed. Different from the previous spatial error concealment methods, which just utilize smooth property, the algorithm exploits both smooth property and texture information to recover the lost blocks. Based on the DCT coefficients in the available neighboring MBs, the algorithm automatically analyzes whether the MB is "smooth-like" or "texture-like" and adaptively select directional interpolation or region matching to recover the lost MB. The proposed algorithm has been evaluated on H.264 reference software JM 9.0. The experimental results demonstrate that the proposed method can achieve better PSNR performance and visual quality, compared with weighted pixel average (WPA) which is adopted in H.264, directional interpolation-only and region matching-only.

This paper addresses the security issues of the multimedia encryption schemes based on multiple Huffman table (MHT). A detailed analysis of known-plaintext attack is presented to show that the Huffman tables used for encryption should be carefully selected to avoid the weak keys problem. Further, we propose an efficient chosen-plaintext attack on the basic MHT method as well as the enhanced scheme inserting random bits. We also show that random rotation in partitioned bit stream cannot essentially improve the security.

In this paper, a novel temporal error concealment algorithm, called spatio-temporal boundary matching algorithm (STBMA), is proposed to recover the information lost in the video transmission. Different from the classical boundary matching algorithm (BMA), which just considers the spatial smoothness property, the proposed algorithm introduces a new distortion function to exploit both the spatial and temporal smoothness properties to recover the lost motion vector (MV) from candidates. The new distortion function involves two terms: spatial distortion term and temporal distortion term. Since both the spatial and temporal smoothness properties are involved, the proposed method can better minimize the distortion of the recovered block and recover more accurate MV. The proposed algorithm has been tested on H.264 reference software JM 9.0. The experimental results demonstrate the proposed algorithm can obtain better PSNR performance and visual quality, compared with BMA which is adopted in H.264.

When transmitted over error-prone networks, compressed video sequences may be received with errors. In this paper, we propose a priority-ranked region-matching algorithm to recover the "lost" area of the decoded frames, in which both temporal and spatial correlations of the video sequence are exploited. In the proposed scheme, we first calculate the priorities of all edge pixels of the "lost" area and generate a priority-ranked region group. Then according to their priorities, the regions in the group will search their best matching regions temporally and spatially. Finally, the "lost" area is recovered progressively by the corresponding pixels in the matching regions. Experimental results show that the proposed scheme achieves higher PSNR as well as better video quality in comparison with the method adopted in H.264.