Indoor falls often lead to fatalities due to delayed assistance. Current approaches to detecting indoor falls, such as cameras and wearables, intrude on privacy and are inconvenient. Radar-based device-free sensing has a limited range and requires dense deployment, leading to overhead costs. WiFi-based solutions, while promising, are currently either environment-dependent or insufficiently tested. In this work, we propose a fusion approach that leverages signal processing techniques to extract environment-independent features from the Channel State Information (CSI) in commercial WiFi devices. We then use a neural network to detect differentiating patterns from these features. Our lightweight LSTM network, with just 21,000 parameters, has been tested on 2,400 fall events from over 25 volunteers in 5 environments. It has also undergone 21 months of false alarm testing in 6 diverse settings. The system achieves a 94.1% detection rate and fewer than 5 false alarms per month in single-person homes.

Indoor tracking plays a critical role in a wide range of applications, yet existing solutions based on cameras, acoustics, or radar often face challenges related to privacy, deployment cost, and environmental sensitivity. WiFi-based methods offer a promising alternative by leveraging existing infrastructure, but most current approaches are active, requiring users to carry dedicated devices—limiting practicality in everyday scenarios. Passive WiFi tracking is more user-friendly, but existing solutions typically rely on complex feature engineering, require large training datasets, and struggle to generalize across different users and environments. In this work, we introduce a novel passive tracking system that requires only a single-shot training phase. By leveraging location signature based on statistical proximity metrics derived from CSI across multiple distributed WiFi devices, our method enables accurate, scalable, and training-efficient indoor tracking.

WiFi sensing has emerged as a compelling contactless modality for human activity monitoring by capturing fine-grained variations in Channel State Information (CSI). Its ability to operate continuously and non-intrusively while preserving user privacy makes it particularly suitable for health monitoring. However, existing WiFi sensing systems struggle to generalize in real-world settings, largely due to datasets collected in controlled environments with homogeneous hardware and fragmented, session-based recordings that fail to reflect continuous daily activity.We present CSI-Bench, a large-scale, in-the-wild benchmark dataset collected using commercial WiFi edge devices across 26 diverse indoor environments with 35 real users. Spanning over 461 hours of effective data, CSI-Bench captures realistic signal variability under natural conditions. It includes task-specific datasets for fall detection, breathing monitoring, localization, and motion source recognition, as well as a co-labeled multitask dataset with joint annotations for user identity, activity, and proximity. To support the development of robust and generalizable models, CSI-Bench provides standardized evaluation splits and baseline results for both single-task and multi-task learning. CSI-Bench offers a foundation for scalable, privacy-preserving WiFi sensing systems in health and broader human-centric applications.

In-house occupancy detection is vital for smart energy management, resource optimization, and home security. Traditional sensor-based solutions can be inaccurate and invasive. This paper presents a WiFi-based occupancy detection system that utilizes existing WiFi infrastructure and IoT devices. Our method employs a neural network with a shared CNN and a transformer block. Our preliminary evaluation, conducted using 18 unique IoT devices and data collected from 7 different homes over 42 days, demonstrates a detection accuracy of 94.88% with 8.12% false alarms in familiar environments, and 90.79% accuracy with 10.11% false alarms in new settings, significantly outperforming model-based methods.

Radio-frequency (RF)-based high-resolution human imaging is an emerging area of research fueled by the increasing availability of RF-radar devices. Even though existing works achieve accurate human body reconstruction for pose estimation purposes, human identification with imaging has not been feasible due to its limited resolution. In this work, we present high-resolution neural network (HRNet), a deep neural network based on conditional generative adversarial network architecture, to achieve high-resolution human silhouette images, which can be used for human identification. HRNet uses radar spatial spectrum generated using a modified multiple signal classification algorithm as input and is trained with Kinect images as ground truth. We tested our design using a commodity millimeter-wave radar device operating at 60 GHz. Experiments performed with 12 users in three different environments show that our proposed system can reconstruct human images with 4% mean silhouette difference when compared with Kinect images. Moreover, the system achieved an average classification accuracy of 90.6% for 12 users and 95.0% for seven users in unseen environments; thereby proving robustness to environment changes.

Channel State Information (CSI) is foundational for enabling advanced Wi-Fi sensing applications, yet its efficacy is significantly influenced by environmental factors, hardware variations, and noise. This paper introduces a structured framework designed to rigorously evaluate the quality of CSI, thereby enhancing the performance and reliability of Wi-Fi-based sensing systems. Our evaluation system features a multilayered pipeline, where the first layer assesses fundamental CSI characteristics, including packet loss and amplitude consistency over time, to verify data integrity, and the second layer evaluates the compatibility of CSI with specific applications, such as motion detection. Validation of our framework across various chipset samples demonstrates its utility in improving the accuracy and reliability of CSI-derived sensing and potential in refining data quality for data-driven approaches.

In-car child presence detection (CPD) has become a crucial vehicular safety measure to prevent heat-related injuries or fatalities of unattended children. However, existing CPD solutions based on sensors and cameras have limitations in accuracy, coverage, and additional cost. To address these challenges, leveraging existing in-car WiFi facilities has emerged as a promising solution. Nevertheless, current WiFi-based CPD solutions are not robust against environmental impacts and often generate unnecessary detection alerts when an adult is present in the car. In this demonstration, we propose a novel, robust CPD architecture designed to enhance performance under various conditions while minimizing false alarms. Our design incorporates three main systems: presence detection, seat localization, and child vs. adult classification. To validate the system's performance, we developed a prototype using existing WiFi devices operating at 5 GHz and conducted a series of experiments. Please find the project page with the URL: https://sites.google.com/originwirelessai.com/in-car-cpd.

Detecting the target speaker and enhancing speech with high fidelity has been a long-standing problem, especially in challenging acoustic conditions. To address these problems with minimal user cooperation, we have developed multimodal audioradio speech detection (RadioVAD) and enhancement (RadioSES) systems using mmWave modality. This demo presents how these two systems can be run together in real time to extract target speech and filter any type of ambient noise. Our demo uses an mmWave radar and a microphone attached to a laptop to detect and localize target speakers in the field of view, detect the presence of voice to trigger the microphone, and enhance the noisy speech with a deep learning model running in real-time. Our experiments confirm that an audioradio system can detect and isolate high-fidelity target speech, even with interfering speech and noise; while being privacy preserving and environmentally robust.

Addressing the pivotal challenge of discerning human and nonhuman activities in smart environments, in this demo, we present a system utilizing commercial WiFi transceivers for precise human and non-human motion differentiation through the walls. This system effectively filters non-human interference in smart home systems by extracting physically and statistically explainable features from ubiquitous WiFi signals. It passively recognizes moving subjects in real time without constraining their movement, even in complex environments. Tailored for edge computing, it ensures minimal resource consumption and generalizes well across various settings. Our long-term field tests confirm a high accuracy rate of 97.34% and a low false alarm rate of 1.75%, underscoring its robustness and readiness for practical deployment. Please find the companion video with the URL: https://youtu.be/6xkJZ_VvL9Q.

Voice interfaces have become one of the most ubiquitous human–computer interaction methods in recent years. Voice activity detection (VAD) is typically the first building block of a complex voice interface, often relying on audio signals. Acoustics-based VAD systems do not perform well in noisy and interference-prone environments. Smart assistants mitigate this problem by using a dictionary-based detection system. However, this approach is limited in its applicability. For instance, users may still need to manually mute and unmute their microphones during online meetings to prevent detection of interfering users, and speech leakage. In order to automate voice detection in challenging environments without these limitations, we propose RadioVAD, a noise and interference-resilient VAD system that uses radio modality, which is already available in various smartphones and home assistants. RadioVAD works by detecting possible human presence in the Field of View of the device, extracting the vocal fold’s vibration signal from the target speaker, and utilizing a time-domain neural network on raw radio signals to detect voice activity. Extensive experiments reveal that RadioVAD can detect voice activity in challenging environments with high accuracy and outperforms audio-based VAD when the audio signal has signal-to-noise ratio below 5 dB. Furthermore, RadioVAD reduces false alarm rate in interference-prone environments by 52%–72%, bringing significant improvements to VAD task. RadioVAD lays the foundation for future voice interfaces utilizing radio modality.

Device-free indoor object detection and localization are essential for the success of smart homes. Traditional vision/acoustic/radar-based approaches face operational constraints that limit their effectiveness and scalability. WiFi-based approaches have recently been a promising candidate due to their ubiquity, cost-effectiveness, and privacy-preserving nature. However, most of them show inadequate performance in typical residential settings due to the limited WiFi bandwidth and the resulting low spatial resolution. In this article, we introduce a novel system using commodity WiFi that can accurately determine the specific room where the person is, i.e., room-level localization. The system employs a novel multipath selection technique to concentrate on a limited set of multipaths predominated by the proximate motions to the device. Based on the technique, a spatial feature leveraging multiple antennas to enhance the spatial resolution is proposed for more refined detection coverage. Combining the spatial feature with time- and frequency-domain features, the system is shown to achieve an overall test accuracy of 87.63%, a true positive rate of 89.47%, and a positive predictive value of 88.51%, outperforming state-of-the-art methods by >20% and showing its potential for real-world applications.

Indoor intelligent perception systems have gained significant attention in recent years. However, accurately detecting human presence can be challenging in the presence of non-human subjects such as pets, robots, and electrical appliances, limiting the practicality of these systems for widespread use. In this paper, we propose a novel system (“WI-MOID") that passively and unobtrusively distinguishes moving human and various non-human subjects using a single pair of commodity WiFi transceivers, without requiring any device on the subjects or restricting their movements. WI-MOID leverages a novel statistical electromagnetic wave theory-based multipath model to detect moving subjects, extracts physically and statistically explainable features of their motion, and accurately differentiates human and various non-human movements through walls, even in complex environments. In addition, WI-MOID is suitable for edge devices, requiring minimal computing resources and storage, and is environment-independent, making it easy to deploy in new environments with minimum effort. We evaluate the performance of WI-MOID in five distinct buildings with various moving subjects, including pets, vacuum robots, humans, and fans, and the results demonstrate that it achieves 97.34% accuracy and 1.75% false alarm rate for identification of human and non-human motion, and 95.98% accuracy in unseen environments without model tuning, demonstrating its robustness for ubiquitous use.

In-car child presence detection (CPD) has gained worldwide attention due to increased child deaths reported yearly when they are left unattended in a car. Existing solutions usually require dedicated sensors and are being surpassed by WiFi-based CPD because the latter can provide broader coverage and can reuse the in-car WiFi devices. However, the existing WiFi-based CPD solutions are not robust and may suffer from miss detection due to the very weak breathing of a young child and high false alarms under unfavorable environmental conditions. In this paper, we propose a WiFi-based robust CPD system consisting of a motion and breathing detector. To improve breathing detection, we propose to treat the intermediate spectrogram for breathing estimation as images and apply image enhancement techniques followed by effective false alarm removal. Extensive experimental results have confirmed the robustness of the proposed system with a 99% detection accuracy and 3% false alarm rate.

Respiratory rate tracking has gained more and more interest in the past few years because of its great potential in exploring different pathological conditions of human beings. Conventional approaches usually require dedicated wearable devices, making them intrusive and unfriendly to users. To tackle the issue, many WiFi-based respiration tracking systems have been proposed because of WiFi’s ubiquity, low-cost, and most importantly, contactlessness. However, most existing works are of limited coverage and inflexible deployment, which greatly hinders their applications. In this paper, we propose WiResP, a practical and innovative WiFi-based respiration tracking system that utilizes a contrast enhancement technique to improve the detection of respiration. This approach combines both instantaneous and time-domain information, resulting in better recognition of breaths and identification of breath patterns. Extensive experiments under different settings show that WiResP can well capture respiratory rate during sleep under flexible deployments. Moreover, it remarkably increases the sensing coverage compared with the existing methods, making it a potential candidate toward real-world applications.

Indoor target detection through motion sensing based on Wi-Fi signals has gained much attention recently. However, most of the existing motion detection approaches can only detect motion in a large coverage area without knowing the distance of the target motion from the transmitter (Tx)/receiver (Rx). Passive positioning techniques can provide the location of a target, which, however, requires high deployment efforts without robust performance. In this article, we present a novel technique for detecting motion in proximity by exploring the physics behind the indoor radio frequency (RF) multipath propagation. We discover that motion in the proximity of the Rx/Tx produces distinct time dispersion over the radio channel at the Rx/Tx side. By exploring two novel metrics and linking them with the distance of the motions to antennas, we are able to precisely distinguish motions in nearby proximity from the motions far away. Extensive experiments in various real-world scenarios demonstrate that the proposed scheme can achieve true positive rates (TPRs) greater than 95% and 99% in distance-based and room-level proximity detection, respectively, while maintaining the corresponding false positive rates (FPRs) less than 5% and 0.5%. The detection delays for a detection distance of 2 m are within 0.6 s, which verifies the responsiveness of the proposed scheme.

Voice interfaces have become an integral part of our lives with the proliferation of smart devices. Today, Internet of Things devices mainly rely on microphones to sense sound. Microphones, however, have fundamental limitations, such as weak source separation, limited range in the presence of acoustic insulation, and being prone to multiple side-channel attacks. In this article, we propose RadioMic, a radio-based sound sensing system to mitigate these issues and enrich sound applications. RadioMic constructs sound based on tiny vibrations on active sources (e.g., a speaker diaphragm) or object surfaces (e.g., paper bag), and can work through walls, even a soundproof one. To convert the extremely weak sound vibration in the radio signals into sound signals, RadioMic introduces radio acoustics, and presents training-free approaches for robust sound detection and high-fidelity sound recovery. It then exploits a neural network to further enhance the recovered sound by expanding the recoverable frequencies and reducing the noises. RadioMic translates massive online audios to synthesized data to train the network and, thus, minimizes the need for radio-frequency (RF) data. We thoroughly evaluate different components of RadioMic under different scenarios using a commodity mmWave radar. The results show RadioMic outperforms the state-of-the-art systems significantly. We believe RadioMic provides new horizons for sound sensing and inspires attractive sensing capabilities of mmWave sensing devices.

Recognizing in-air gestures can enable intelligent human–computer interaction (HCI) applications and facilitate human lives. However, existing sensor/camera-based methods for gesture recognition are either nonubiquitous, intrusive to privacy, or inconvenient to carry around. Contemporary device-free approaches require the person to be in the line of sight and proximity to the sensing device. This article shows that WiFi signals can recognize hand-drawn in-air gestures even when the gesture location is nonline-of-sight/beyond walls to the WiFi transceivers. The proposed GWrite system utilizes the channel state information (CSI) time-series information from commercial WiFi chipsets. GWrite employs a unique approach for performing hand gestures, thus enabling the design of a hand movement model. Using the model and the time-reversal (TR) technique, this work derives a correspondence between the similarity of CSIs and the relative distance moved by the hand. This relation gave rise to unique features, such as the number of segments, angle, and the intersection between segments that can classify a set of gesture shapes consisting of straight-line segments. GWrite achieved an accuracy of 92% on a group of 15 gestures. The proposed approach can be applied to a broader set of gestures, unlike the current systems that function over a limited gesture set.

Indoor-location-based services rely on indoor maps, which are yet widely available despite numerous efforts from the industry. Existing solutions employ costly hardware (e.g., lidar) to achieve accurate mapping of indoor environments, or resort to crowdsourcing for floor plan generation at the cost of precision due to inaccurate inertial sensing. In this article, we leverage a new opportunity enabled by recent advances in RF-based inertial tracking that achieves centimeter accuracy. We present EZMAP, a high-accuracy, low-cost floor plan construction system that fuses RF and inertial sensing. EZMAP combines the fine-grained yet local information from RF tracking with the coarse grained but global contexts from inertial sensing (e.g., magnetic field strength), which together makes for an accurate map. Our system employs a robot for trajectory collection and requires only a single access point to be arbitrarily installed in the space, both of which are widely available nowadays. Furthermore, it can generate a map even only a small amount of data is available, allowing it to scale for different buildings, such as malls, office buildings, and homes with little cost. We validate the performance using a Dji RoboMaster S1 robot with commodity WiFi in three different buildings. The results show that our system can efficiently generate faithful maps for the targeted areas. With the ubiquity of the WiFi infrastructure and the rise of home robots, we believe our approach will pave the way for pervasive indoor maps services.

Speech enhancement and separation have been a long-standing problem, especially with the recent advances using a single microphone. Although microphones perform well in constrained settings, their performance for speech separation decreases in noisy conditions. In this work, we proposeRadioSES, an audioradio speech enhancement and separation system that overcomes inherent problems in audio-only systems. By fusing a complementary radio modality,RadioSEScan estimate the number of speakers, solve the source association problem, separate and enhance noisy mixture speeches, and improve both intelligibility and perceptual quality. We perform millimeter-wave sensing to detect and localize speakers and introduce an audioradio deep learning framework to fuse the separate radio features with the mixed audio features. Extensive experiments using commercial off-the-shelf devices show thatRadioSESoutperforms a variety of state-of-the-art baselines, with consistent performance gains in different environmental settings. Similar to the audiovisual methods,RadioSESprovides significant performance improvements (e.g. 3 dB gains in SiSDR, when compared with the corresponding audio-only method), along with the benefits of lower computational complexity and better privacy preservation.

Speech enhancement and separation have been a long-standing problem with recent advances using a single microphone. With the help of video modality, improvements have been shown for these tasks. In this work, we explore a multimodal approach using mmWave radio devices, as these devices can measure vocal folds vibration. Thorough data collection and extensive experiments with two different neural networks indicate that radio modality can bring significant improvements in speech enhancement and separation.

Heat-stroke and death of children being left alone in a parked car has attracted more and more attentions. As a result, car manufactures start to reward solutions for in-car Child Presence Detection (CPD) system to save lives recently. However, most of the existing works rely on dedicated sensors and only achieve limited accuracy and coverage. This paper presents the first-of-its-kind intelligent CPD system using commodity Wi-Fi. Based on a statistical electromagnetic wave model to fully leverage the information in all the multi-path components, the proposed CPD system mainly consists of a motion target detector to detect a child in awake/motion status, a stationary target detector to detect a sleeping child by extracting breathing rate information, and a transition target detector based on a Naive Bayes Classifier using multipath profiles as features. We build a real-time testbed and show through extensive experiments that the proposed system can achieve ≥ 99.34% detection rate and ≤ 4.38% false alarm rate, regardless of the location and motion status of a child. Built upon 2.4/5GHz Wi-Fi, the proposed system can integrate with the existing in-car Wi-Fi system with no additional hardware and calls for low CPU and memory consumptions, thus promising a practical candidate for CPD applications.

Indoor maps are essential to indoor location-based services, but are not widely accessible despite considerable efforts from the industry. Existing solutions employ costly hardware to achieve accurate mapping, or resort to laborious crowdsourcing methods, which may suffer from low accuracy due to inaccurate inertial sensing. In this paper, we leverage advanced RF-based inertial tracking and present a high-accuracy and low-cost floor plan reconstruction system. The proposed system combines local information from RF tracking with the global contexts from inertial sensing (e.g., magnetic field strength) for an accurate map. We validate the performance with commodity WiFi in an office building, which shows that the proposed system can efficiently generate faithful maps for a targeted area. With the ubiquitous deployment of WiFi devices, our approach will make a wide range of indoor location-based systems possible.

Indoor moving direction and rotation angle measurements are crucial to many ubiquitous mobile computing applications. Most of the state-of-the-art approaches rely on inertial sensors, e.g., accelerometers, gyroscopes, and magnetometers, which suffer from severe accumulative errors or accuracy degradation indoors. This article presents an RF-based direction estimation method, which utilizes the off-the-shelf commodity WiFi devices for accurate moving direction and in-place rotation angle estimation. The proposed approach employs a novel 2-D antenna array and leverages the spatial decay property of the time-reversal resonating strength. First, the moving speeds along different directions, specified by the 2-D array, are derived using virtual antenna alignment. The precise estimation of the device’s moving direction is then achieved by combining the obtained velocity information and the a prior knowledge of the array’s geometry layout. Experiments in a multipath-rich indoor environment have shown that the median error for moving direction estimation is 6.9°, which outperforms the accelerometer counterpart. The results also verify the good accuracy of in-place rotation angle estimation without any accumulative error, which beats the gyroscope in long-term tests. Because the proposed approach can achieve high accuracy without accumulative drifts, it is a promising candidate solution to applications that require accurate direction information.

Keyboard acts as one of the most commonly used mediums for human–computer interaction. Today, massive Internet-of-Things (IoT) devices are designed without a physical keyboard as they go tiny, but are almost all equipped with a wireless module for networks. In this work, we aim to enable a universal virtual keyboard using wireless signals, which would allow a typing interface for tiny IoT devices or serve as a portable alternative to the unwieldy physical keyboards. To this end, we presentmmKey, the first universal virtual keyboard system using a single millimeter-wave (mmWave) radio. By leveraging the unique advantages of mmWave signals,mmKeyconverts any flat surface, with a printed paper keyboard, into an effective typing medium.mmKeyenables concurrent keystrokes and supports multiple keyboard layouts (e.g., computer keyboard, piano keyboard, or phone keypad). We design a novel signal processing pipeline to detect, segment and separate, and finally, recognize keystrokes.mmKeydoes not need any training except for a minimal one-time effort of only three key-presses for keyboard calibration upon the initial setup. We prototypemmKeyusing a commodity 802.11ad/ay chipset, customized to support radar-like operations, and evaluate it with different keyboard layouts under various settings. Experimental results with ten participants demonstrate a keystroke recognition accuracy of >95% for single-key case and >90% for multikey scenario, which leads to a word recognition accuracy of >97%.

Fall is recognized as one of the most frequent accidents among elderly people. Many solutions, either wearable or noncontact, have been proposed for fall detection (FD) recently. Among them, WiFi-based noncontact approaches are gaining popularity due to the ubiquity and noninvasiveness. The existing works, however, usually rely on labor-intensive and time-consuming training before it can achieve a reasonable performance. In addition, the trained models often contain environment-specific information and, thus, cannot be generalized well for new environments. In this article, we propose DeFall, a WiFi-based passive FD system that is independent of the environment and free of prior training in new environments. Unlike previous works, our key insight is to probe the physiological features inherently associated with human falls, i.e., the distinctive patterns of speed and acceleration during a fall. DeFall consists of an offline template-generating stage and an online decision-making stage, both taking the speed estimates as input. In the offline stage, augmented dynamic time-warping (DTW) algorithms are performed to generate a representative template of the speed and acceleration patterns for a typical human fall. In the online phase, we compare the patterns of the real-time speed/acceleration estimates against the template to detect falls. To evaluate the performance of DeFall, we built a prototype using commercial WiFi devices and conducted experiments under different settings. The results demonstrate that DeFall achieves a detection rate above 95% with a false alarm rate lower than 1.50% under both line-of-sight (LOS) and non-LOS (NLOS) scenarios with one single pair of transceivers. Extensive comparison study verifies that DeFall can be generalized well to new environments without any new training.

Monitoring and identifying gait has recently emerged as a promising solution candidate for unobtrusive human recognition. In order to enable ubiquitous and reliable application, a gait recognition system must be robust to environment changes and easy to use without requiring too much user cooperation and recalibration, while maintaining high accuracy, which is often not satisfied in conventional approaches. In this article, we present$\boldsymbol {GaitCube}$, a high-accuracy gait recognition system with the minimal training requirement using a single commodity millimeter-wave (mmWave) radio. To reduce the training overhead, we proposegait data cube, a novel 3-D joint-feature representation of micro-Doppler and micro-range signatures over time that can comprehensively embody the physical relevant features of one’s gait. With a pipeline of signal processing,$\boldsymbol {GaitCube}$can automatically detect and segment human walking and effectively extract thegait data cubes. We implement and evaluate$\boldsymbol {GaitCube}$through experiments conducted at six different locations in a typical indoor space with ten subjects over a month, resulting in >50000 gait instances. The results show that$\boldsymbol {GaitCube}$achieves an accuracy of 96.1% with a single gait cycle using one receive antenna, and the accuracy increases to 98.3% when combining all the receive antennas. Further, it achieves an average recognition accuracy of 79.1% for testing over different times and unseen locations by using only 2 min of training data collected in a single location, enabling a practical and ubiquitous gait-based identification.

As automobiles have become an essential part to facilitate our daily life, advanced driver assistance systems (ADASs) have been gaining more and more interest in assisting drivers to enhance both safety and convenience. To respond timely in case of an emergency, ADAS needs to keep track of the driver’s health/consciousness, which is generally achieved by monitoring the driver’s vital signs, including respiration rate (RR), heart rate (HR), and heart rate variability (HRV). However, most of the state-of-art solutions need to assume that the human is stationary, which does not hold in practical driving scenarios. To tackle the problem, we propose a novel system, which can estimate driver’s RR, HR, and interbeat intervals (IBIs) in the presence of driver’s motion artifacts using commercial millimeter-wave (mmWave) radio. The system consists of two key components. First, to extract the reflection signals containing vital signals, the motion artifacts are first removed by a novel motion compensation module, followed by the periodicity check to identify the components with vital signals. Second, the respiration and heartbeat signals are reconstructed by jointly optimizing the decomposition of all the extracted compound vital signals over different range-azimuth bins. We evaluate the system performance in a real driving environment and investigate the impact of different parameters, including the device locations, pavement conditions, and motion types. The experimental results show that the proposed system can achieve a median error of 0.16 respiration per minute (RPM), 0.82 beat per minute (BPM), and 46 ms for RR, HR, and IBI estimations, corresponding to the relative accuracy of 99.17%, 98.94%, and 94.11%, respectively.

Child presence detection (CPD) is becoming a regulatory requirement for car manufacturers to save children’s lives when they are left alone in unattended vehicles. However, most of the existing solutions require dedicated devices and suffer from limited accuracy and coverage. In this article, we build WiCPD, the first-of-its-kind in-car CPD system using commodity Wi-Fi, which can cover the entire interior of a car with no blind spot. First, we introduce a statistical electromagnetic model which accounts for the impact of motion on all the multipaths inside a car, followed by a motion statistics metric indicating the ambient motion intensity and a signal-to-noise-ratio (SNR) boosting scheme to extract the minute chest movement. Then, we design a unified CPD framework consisting of three target detector modules, including a motion target detector to detect a child in motion/awake, a stationary target detector to detect a stationary/sleeping child, and a transition target detector to detect a sleeping child with sporadic motion who is missed by both the motion and stationary target detectors. We implement a real-time WiCPD system by using commercial Wi-Fi chipsets, deploy it over 20 different cars, and collect data for multiple children aging from 4 to 50 months. The results show that WiCPD can achieve 100% detection rate within 8 s when the child is awake/in-motion and 96.56% detection rate within 20 s for a static/sleeping child. Extensive experiments also demonstrate that WiCPD can be easily deployed in minutes without calibration and enjoys very low CPU and memory consumption, thus promising a practical candidate for CPD applications.

Motion detection based on WiFi signals has gained much attention recently. However, most of the existing approaches can only detect motion in a large coverage area without knowing how far the target motion happens. In this paper, we propose two robust and responsive features in the frequency dimension, which are sensitive to the distance of motion, and establish the connection between the underlying radio propagation properties and the features. Extensive experiments in various environments demonstrate that the proposed proximity detection scheme can achieve true positive rates greater than 90% and 98% in corridor and room scenarios, respectively, while maintaining the corresponding false positive rates less than 5% and 1%. The responsiveness of the proposed scheme is verified by measured detection delays within 1.5 s for a detection distance of 2 m.

With the proliferation of smart devices, voice interfaces have become an integral part of our lives, which typically senses sound by microphones through converting the changes in air pressure into electrical signals. Sound sensing through another modality can enable various sensing applications in the absence of a microphone. In fact, environmental sound creates tiny vibrations on object surfaces, which could be captured by radio signals. In this work, we model the vibration on object surfaces due to sound for mmWave devices. We propose a method for the recovery of sound and conduct experiments with various materials to investigate the feasibility of sound reconstruction. We further evaluate the effect of distance and placement to understand the practical limits on the sound reconstruction. The results show that, by using a commodity off-the-shelf radar, it is possible to capture a significant amount of sound from the environment.

Device-free (passive) gesture recognition offers an enormous potential to simplify Human-Computer Interaction (HCI) in future smart environments. WIFI-based gesture recognition approaches have attained acclaim amongst others due to the omnipresence, privacy-preservation, and broad coverage of WIFI. However, there is no universal solution built on off-the-shelf devices that can accommodate an expandable set of gestures in a through-the-wall setting. In this work, we propose such a gesture recognition system that can recover information about the actual trajectory of the hand movement allowing an expandable set of gestures. Further, we leverage the rich multipath in a through-the-wall setting to develop a statistical model for the channel variations induced by a hand gesture. This model is used to derive a correspondence between the relative distance moved by the hand and the Time Reversal Resonating Strength (TRRS) decay. Based on this relation and the geometry of the gesture shape, we design feature extraction modules to enable gesture classification. We built a prototype of the proposed system on off-the-shelf WIFI devices and achieved a classification accuracy of 87% on a set of 6 uppercase English alphabets.

Heart Rate Variability (HRV), which measures the fluctuation of heartbeat intervals, has been considered as an important indicator for general health evaluation. In this paper, we present mmHRV, a contact-free HRV monitoring system using commercial millimeter-wave (mmWave) radio. We devise a heartbeat signal extractor, which can optimize the decomposition of the phase of the channel information modulated by the chest movement, and thus estimate the heartbeat signal. The exact time of heartbeats is estimated by finding the peak location of the heartbeat signal while the Inter-Beat Intervals (IBIs) can be further derived for evaluating the HRV metrics. Experimental results show that mmHRV can measure the HRV accurately with 3.68ms average error of mean IBI (w.r.t. 99.49% accuracy) based on the experiments over 10 participants.

While high accuracy tracking of targets has been extensively explored because of its wide applications, many exiting methods degenerate in the presence of multipath distortions. This paper proposes an accurate and novel multipath-resilient system to track the targets by leveraging the large number of antennas in massive MIMO systems. We first prove that statistical autocorrelation of the received energy physically shows a sinc-like distribution around the receiver in far-field scenario. Based on such an observation, a novel method is developed to estimate the moving speed of the target with respect to a single base station. The absolute moving speed and direction are further estimated by using the geometrical relationships among multiple base stations and thus we can track the target by dead-reckoning using the consecutive moving speed and moving direction estimations. Numerical simulations show that the proposed system can achieve decimeter-lever accuracy for tracking in various environments, which outperforms the existing methods.

In the era of pervasively connected and sensed Internet of Things, many of our interactions with machines have been shifted from conventional computer keyboards and mouses to hand gestures and writing in the air. While gesture recognition and handwriting recognition have been well studied, many new methods are being investigated to enable pervasive handwriting tracking. Most of the existing handwriting tracking systems either require cameras and handheld sensors or involve dedicated hardware restricting user convenience and the scale of usage. In this article, we present mmWrite, the first high-precision passive handwriting tracking system using a single commodity millimeter-wave (mmWave) radio. Leveraging the short wavelength and large bandwidth of 60-GHz signals and the radar-like capabilities enabled by the large phased array, mmWrite transforms any flat region into an interactive writing surface that supports handwriting tracking at millimeter accuracy. MmWrite employs an end-to-end pipeline of signal processing to enhance the range and spatial resolution limited by the hardware, boost the coverage, and suppress interference from backgrounds and irrelevant objects. We implement and evaluate mmWrite on a commodity 60-GHz device. The experimental results show that mmWrite can track a finger/pen with a median error of 2.8 mm and thus can reproduce handwritten characters as small as 1 cm × 1 cm, with a coverage of up to 8 m2supported. With minimal infrastructure needed, mmWrite promises ubiquitous handwriting tracking for new applications in the field of human-computer interactions.

The continuous development of 802.11ad technology provides new opportunities in wireless sensing. In this work, we propose ViMo, a calibration-free remote vital sign monitoring system that can detect stationary/nonstationary users and estimate the respiration rates (RRs) as well as heart rates (HRs) built upon a commercial 60-GHz WiFi. The design of ViMo consists of two key components. First, we design an adaptive object detector that can identify static objects, stationary human subjects, and human in motion without any calibration. Second, we devise a robust HR estimator, which eliminates the respiration signal from the phase of the channel impulse response (CIR) to remove the interference of the harmonics from breathing and adopts dynamic programming (DP) to resist the random measurement noise. The influence of different settings, including the distance between a human and the device, user orientation and incidental angle, blockage material, body movement, and conditions of multiuser separation is investigated by extensive experiments. The experimental results show that ViMo monitors user’s vital signs accurately, with a median error of 0.19 and 0.92 breaths per minute (BPM), respectively, for RR and HR estimation.

Heart rate variability (HRV), which measures the fluctuation of heartbeat intervals, has been considered as an important indicator for general health evaluation. To alleviate the user burden and explore the usability for long-term health monitoring, noncontact methods for HRV monitoring have drawn tremendous attention. In this article, we present mmHRV, the first contact-free multiuser HRV monitoring system using commercial millimeter-wave (mmWave) radio. The design of mmHRV consists of two key components. First, we develop a calibration-free target detector to identify each user’s location. Second, a heartbeat signal extractor is devised, which can optimize the decomposition of the phase of the channel information modulated by the chest movement and, thus, estimate the heartbeat signal. The exact time of heartbeats is estimated by finding the peak location of the heartbeat signal while the interbeat intervals (IBIs) can be further derived for evaluating the HRV metrics of each target. We evaluate the system performance and the impact of different settings, including the distance between human and the device, user orientation, incidental angle, and blockage. Experimental results show that mmHRV can measure the HRV accurately with a median IBI estimation error of 28 ms (with respect to 96.16% accuracy). In addition, the root-mean-square error (RMSE) measured in the nonline-of-sight (NLOS) scenarios is 31.71 ms based on the experiments with 11 participants. The performance of the multiuser scenario is slightly degraded compared with the single-user case; however, the median error of the 3-user case is within 52 ms for all three tested locations.

High-accuracy target localization and tracking have been widely used in the modern navigation system. However, most of the methods such as global positioning system (GPS) are highly dependent on time measurement accuracy, which prevents them from achieving high accuracy in practice. Time reversal (TR)-based technique has been shown to be able to achieve centimeter accuracy localization by fully utilizing the focusing effect brought by the massive multipaths naturally existing in a rich scattering environment such as indoor scenarios. By investigating a similar statistical property, this article develops a novel high-accuracy target localization method by using massive MIMO to provide massive signal components. We first observe that the statistical autocorrelation of the received energy physically focuses into a beam around the receiver exhibiting a sinc-like distribution in the far-field scenario. By leveraging such a distribution of the focusing beam, an effective way to estimate the relative moving speed of the target with respect to a single base station is proposed. We also obtain the absolute moving speed and subsequently track the target accurately by associating the speed estimation results and geometrical relationship of multiple stations. The theoretical analysis on the error in the speed and localization estimation validated by numerical simulation results show that the proposed system can achieve decimeter accuracy for target localization and tracking.

RF imaging is a dream that has been pursued for years yet not achieved in the evolving wireless sensing. The existing solutions on WiFi bands, however, either require specialized hardware with large antenna arrays or suffer from poor resolution due to fundamental limits in bandwidth, the number of antennas, and the carrier frequency of 2.4 GHz/5 GHz WiFi. In this article, we observe a new opportunity in the increasingly popular 60-GHz WiFi, which overcomes such limits. We present mmEye, a super-resolution imaging system toward a millimeter-wave camera by reusing a single commodity 60-GHz WiFi radios. The key challenge arises from the extremely small aperture (antenna size), e.g., <; 2 cm, which physically limits the spatial resolution. mmEye's core contribution is a super-resolution imaging algorithm that breaks the resolution limits by leveraging all available information at both the transmitter and receiver sides. Based on the MUSIC algorithm, we devise a novel technique of joint transmitter smoothing, which jointly uses the transmit and receive arrays to boost the spatial resolution while not sacrificing the aperture of the antenna array. Built upon this core, we design and implement a functional system on commodity 60-GHz WiFi chipsets. We evaluate mmEye on different persons and objects under various settings. Results show that it achieves a median silhouette (shape) difference of 27.2% and a median boundary keypoint precision of 7.6 cm, and it can image a person even through a thin drywall. The visual results show that the imaging quality is close to that of commercial products like Kinect, making for the first-time super-resolution imaging available on the commodity 60-GHz WiFi devices.

Interests in monitoring and recognizing gait have surged significantly over the past decades. Traditional approaches rely on camera array, floor sensors (e.g., pressure mats), or wearables (e.g., accelerometers), none of which are suitable for continuous and ubiquitous everyday use. In this article, we present GaitWay, the first system that monitors and recognizes an individual's gait through the walls via wireless radios. GaitWay passively and unobtrusively monitors an individual's gait speed by a single pair of commodity WiFi transceivers, without requiring the user to wear any device or walk on a restricted walkway. On this basis, GaitWay automatically identifies stable walking periods, extracts physically plausible and environmentally irrelevant speed features, and accordingly recognizes a subject's gait. Built upon a distinct rich-scattering multipath model, GaitWay can capture one's gait speed when one is $>$ >10 meters away behind the walls. We conduct experiments in a typical indoor space and perform eight sessions of data collection with 11 subjects across six months, resulting in $>$ >5,000 gait instances. The results show that GaitWay achieves a median 0.12 m/s and 90%tile 0.35 m/s error in speed estimation, with a mean error of 3.36 cm in stride lengths. Further, it achieves a verification rate of 90.4% and a recognition rate of 81.2% for five users and 69.8% for 11 users, confirming its comfort and accuracy for continuous and ubiquitous use.

We present the model, design, and implementation of SMARS, the first practical Sleep Monitoring system that exploits Ambient Radio Signals to recognize sleep stages and assess sleep quality. This will enable a future smart home that monitors daily sleep in a ubiquitous, non-invasive and contactless manner, without instrumenting the subject's body or the bed. The key enabler underlying SMARS is a statistical model that accounts for all reflecting and scattering multipaths, allowing highly accurate and instantaneous breathing estimation with best-ever performance achieved on commodity devices. On this basis, SMARS then recognizes different sleep stages, including wake, rapid eye movement (REM), and non-REM (NREM), which was previously only possible with dedicated hardware. We implement a real-time system on commercial WiFi chipsets and deploy it in 6 homes, resulting in 32 nights of data in total. Our results demonstrate that SMARS yields a median absolute error of 0.47 breaths per minute (BPM) and a 95 percent-tile error of only 2.92 BPM for breathing estimation, and detects breathing robustly even when a person is 10 meters away from the link, or behind a wall. SMARS achieves a sleep staging accuracy of 88 percent, outperforming the prevalent unobtrusive commodity solutions using bed sensor or UWB radar. The performance is also validated upon a public sleep dataset of 20 patients. By achieving promising results with merely a single commodity RF link, we believe that SMARS will set the stage for a practical in-home sleep monitoring solution.

Heading direction information is crucial to many ubiquitous computing applications. The main stream has been resorting to inertial sensors, such as accelerometer, gyroscope and magnetometer, which suffer from severe accumulative errors or large degradations indoors. In this paper, we utilize the radio frequency (RF) signals, received from the commercial off-the-shelf (COTS) WiFi devices, to accurately estimate the heading direction in indoor environments. Based on the time- reversal (TR) technique, we make use of the channel state information (CSI) and the geometry of the antenna array to design the proposed algorithm. A prototype is built using a single access point (AP), without knowing its location, and a two dimensional (2D) antenna array to validate the proposed method. Experiments, conducted in strong non-line-of-sight (NLOS) scenarios with rich multipaths indoors, have shown that the median error for heading direction estimation is 6.9°, which surpasses the inertial sensors. With the high accuracy and low cost, it illustrates the proposed system as a promising solution to large varieties of applications that require accurate heading direction information.

Fall detection systems based on WiFi signals are gaining popularity recently. However, most of the existing works relying on training are environment-dependent. In this paper, we propose DeFall, a novel WiFi-based environment-independent fall detection system by leveraging the features inherently associated with human falls - the patterns of speed and acceleration over time. The system consists of an offline template-generating stage and an online decision-making stage. In the offline stage, the speed of human falls is first estimated based on a statistical modeling about the Channel State Information (CSI). Dynamic Time Warping (DTW) based algorithms are applied to generate a representative template for typical human falls. Then fall event is detected in the online stage by evaluating the similarity between the patterns of realtime speed/acceleration estimates and the representative template. Extensive experiment results show that with a single pair of WiFi transceivers, the proposed system can achieve a detection rate of 96% and a false alarm rate smaller than 1.5% under both line-of-sight (LOS) and non-LOS (NLOS) scenarios.

Gesture recognition using wireless sensing opened a plethora of applications in the field of human-computer interaction. However, most existing works are not robust without requiring wearables or tedious training/calibration. In this work, we propose WiGRep, a time reversal based gesture recognition approach using Wi-Fi, which can recognize different gestures by counting the number of repeating gesture segments. Built upon the time reversal phenomenon in RF transmission, the Time Reversal Resonating Strength (TRRS) is used to detect repeating patterns in a gesture. A robust low-complexity algorithm is proposed to accommodate possible variations of gestures and indoor environments. The main advantages of WiGRep are that it is calibration-free and location and environment independent. Experiments performed in both line of sight and non-line-of-sight scenarios demonstrate a detection rate of 99.6% and 99.4%, respectively, for a fixed false alarm rate of 5%.

Accurate monitoring of human vital signs (e.g. breathing and heart rates) is crucial in detecting medical problems. In this paper, we propose ViMo, a calibration-free remote Vital sign Monitoring system that can simultaneously monitor multiple users by leveraging the channel impulse response (CIR) of 60GHz WiFi. By exploiting the periodicity introduced by respiration, we first propose a human detection algorithm which does not require any prior calibration. Then, we apply the auto-correlation function (ACF) of the CIR phase to estimate the breathing rate. Lastly, to mitigate the impact of the breathing signal on the weak heartbeat signal, the cubic spline interpolation is used to eliminate the breathing signal before the estimation of the heart rate. Extensive experiments show that ViMo can achieve a median accuracy of 0.19 BPM for breathing rate estimation and 1 BPM for heart rate estimation, out-performing the existing non-contact solutions that are purely based on frequency analysis.

Passive human localization and tracking using RF signals have been studied for over a decade. Most of the existing solutions, however, can only track a single moving subject due to the coarse multipath resolvability limited by bandwidth and antenna number. In this paper, we break down the limitations by leveraging the emerging 60GHz millimeter-wave radios. We present mmTrack, the first system that passively localizes and tracks multiple users simultaneously using a single commodity 60GHz radio. The design of mmTrack consists of three key components. First, we significantly improve the spatial resolution, limited by the small aperture of the compact 60GHz array, by performing digital beamforming over all receive antennas. Second, we propose a novel multi-target detection approach that tackles the near-far-effect and measurement noise. Finally, we devise a robust clustering technique to accurately recognize multiple targets and estimate the respective locations, from which their individual trajectories are further derived by a continuous tracking algorithm. We implement mmTrack on a commodity 802.11ad device and evaluate it in indoor environments. Our experiments demonstrate that mmTrack detects and counts multiple users precisely with an error ≤ 1 person for 97.8% of the time and achieves a respective median location error of 9.9 cm and 19.7 cm for dynamic and static targets.

Existing indoor location systems do not easily scale at low cost while maintaining high accuracy. We present EasiTrack, an indoor tracking system that achieves decimeter accuracy using a single commodity WiFi Access Point (AP) under Non-Line-Of-Sight conditions and can deploy at scale with (almost) zero cost. We build a fully functional real-time system with a satellite-like architecture, which enables EasiTrack to support an unlimited number of clients. We have demonstrated EasiTrack in a number of different scenarios to track both humans and machines. The results reveal that EasiTrack achieves a decimeter median accuracy and a <2m maximum error and supports a broad coverage of 50 m×60 m using a single AP.

In the present evolving world, automobiles have become an intelligent electronic machine and are no longer a mere transport medium. In this article, we attempt to make them smarter by introducing the idea of in-car driver authentication using wireless sensing and develop a system that can recognize drivers automatically. The proposed system can recognize human identity by identifying the unique radio biometric information recorded in the channel state information (CSI) through multipath propagation. However, since the environmental information is also captured in the CSI, the performance of radio biometric recognition may be degraded by the changing environment. In this article, we first address the problem of “in-car changing environments” where the existing wireless sensing-based human identification system fails. We build a long-term driver radio biometric database consisting of radio biometrics of seven people collected over a period of two months. We leverage this database to create machine learning models that make the proposed system adaptive to new in-car environments. Second, we study the performance of the in-car driver authentication system with increasing effective bandwidth. We realize an effective bandwidth of 960 MHz by exploiting the multiantenna and frequency diversities in commercial WiFi devices. The performance of the proposed system is shown to improve with increasing effective bandwidth and the long-term experiments demonstrate the feasibility and accuracy of the proposed system. The accuracy achieved in the two-driver scenario is up to 99.13% for the best case.

Wireless detection of respiration rates is crucial for many applications. Most of the state-of-the-art solutions estimate breathing rates with the prior knowledge of crowd numbers as well as assuming the distinct breathing rates of different users, which is neither natural nor realistic. However, few of them can leverage the estimated breathing rates to recognize human subjects (also known as identity matching). In this article, using the channel state information (CSI) of a single pair of commercial WiFi devices, a novel system is proposed to continuously track the breathing rates of multiple persons without such impractical assumptions. The proposed solution includes an adaptive subcarrier combination method that boosts the signal-to-noise ratio (SNR) of breathing signals, and iterative dynamic programming and a trace concatenating algorithm that continuously tracks the breathing rates of multiple users. By leveraging both the spectrum and time diversity of the CSI, our system can correctly extract the breathing rate traces even if some of them merge together for a short time period. Furthermore, by utilizing the breathing traces obtained, our system can do people counting and recognition simultaneously. Extensive experiments are conducted in two environments (an on-campus lab and a car). The results show that 86% of average accuracy can be achieved for people counting up to four people for both cases. For 97.9% out of all the testing cases, the absolute error of crowd number estimates is within 1. The system achieves an average accuracy of 85.78% for people recognition in a smart home case.

Despite decades of efforts, existing indoor location systems do not easily scale with low cost while maintaining high accuracy. We present EasiTrack, an indoor tracking system that achieves decimeter accuracy using a single commodity WiFi access point (AP) under non-line-of-sight (NLOS) conditions and can deploy at scale with almost zero cost. EasiTrack makes two key technical contributions. First, it incorporates RF-based inertial measurement algorithms that can accurately infer a target's moving distance purely using the RF signals received by itself. Second, EasiTrack devises a map-augmented tracking algorithm that outputs fine-grained locations by jointly leveraging the distance estimates and an indoor map that is ubiquitously available nowadays. We build a fully functional real-time system centering around a satellite-like architecture, which enables EasiTrack to support an unlimited number of clients. We have deployed EasiTrack in seven different scenarios (including offices, hotels, museums, and manufacturing facilities) to track both humans and machines. The results reveal that EasiTrack achieves a median 0.25 m and 90%tile 0.69-m accuracy in distance measurement, a median 0.58 m and 90%tile 1.33-m location accuracy for tracking objects, and a median 0.70 m and 90%tile 1.97-m accuracy for tracking humans in both line-of-sight and NLOS scenarios and supports a broad coverage of 50 m × 60 m using a single AP. It is also verified that EasiTrack can be easily deployed in massive buildings with little cost, promising a practical solution for ubiquitous indoor tracking.

Automobiles have become an essential part of everyday lives. In this work, we attempt to make them smarter by introducing the idea of in-car driver authentication using wireless sensing. Our aim is to develop a model which can recognize drivers automatically. Firstly, we address the problem of "changing in-car environments", where the existing wireless sensing based human identification system fails. To this end, we build the first in-car driver radio biometric dataset to understand the effect of changing environments on human radio biometrics. This dataset consists of radio biometrics of five people collected over a period of two months. We leverage this dataset-to create machine learning (ML) models that make the proposed system adaptive to new in-car environments. We obtained a maximum accuracy of 99.3% in classifying two drivers and 90.66% accuracy in validating a single driver.

Inertial measurements are critical to almost any mobile applications. It is usually achieved by dedicated sensors (e.g., accelerometer, gyroscope) that suffer from significant accumulative errors. This paper presents RIM, an RF-based Inertial Measurement system for precise motion processing. RIM turns a commodity WiFi device into an Inertial Measurement Unit (IMU) that can accurately track moving distance, heading direction, and rotating angle, requiring no additional infrastructure but a single arbitrarily placed Access Point (AP) whose location is unknown. RIM makes three key technical contributions. First, it presents a spatial-temporal virtual antenna retracing scheme that leverages multipath profiles as virtual antennas and underpins measurements of distance and orientation using commercial WiFi. Second, it introduces a super-resolution virtual antenna alignment algorithm that resolves sub-centimeter movements. Third, it presents an approach to handle measurement noises and thus delivers an accurate and robust system. Our experiments, over a multipath rich area of > 1,000 m2 with one single AP, show that RIM achieves a median error in moving distance of 2.3 cm and 8.4 cm for short-range and long-distance tracking respectively, and 6.1° mean error in heading direction, all significantly outperforming dedicated inertial sensors. We also demonstrate multiple RIM-enabled applications with great performance, including indoor tracking, handwriting, and gesture control.

By sensing wirelessly the radio propagation environment and analyzing the channel state information (CSI), one can extend human senses beyond our traditional reach and enrich the insight into the surrounding environment and activities, with or without line-of-sight. On one hand, different indoor activities bring distinctive perturbations to wireless radio propagations. On the other hand, thanks to the nature of multipaths, indoor environmental information is contained and embedded in the wireless CSI. Since the occurrence of an indoor event lasts for a certain period of duration and repeats a similar transition pattern among different realizations, information is embedded not only in each instantaneous CSI sample, but also in how CSI changes along time, e.g., the CSI time series. Inspired by that, this paper proposes an indoor monitoring system that monitors the occurrence of different indoor events in real time with commercial WiFi devices, by exploiting the temporal information embedded in the CSI time series. Through extensive experiments, this paper studies the robustness of the proposed system to variabilities in event instances and human motion interference, and its long-term performance in a one-month test.

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.

Environmental information is recorded in the multipath propagation, and can be accessed in the form of channel state information (CSI) through commodity WiFi devices. A single CSI reading for event detection is adopted in most existing CSI based indoor monitoring system. However, due to the impact of noise, event inconsistency and environmental dynamics, this type of approaches is not very robust. In this work, we design efficient algorithms to fully exploit the information embedded in the CSI time series to combat interference introduced by CSI perturbations, and propose an indoor event monitoring system that achieves accurate real-time monitoring. The accuracy and robustness of the proposed system is evaluated through experiments, which illustrate its potential in future smart home applications.

In a rich-scattering environment, radio frequency (RF) devices communicate through multipath channels and the channel state information (CSI) is uniquely determined by the location of the transmitter or receiver devices along with surrounding environments. Whenever one of the devices moves, the CSI changes accordingly. In other words, there is a one-to-one mapping between the CSI and the location of RF devices. Inspired by the relationship, we propose a real-time indoor tracking system that utilizes time-reversal (TR) technique to capture differences in the CSI and then accurately locate the moving RF device along its trajectory. Moreover, a real-time speed estimation algorithm is designed based on the spatial distribution of the TR resonance. A prototype is built to validate the accuracy and robustness of the proposed system through a train tracking experiment. It illustrates the TR technique as a promising solution to high-precision indoor tracking applications.

Motion detection as a key component in modern security systems has received an increasing attention recently, but most existing solutions require special installation, calibration, and only have a limited coverage. In this paper, we propose WiDetect, a highly accurate, calibration-free, and low-complexity wireless motion detector. By exploiting the statistical theory of electromagnetic waves, we establish a link between the autocorrelation function of the physical layer channel state information (CSI) and motion in the environment. Temporal, frequency and spatial diversity are also exploited to further improve the robustness and accuracy of WiDetect. Extensive experiments conducted in several facilities show that WiDetect can achieve similar detection performance compared to a commercial home security system, while with much larger coverage and lower cost.

In this paper, online convex optimization (OCO) problem with time-varying constraints is studied from the perspective of an agent taking sequential actions. Both the objective function and the constraint functions are dynamic and unknown a priori to the agent. We first consider the scenario of function feedback, in which complete information about the objective function and constraint functions is revealed to the agent after an action is submitted. We propose a computationally efficient online algorithm, which only involves direct closed-form computations at each time instant. It is shown that the algorithm possesses sublinear regret with respect to the dynamic benchmark sequence and sublinear constraint violations, as long as the drift of the benchmark sequence is sublinear, or in other words, the underlying dynamic optimization problems do not vary too drastically. Furthermore, we investigate the scenario of bandit feedback, in which, after an action is chosen, only the values of the objective function and the constraint functions at several random points close to the action are announced to the agent. A bandit version of online algorithm is proposed and we also establish its sublinear expected regret and sublinear expected constraint violations. Finally, two numerical examples, namely online quadratic programming and online logistic regression, are presented to corroborate the effectiveness of the proposed algorithms and to confirm the theoretical guarantees.

In this work, we study a generic network cost minimization problem, in which every node has a local decision vector to determine. Each node incurs a cost depending on its decision vector and each link also incurs a cost depending on the decision vectors of its two end nodes. All nodes cooperate to minimize the overall network cost. To obtain a decentralized algorithm for this problem, we resort to the distributed alternating direction method of multipliers (DADMM). However, each iteration of the DADMM involves solving a local optimization problem at each node, leading to intractable computational burden in many circumstances. As such, we propose a distributed linearized ADMM (DLADMM) algorithm, in which each iteration only involves closed-form computations and avoids local optimization problems. This greatly reduces the computational complexity and makes the proposed DLADMM amenable to devices with low computational capability, such as vastly deployed sensors, to which the computationally intensive traditional DADMM is not applicable. We prove that the DLADMM converges to an optimal point when the local cost functions are convex and have Lipschitz continuous gradients. Linear convergence rate of the DLADMM is also established if the local cost functions are further strongly convex. Numerical experiments are conducted to corroborate the effectiveness of the DLADMM and we observe that the DLADMM has similar convergence performance as DADMM does while the former enjoys much lower computational overhead.

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.

As embedded in wireless signals, information of an indoor environment is captured during radio propagation, motivating the development of emerging wireless sensing technologies. In this paper, we propose a smart radio system that leverages the informative wireless radios to enable intelligent environment and extend human senses to perceive the world. In particular, owing to the time-reversal (TR) technique that captures changes in multipath profiles, the proposed TR indoor monitoring system (TRIMS) is capable of monitoring indoor events and detecting motion through walls in real time. A statistic model of intraclass TR resonance strength is developed and treated as the feature for TRIMS. Moreover, a prototype of TRIMS is implemented using commercial WiFi devices with three antennas. We investigate the performance of TRIMS in different single family houses with normal resident activities. In general, TRIMS can have a perfect detection rate with almost zero false alarm rates for seven target events, whereas during a two-week experiment TRIMS achieves a detection rate of 95.45% in the indoor multievent monitoring. The proposed TRIMS illustrates the potential of smart radio applications in smart homes, thanks to the ubiquitous WiFi.

Due to the severe multipath effect, no satisfactory device-free methods have ever been found for indoor speed estimation problem, especially in non-line-of-sight (LOS) scenarios, where the direct path between the source and observer is blocked. In this paper, we present WiSpeed, a universal low-complexity indoor speed estimation system leveraging radio signals, such as commercial WiFi, LTE, 5G, etc., which can work in both device-free and device-based situations. By exploiting the statistical theory of electromagnetic waves, we establish a link between the autocorrelation function of the physical layer channel state information and the speed of a moving object, which lays the foundation of WiSpeed. WiSpeed differs from the other schemes requiring strong LOS conditions between the source and observer in that it embraces the rich-scattering environment typical for indoors to facilitate highly accurate speed estimation. Moreover, as a calibration-free system, WiSpeed saves the users' efforts from large-scale training and fine-tuning of system parameters. In addition, WiSpeed could extract the stride length as well as detect abnormal activities such as falling down, a major threat to seniors that leads to a large number of fatalities every year. Extensive experiments show that WiSpeed achieves a mean absolute percentage error of 4.85% for device-free human walking speed estimation and 4.62% for device-based speed estimation, and a detection rate of 95% without false alarms for fall detection.

With the development of the Internet of Things technology, indoor tracking has become a popular application nowadays, but most existing solutions can only work in line-of-sight scenarios, or require regular recalibration. In this paper, we propose WiBall, an accurate and calibration-free indoor tracking system that can work well in non-line-of-sight based on radio signals. WiBall leverages a stationary and location-independent property of the time-reversal focusing effect of radio signals for highly accurate moving distance estimation. Together with the direction estimation based on inertial measurement unit and location correction using the constraints from the floorplan, WiBall is shown to be able to track a moving object with decimeter-level accuracy in different environments. Since WiBall can accommodate a large number of users with only a single pair of devices, it is low-cost and easily scalable, and can be a promising candidate for future indoor tracking applications.

The explosive growth of wireless traffic requires revolutionary wireless communication techniques. The cloud radio access network (C-RAN) is a solution to leverage the spatial multiplexing gain by utilizing a number of antennas distributed in a certain area. However, in most of the current literature, due to the limited front-haul capacity, each remote radio head (RRH) can use only a single antenna, and thus the total number of antennas can be utilized are limited. In this work, we propose a distributed baseband signal compressive quantization scheme for the uplink of a multi-antenna C-RAN where each RRH uses multiple antennas. At each RRH, the baseband signals of multiple time instants are embedded into a vector with low dimension using delay-and-add so that more bits can be allocated to each value, and the quantization noise power caused by the front-haul link capacity deficit is reduced. The delay-and-add operation is low complexity that can be realized using basic buffering and adding, and it does not require channel information in the RRHs. Therefore, the low deployment cost feature of C-RAN is preserved. As a result, a large number of antennas can be utilized by deploying a lot of multi-antenna RRHs, which provide rich spatial diversity that assists the detection which happens in the baseband units. In the symbol detection phase, the corresponding weight vectors are designed to detect the symbols from the compressive quantized baseband signal. A parallel interference cancellation algorithm is proposed to further improve the accuracy of the symbol detection. Numerical results show that the proposed scheme is efficient in tackling the front-haul capacity challenge. We also apply the proposed scheme to orthogonal frequency-division multiplexing-based multi-antenna C-RAN, where we find that the system can utilize larger bandwidth with limited front-haul capacity. It facilitates the deployment of C-RAN based on both 4G and 5G wireless communications.

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.

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.

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.

Next-generation wireless networks are expected to support exponentially increasing number of users and demands of data, which all rely on the essential media: spectrum. Notice that the 5G networks are featured either by wide bandwidth like mmWave systems, or by large-scale antennas like massive MIMO. We find those two trends, together with waveforming or beamforming, can lead to a common phenomenon: the spatial focusing effect. Based on this focusing effect, we propose a general spatial spectrum sharing framework that enables concurrent multi-users spectrum sharing without the requirement of orthogonal resource allocation. Simulation results show that both TR wideband and massive MIMO system can achieve equivalently high throughput performance with the spatial spectrum sharing scheme.

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.

Time-reversal (TR) transmission scheme has attracted more and more attention from both academia and industry due to its ability to focus the energy of a transmitted signal at an intended focal spot, both in the time and spatial domains. Based on the extensive data collected in the real world, we observe that the energy distribution around the focal spot is highly stationary and location-independent, which we call as the “TR spatial hardening effect”. This is because TR scheme adds up numerous copies of the transmitted signal bouncing off different scatterers coherently and the randomness from the environment is averaged out. We characterize the statistical behaviors of the energy distribution around the focal spot using a statistical model. By exploiting the hardening effect, the moving speed of transceivers can be estimated in indoor environments and extensive experiments show the superiority of the proposed method compared with previous works using RF signals.

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 work, we study the social learning problem, in which agents of a networked system collaborate to detect the state of the nature based on their private signals. A novel distributed graphical evolutionary game-theoretic learning method is proposed. In the proposed game-theoretic method, agents only need to communicate their binary decisions rather than the real-valued beliefs with their neighbors, which endows the method with low communication complexity. Under mean field approximations, we theoretically analyze the steady-state equilibria of the game and show that the evolutionarily stable states coincide with the decisions of the benchmark centralized detector. Numerical experiments are implemented to confirm the effectiveness of the proposed game-theoretic learning method.

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.

Next-generation wireless networks are expected to support an exponentially increasing number of users and demands of data, which all rely on the essential media: spectrum. Spectrum sharing among heterogeneous networks is a fundamental issue that determines network performance. Previous works have focused on a “dynamic spectrum access” mode associated with the cognitive radio technology. These works all focused on the discovery of available spectrum resource either in the time domain or in the frequency domain, i.e., by separating different users' transmission. To initiate a new paradigm of spectrum sharing, the unique characteristics of the technologies should be utilized in the next-generation networks. The 5G networks are featured either by wide bandwidth like mm-wave systems, or by large-scale antennas like massive MIMO. Those two trends can lead to a common phenomenon: the spatial focusing effect. Based on this focusing effect, we propose a general spatial spectrum sharing framework that enables concurrent multi-users spectrum sharing without the requirement of orthogonal resource allocation. Moreover, we design two general network association protocols either in a centralized manner or in a distributed manner. Simulation results show that both the time reversal wideband and the massive MIMO system can achieve high throughput performance with the spatial spectrum sharing scheme.

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.

Driven by the demand for supporting the rapidly increasing wireless traffic, the next-generation communication system, i.e., the 5G system, needs to accommodate a massive number of users and judiciously manage the interference. One promising candidate, the time reversal (TR) system, uses a large bandwidth and designs transmitting waveforms, such that the environment acts as a matched filter and the transmitted signal adds up coherently at the intended users. Therefore, the energy is focused only at the intended users with reduced interference to others. The other candidate, massive MIMO system, utilizes a large number of antennas to focus on the energy to the users and reduce the mutual interference. However, the massive number of users poses a limit on system performance due to increasing interuser interference, and the system has to make a judicial selection of transmitting users. In this paper, we propose a scheduler that maximizes the system weighted sum rate while satisfying the minimum rate requirements of the transmitting users. The optimization problem is transformed into a mixed integer quadratically constrained quadratic programming with linear time complexity. We also investigate the impact of imperfect channel information on the proposed scheduler algorithm and reveal similar channel estimation error distribution between the TR and massive MIMO system. We evaluate the performance of the proposed scheduler in different scenarios and the results show that the proposed scheduler has several desirable characteristics, including low time complexity, suitable on versatile system structure, and robustness against imperfect channel information.

This paper explores the rate-energy (R-E) region of simultaneous wireless information and power transfer for MIMO broadcasting channel under the nonlinear radio frequency energy harvesting (EH) model. The goal is to characterize the tradeoff between the maximal energy transfer versus information rate. The separated EH and information decoding (ID) receivers and the co-located EH and ID receivers scenarios are considered. For the co-located receivers scenario, both time switching (TS) and power splitting (PS) receiver architectures are investigated. Optimization problems are formulated to derive the boundaries of the R-E region s for the considered systems. As the problems are nonconvex, we first transform them into equivalent ones and derive some semi-closed-form solutions, and then design efficient algorithms to solve them. Numerical results are provided to show the R-E region s of the systems, which provide some interesting insights. It is shown that all practical circuit specifications greatly affect the system R-E region. Compared with the systems under traditional linear EH model, the ones under the nonlinear EH model achieve smaller R-E region s due to the limitations of practical circuit features and also show very different R-E tradeoff behaviors.

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.

In wideband communication systems, the time-reversal (TR) technique can boost the signal-to-noise ratio (SNR) at the receiver with simple single-tap detection. It has been shown that conventional waveform design can significantly improve the system performance of TR systems. However, when the symbol rate is very high, the severe intersymbol interference still limits the performance at high-power region. In this work, we study a new waveform design with interference pre-cancellation by exploiting the message information to further improve the performance. In the proposed design, the causal interference is subtracted by interference compensation, and the anticausal interference can be further suppressed by conventional waveform design by virtue of the more abundant degrees of freedom. The transmitter utilizes the information of previous symbols to enhance the signal quality while the receiver structure remains simple. In the multiuser scenario, both the interuser interference and intersymbol interference can be similarly categorized by the causality, and then be tackled accordingly by the proposed waveform design with interference pre-cancellation. The resulting multiuser waveform design is a nonconvex optimization problem, for which two iterative algorithms are proposed and both are guaranteed to converge to suboptimal solutions. Simulation results validate the convergence behavior and demonstrate the remarkable performance improvement over the conventional waveform design in the previous work.

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.

Identifying a signal's origin and how it was acquired is an important forensic problem. While forensic techniques currently exist to determine a signal's acquisition history, these techniques do not account for the possibility that a signal could be compressively sensed. This is an important problem since compressive sensing techniques have seen increased popularity in recent years. In this paper, we propose a set of forensic techniques to identify signals acquired by compressive sensing. We do this by first identifying the fingerprints left in a signal by compressive sensing. We then propose two compressive sensing detection techniques that can operate on a broad class of signals. Since compressive sensing fingerprints can be confused with fingerprints left by traditional image compression techniques, we propose a forensic technique specifically designed to identify compressive sensing in digital images. In addition, we propose a technique to forensically estimate the number of compressive measurements used to acquire a signal. Through a series of experiments, we demonstrate that each of our proposed techniques can perform reliably under realistic conditions. Simulation results show that both our zero ratio detector and distribution-based detector yield perfect detections for all reasonable conditions that compressive sensing is used in applications, and the specific two-step detector for images can at least achieve probability of detection of 90% for probability of false alarm <;10%. In addition, our estimator for the number of compressive measurements can well reflect the real number.

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.

Random channel fading may cause serious channel outage, and automatic repeat-request protocol has been widely used to mitigate packet loss by retransmitting the packet when the receiver fails to decode that packet. To restrict the transmission delay, a single packet might be retransmitted up to a limited number of times. For wireless two-way relay channels, the packet could be retransmitted by the original source node or the relay nodes, and the delay constraint might be imposed in a per-hop manner or end-to-end manner. For single-relay networks, we study the throughput of several relaying strategies including pure relaying and network-coded relaying subject to the delay constraint. We demonstrate that the binary xor-based network coding can greatly improve the system throughput, but the throughput gain is upper bounded. We also develop a near-optimum power allocation scheme to maximize the system throughput. For multirelay networks, we show that using digital network coding alone may cause some throughput loss when the frame length is much smaller than the number of relays, and we develop a hybrid network coding scheme to recover such throughput loss.

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.

Power scheduling is an important issue for energy harvesting systems. In this work, we study the power control policy for minimizing the weighted sum of the outage probabilities under a set of predetermined transmission rates over a finite horizon. This problem is challenging in that the objective function is non-convex. To make the analysis tractable, we apply the approximation at high signal-to-noise ratios and obtain a near-optimal offline solution. In the case of infinite battery capacity, we demonstrate that the allocated power has a piecewise structure, i.e., each power scheduling cycle should be divided into disjoint segments and the normalized power should remain constant within each segment. An iterative algorithm is developed to obtain the power solution. In the case of finite battery capacity, we show that the piecewise structure still holds true, and we develop a divide-and-conquer algorithm to recursively solve the power allocation problem. Finally, we obtain a simple online power control policy that is fairly robust to prediction errors of the harvested energy. Simulations demonstrate that the proposed power solution has better performance than other strategies such as best-effort, fixed-ratio and random allocation.

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.

The concept of Time-Reversal Division Multiple Access (TRDMA) has recently been proposed as a promising medium access technology for wireless broadband communications. Previous work on TRDMA mainly focuses on the multi-user downlink system. In this paper, we first introduce a TRDMA-based multi-user uplink architecture and then propose a 2D parallel interference cancellation scheme to enhance system performance. The TRDMA uplink architecture keeps the cost of end-users at a minimum level, and reuses the processing power at the base station that has already been made available for the downlink. The proposed 2D parallel interference cancellation scheme utilizes tentative decisions of detected symbols to effectively cancel both inter-symbol interference and inter-user interference, which significantly improve the bit-error-rate (BER) performance in high SNR regime. To further improve BER performance, a multi-stage processing can be implemented by cascading multiple stages of the proposed 2D interference cancellation, with a total delay that increases linearly with the number of stages, but independent of the number of users. The BER performance of the single-stage cancellation is analyzed, and the approximated theoretical result well matches the simulation results. Simulations are provided for up-to 3 stages of interference cancellation and compared with the basic TRDMA system without interference cancellation.

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.

The concept of Time Reversal Division Multiple Access (TRDMA) has recently been proposed as a promising medium access technology for the multi-user wireless broadband communications. Compared with the existing multi-carrier technology like OFDM/OFDMA, the TRDMA provides a cost-effective single-carrier alternative technology to combat the inter-symbol interference (ISI) for broadband communications, and at the same time leverages the degrees of freedom in a large number of multi-paths to form a unique high-resolution spatial focusing effect. Previous work on TRDMA mainly focus on the multi-user downlink system. In this paper, we first introduce a TRDMA-based multi-user uplink architecture and then propose a 2-dimensional (2D) parallel interference cancellation scheme to further enhance the system performance. The TRDMA uplink architecture keeps the cost of end-users at a minimum level, and reuses the processing power at the base station (BS) that has already been made available for the downlink. The proposed 2D parallel interference cancellation scheme utilizes the tentative decisions of detected symbols to effectively cancel the interference in both the time domain (i.e. ISI) and the user domain (i.e. inter-user interference (IUI)), which significantly improve the bit-errorrate performance in the high signal-to-noise-ratio (SNR) regime. Simulation results are provided and compared with the basic TRDMA system without interference cancellation.

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.

The spatial and time focusing effects of time-reversal technology make it an ideal candidate for the low interference downlink system to relieve the coexistence issues among different wireless communication systems. The transmitting power would be concentrated at the intended receiver and the leakage to unintended receivers would be automatically suppressed. In this paper, a time-reversal based secondary user downlink system with the coexistence of primary users is proposed. The primary user's performance loss is investigated in terms of effective SINR degradation and achievable data rate deduction. It is illustrated through numerical examples that the time-reversal downlink system relieves the primary user's performance loss as well as achieves satisfying power efficiency compared with the traditional direct transmission.

In this paper, we study the power scheduling problem to optimize the outage probability with both equal and unequal transmission rates under energy harvesting constraints. In particular, we first model the average outage probability minimization problem over finite horizon, and then reformulate the problem by minimizing its upper bound, which converts the non-convex problem to a more efficient convex optimization problem. Next, we show the optimal power scheduling solution is related to the global minimum of an energy-and-time function which is defined as arithmetic average of harvested energy from current time slot to the arbitrary time slot in the future before the end of transmission. Based on such properties, we propose a Min-Average strategy which presents the optimal performance in upper bound of outage probability for equal and non-equal transmission rates. Simulation results show that, our power scheduling scheme achieves better outage performance compared to other alternative strategies, e.g., best-effort, fixed-saving and random saving strategies.

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.

Over the past decade, a number of digital forensic techniques have been developed to authenticate digital signals. One important set of forensic techniques operates by estimating signal processing components of a digital camera's signal processing pipeline, then using these estimates to perform forensic tasks such as camera identification or forgery detection. However, because these techniques are capable of estimating a camera's internal signal processing components, these forensic techniques can be used for reverse engineering. In this paper, we propose integrating an anti-forensic module into a digital camera's processing pipeline to protect against forensic reverse engineering. Our proposed technique operates by removing linear dependencies amongst an output images interpolated color values and by disrupting the color sampling grid. Experimental results show that our proposed technique can be effectively used to protect against the forensic reverse engineering of key components of a digital camera's processing pipeline.

A number of forensic techniques have been developed to identify the use of digital multimedia editing operations. In response, several anti-forensic operations have been designed to fool forensic algorithms. One operation that has received considerable attention is median filtering, since it can be used for image enhancement or anti-forensic purposes. As a result, several median filtering detectors have been developed. In this paper, we propose an anti-forensic technique to disguise the use of median filtering. We do this by first proposing a model for an unaltered image's pixel difference distribution. We then modify a median filter image's pixel difference distribution using anti-forensic noise so that it no longer contains median filtering fingerprints. Through a series of experiments, we are able to show that our anti-forensic technique can fool existing median filtering detectors under realistic conditions.

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.

With the explosive deployment of the information and communications technology (ICT) infrastructures, the rising cost of energy and increased environmental awareness has sparked a keen interest in the development and deployment of energy-efficient communication technologies. As a major player in the ICT sector, the energy efficiency of the mobile cellular networks can be significantly improved by switching off some base stations during off-peak periods. In this paper, we propose an energy-efficient BS switching strategy, and use cooperative communication techniques among the base stations to effectively extend network coverage. We incorporate both the path-loss and fading effects in our system model, and derive closed-form expressions for two important quality of service metrics, the call-blocking probability and the channel outage probability. The proposed scheme guarantees the quality of service of the user equipments by identifying the user equipments situated at the worst-case locations. The energy-saving performance is evaluated and compared with the conventional uni-pattern operation. Both analytical and numerical results show that the proposed energy-efficient switching strategy, facilitated by BS cooperation, can provide significant energy-saving potential for the cellular networks with guaranteed quality of service.

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.

In order to verify the authenticity of digital images, researchers have begun developing digital forensic techniques to identify image editing. One editing operation that has recently received increased attention is median filtering. While several median filtering detection techniques have recently been developed, their performance is degraded by JPEG compression. These techniques suffer similar degradations in performance when a small window of the image is analyzed, as is done in localized filtering or cut-and-paste detection, rather than the image as a whole. In this paper, we propose a new, robust median filtering forensic technique. It operates by analyzing the statistical properties of the median filter residual (MFR), which we define as the difference between an image in question and a median filtered version of itself. To capture the statistical properties of the MFR, we fit it to an autoregressive (AR) model. We then use the AR coefficients as features for median filter detection. We test the effectiveness of our proposed median filter detection techniques through a series of experiments. These results show that our proposed forensic technique can achieve important performance gains over existing methods, particularly at low false-positive rates, with a very small dimension of features.

Network coding can improve the bandwidth efficiency by letting multiple users share the relay channels. However, the multi-user interferences (MUI) at the intended receiver may degrade the diversity performances. So in this work, we study several diversity strategies for the multiple-access relay channel and analyze the impact of MUI on the diversity performances. Both the variable gain relaying (VGR) and the fixed gain relaying (FGR) are considered, where the relay nodes are subject to instantaneous power constraints and long-term power constraints, respectively. We first develop a Min-Max relay selection strategy and prove that full diversity can be achieved. Next, the distributed space-time block coding (DSTBC) and the diagonal distributed space-time coding (DDSTC) are studied. For DSTBC, we show that full diversity can be achieved by FGR, but the diversity of VGR is upper bounded by \min (L+1, K+1), where L and K are the number of relays and users, respectively. For single-user systems, we then develop a selective DSTBC-VGR scheme to recover the diversity loss by adaptively allocating the relay power. Finally for DDSTC, we show that both FGR and VGR can achieve full diversity, and the optimum code design criterion is to maximize the minimum product distance.

Network coding has been widely used in the wireless uplink. To do coherent detection at the receiver, channel estimation is performed to collect the channel state information (CSI). For multi-user channels, the channel estimation overhead is sometimes formidable and may even outweigh the network coding gain. To address this issue, we study the non-coherent network coding in this work. Both the analog network coding (ANC) and digital network coding (DNC) are studied. Depending on the available CSI, we first develop the coherent and non-coherent maximum likelihood receivers. For ANC, as the non-coherent receivers have no closed form, we develop two suboptimum receivers according to the average link qualities. Next we study the error rates, and show that full diversity can always be achieved at extremely high signal-to-noise ratios (SNR) regardless of the CSI assumptions. However, the lack of perfect CSI would incur some diversity loss at modest SNRs. Besides, the performance loss of ANC is more serious due to the incapability to efficiently suppress multi-user interferences at the receivers. Extensive simulations are performed to verify our analytical results.

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.

Utilizing channel reciprocity, the traditional time-reversal technique boosts the signal-to-noise ratio at the receiver with very low transmitter complexity. However, the large delay spread gives rise to severe inter-symbol interference (ISI) when the data rate is high, and the achievable transmission rate is further degraded in the multiuser downlink due to the inter-user interference (IUI). In this work, we study the weighted sum rate optimization problem by means of waveform design in the time-reversal multiuser downlink where the receiver processing is based on a single sample. Power allocation has a significant impact on the waveform design problem. We propose a new power allocation algorithm named Iterative SINR Waterfilling, which is able to achieve comparable sum rate performance to that of globally optimal power allocation. We further propose another approach called Iterative Power Waterfilling for multiple data streams. Iterative SINR Waterfilling provides better performance than Iterative Power Waterfilling in the scenario of high interference, while Iterative Power Waterfilling can work under multiple data streams. Simulation results show the superior performance of the proposed algorithms in comparison with other waveform designs such as zero-forcing and conventional time-reversal waveform.

Analog network coding (ANC) has been widely used in wireless uplink to improve throughput and provide spatial diversity. However, the receiver has to estimate the channel coefficients of all users to perform coherent detection, thus the signaling overhead is sometimes formidable and may even outweigh the performance gain. To reduce the channel estimation overhead, we study non-coherent modulations in this work with emphasis on receiver design and performance analysis. Depending on channel state information, we first develop the coherent, partial coherent and non-coherent receivers based on maximum likelihood (ML) principle. As the ML non-coherent receiver has a non-tractable integral form, we further propose two suboptimum receivers depending on the relative quality of source-relay channel and relay-destination channel.We also study the pairwise error probability, and show that full diversity is still achievable at high signal-to-noise ratios using non-coherent modulations; however, the error rate decreases much slower than that of coherent systems due to the incapability to efficiently suppress multi-user interferences. Extensive simulations are also given to verify our analytical results.

Wireless network coding suffers the error propagation issues that may severely degrade the diversity performance. In this work, we develop two power scaling schemes at the relay side and two detection schemes at the receiver side, respectively, to mitigate error propagation in network-coded uplink channel and thus achieve full diversity. For the soft power scaling based link adaptive relaying, we develop a virtual channel model and demonstrate that the relay power should be such to balance the signal-to-noise ratios of the source-relay channel and relay-destination channel. As for the hard power scaling based ON-OFF relaying, we first design a decision rule based on total pairwise error probability, and then simplifies it to the threshold-based relaying strategy. At the receiver side, we show that the weighted minimum distance detection with the weight being determined by the relative link quality of source-relay channel and relay-destination channel can achieve full diversity once the global channel state information is available, otherwise the maximum likelihood detection that explicitly takes into account relay decoding error should be employed to achieve full diversity.

Many-to-one communication is a challenging problem in practice due to channel fading and multi-user interferences. In this work, a new protocol that leverages spatial diversity through space-time network coding is proposed. The N source nodes are first divided into K clusters, each having Q nodes, and the clusters send data successively in a time-division multiple access way. Each node behaves as a decode-and-forward relay to other clusters, and uses linear coding to combine the local symbol and the relayed symbols. To separate the multi-source signals, each node has a unique signature waveform, and linear decorrelator is used at the receivers. Both the exact Symbol Error Rate (SER) and the asymptotic SER at high signal-to-noise ratios of the M-ary phase-shift keying signal are studied then. It is shown that a diversity order of (N - Q + 1) can be achieved with a low transmission delay of K time slots, which is more bandwidth efficient than the existing protocols. Simulation results also justify the performance gains.

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.

Due to the ease with which digital images can be forged, a great deal of work has been done in the field of digital image forensics. A particularly important problem is to forensically determine an image's acquisition and storage history. Recent research has shown that a new acquisition technique known as compressive sensing can be used to capture a digital image. Images acquired by compressive sensing can not be easily forensically distinguished from the images captured using standard digital cameras, nor from those which have undergone Discrete Wavelet Transform (DWT) based compressions. In this paper, we propose a two step detection scheme to identify the images acquired by compressive sensing. The first step identifies unaltered images captured using standard digital cameras and the second step separates compressively sensed images from ones compressed using DWT-based techniques. Our simulations show the first step of detection can achieve a probability of detection (Pd) of 100% for probability of false alarm (Pf) less than 4%, and the second step gets Pdnearly 90% with Pfof 10%.

This paper presents a game-theoretical approach to provide a framework for optimal template selection in image prediction. Image prediction is an effective tool for coding still images and intra pictures in videos. Template matching algorithms which use neighboring blocks of the prediction target as templates have been widely used for image prediction. The assumption of these approaches is that the template has similar textural structures as the prediction target. Up to now these approaches all use pre-fixed templates for all prediction targets. However, in real images, these fixed templates are very likely to contain textures that are not or are not significant in the prediction targets and these insignificant textures introduce larger prediction residues. In this paper, we propose a coalitional game in which every pixel is treated as a player and tries to seek partners to form a coalition to capture the textural structure. By forming a coalition, every player in the coalition can obtain a gain of improving the ability of capturing the textural structure of coalition while incurring a cost of introducing textural variance within the coalition. Experimental results show that the proposed game-theoretical approach outperforms the conventional pre-fixed template matching prediction up to 2dB coding gain.

In order to combat the spread of digital forgeries, researchers have developed a variety of forensic techniques to verify the authenticity of digital multimedia files. Though many of these techniques can reliably detect traditional forgeries, recent research has shown that they can easily be fooled by anti-forensic operations designed to hide evidence of forgery. In response, new forensic techniques have been developed to detect the use of anti-forensics. In light of this, there is now a need to develop a theoretical understanding of the interactions between a forger using anti-forensics and a forensic investigator. In this paper, we propose techniques to evaluate the performance of anti-forensic algorithms along with a game theoretic framework for analyzing the interplay between forensics and anti-forensics. Furthermore, we propose a new automatic video frame deletion detection technique along with a technique to detect the use of video anti-forensics. We evaluate these techniques using our proposed analytical framework.

A new cooperative transmission protocol with network coding scheme for synchronized multi-source system are proposed. The users are organized into two groups. The users in different groups transmit in distinct time slots, while the users in the same group transmit with orthogonal waveforms. In each transmission, the user transmits his own information and relays other users' information at the same time. The proposed protocol is able to mimic the full-duplex transmission with half-duplex mode to achieve high data rate. The network code for this protocol can be designed using classical linear block codes, whose minimum Hamming distance of the network code determines the system diversity order. Hence, with well-designed network code, the proposed protocol can achieve high diversity order. Simulation results show that the proposed scheme outperforms previous work.

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.

In this work, we study the error performances of the multiple access system using analog network coding (ANC), where all the users broadcast their data to the relays and destination simultaneously in the first phase, and the relays forward the source messages that have been mixed in the air in the second phase. Depending on the type of relay power constraint, both the variable gain relaying and fixed gain relaying are considered. We show that the error rates of both schemes scale as O(log P/P2) at high signal-to-noise ratio (SNR), where P is the transmitted power. We also show that the error performance is dominated by the relay-destination channel, thus the best relay position should be close to the destination. We also demonstrate that ANC may be outperformed by the conventional amplify-and-forward scheme when the data rate is low and the user number is not large. Finally, we propose a relay selection strategy based on Min-Max criterion that can achieve full diversity when there are multiple relays.

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.

Identifying a signal's origin and how it was acquired is an important problem for digital forensics. Recently, compressive sensing has achieved substantial attention due to its ability to accurately acquire sparse signals at rates below the Nyquist rate. The increased popularity of this signal acquisition technique gives rise to a new forensic problem: is it possible to distinguish signals that have been compressively sensed from traditionally sampled ones? In our previous work, we addressed this problem of differentiating between traditionally acquired and compressively sensed images. In this paper, we examine the problem of distinguishing traditionally sampled signals from compressively sensed ones for a broader class of signals. We categorize those compressive sensing applicable signals into two cases: sparse signals with noise and nearly sparse signals. For each category, we discuss the traces left in a signal by compressive sensing and propose a corresponding detection scheme. Experimental results show that both of our proposed detection schemes can be effectively used to distinguish compressively sensed signals from traditionally sensed signals.

Sensor pattern noise (SPN) is a noise-like spread-spectrum signal inherently cast onto every digital image by each imaging device and has been recognised as a reliable device fingerprint for camera source identification (CSI) and image origin verification. It can be estimated as the noise residual between the image content and its denoised version. However, the SPN extracted from a single image can be contaminated largely by image scene because image edge noise is usually much stronger than the SPN. So the identification performance is heavily dependent upon the purity of the estimated SPN, especially for small size images because they have less and weaker SPN. Although there are some existing works dedicated to improving the performance of source camera identification, an effective method to eliminate the contamination of image scene and extract an accurate SPN is currently lacking. In this paper, we will propose an edge adaptive SPN predictor based on context adaptive interpolation (PCAI) to exclude the contamination of image scene. Different from most of the existing methods extracting SPN from wavelet high frequency coefficients, we extract SPN directly from the spatial domain with a pixel-wise adaptive Wiener filter, based on the assumption that the SPN is a white signal. Extensive experiments show that our proposed PCAI method achieves the best receiver operating characteristic (ROC) performance among all of the state-of-the-art CSI schemes on different sizes of images, and has the best performance in resisting JPEG compression (e.g. with a quality factor of 90%) simultaneously.

This paper tries to answer the questions of how to enable cooperative communications in random access networks. And, since cooperation introduces extra transmissions in the channel, what are the benefits and possible tradeoffs associated with cooperation? To answer these questions, a novel cooperative protocol for wireless networks based on IEEE 802.11 random access protocol is proposed. Cooperation is achieved through the deployment of a relay node that intelligently access the wireless medium without imposing any penalties in terms of increased collision probability. To help other network nodes, relay stores packets in its queue, and accesses the channel after each transmission attempt made by any network node. Using this scheme, relay is guaranteed uncontested access to the channel. To capture the interactions between different network nodes and the relay, the network is modeled using Markov chains in conjunction with queuing analysis. Throughput and delay performances of the proposed protocol are characterized and compared to CSMA/CA without cooperation. The results demonstrate that significant gains are achieved by the proposed cooperative protocols.

Crucial to the successful deployment of cognitive radio networks, security issues have begun to receive research interests recently. In this paper, we focus on defending against the jamming attack, one of the major threats to cognitive radio networks. Secondary users can exploit the flexible access to multiple channels as the means of anti-jamming defense. We first investigate the situation where a secondary user can access only one channel at a time and hop among different channels, and model it as an anti-jamming game. Analyzing the interaction between the secondary user and attackers, we derive a channel hopping defense strategy using the Markov decision process approach with the assumption of perfect knowledge, and then propose two learning schemes for secondary users to gain knowledge of adversaries to handle cases without perfect knowledge. In addition, we extend to the scenario where secondary users can access all available channels simultaneously, and redefine the anti-jamming game with randomized power allocation as the defense strategy. We derive the Nash equilibrium for this Colonel Blotto game which minimizes the worst-case damage. Finally, simulation results are presented to verify the performance.

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.

The multi-path effect makes high speed broadband communications a very challenging task due to the severe inter-symbol interference (ISI). By concentrating energy in both the spatial and temporal domains, time-reversal (TR) transmission technique provides a great potential of low-complexity energy-efficient communications. In this paper, a novel concept of time-reversal division multiple access (TRDMA) is proposed as a wireless channel access method based on its high-resolution spatial focusing effect. It is proposed to use TR structure in multi-user downlink systems over multi-path channels, where signals of different users are separated solely by TRDMA. Both the single-transmit-antenna scheme and its enhanced version with multiple transmit antennas are developed and evaluated in this paper. The system performance is investigated in terms of its effective signal-to-interference-plus-noise ratio (SINR), the achievable sum rate and the achievable rates with outage. And some further discussions regarding its advantage over conventional rake receivers and the impact of spatial correlations between users are given at the end of this paper. It is shown in both analytical and simulation results that desirable properties and satisfying performances can be achieved in the proposed TRDMA multi-user downlink system, which makes TRDMA a promising candidate for future energy-efficient low-complexity broadband wireless communications.

Due to the ease with which digital information can be altered, many digital forensic techniques have been developed to authenticate multimedia content. Similarly, a number of anti-forensic operations have recently been designed to make digital forgeries undetectable by forensic techniques. However, like the digital manipulations they are designed to hide, many anti-forensic operations leave behind their own forensically detectable traces. As a result, a digital forger must balance the trade-off between completely erasing evidence of their forgery and introducing new evidence of anti-forensic manipulation. Because a forensic investigator is typically bound by a constraint on their probability of false alarm (P_fa), they must also balance a trade-off between the accuracy with which they detect forgeries and the accuracy with which they detect the use of anti-forensics. In this paper, we analyze the interaction between a forger and a forensic investigator by examining the problem of authenticating digital videos. Specifically, we study the problem of adding or deleting a sequence of frames from a digital video. We begin by developing a theoretical model of the forensically detectable fingerprints that frame deletion or addition leaves behind, then use this model to improve upon the video frame deletion or addition detection technique proposed by Wang and Farid. Next, we propose an anti-forensic technique designed to fool video forensic techniques and develop a method for detecting the use of anti-forensics. We introduce a new set of techniques for evaluating the performance of anti-forensic operations and develop a game theoretic framework for analyzing the interplay between a forensic investigator and a forger. We use these new techniques to evaluate the performance of each of our proposed forensic and anti-forensic techniques, and identify the optimal actions of both the forger and forensic investigator.

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.

Mobile phones are among the most popular consumer devices, and the recent developments of 3G networks and smart phones enable users to watch video programs by subscribing data plans from service providers. Due to the ubiquity of mobile phones and phone-to-phone communication technologies, data-plan subscribers can redistribute the video content to nonsubscribers. Such a redistribution mechanism is a potential competitor for the mobile service provider and is very difficult to trace given users' high mobility. The service provider has to set a reasonable price for the data plan to prevent such unauthorized redistribution behavior to protect or maximize his/her own profit. In this paper, we analyze the optimal price setting for the service provider by investigating the equilibrium between the subscribers and the secondary buyers in the content-redistribution network. We model the behavior between the subscribers and the secondary buyers as a noncooperative game and find the optimal price and quantity for both groups of users. Based on the behavior of users in the redistribution network, we investigate the evolutionarily stable ratio of mobile users who decide to subscribe to the data plan. Such an analysis can help the service provider preserve his/her profit under the threat of the redistribution networks and can improve the quality of service for end users.

Users in multimedia social networks actively interact with each other. It is crucial to study the complex user dynamics and analyze its impact on the performance of multimedia social networks. This paper uses multimedia fingerprinting as an example and studies user dynamics in colluder social networks. During collusion, a group of attackers collectively attack multimedia fingerprinting system and use multimedia content illegally. This paper analyzes the incentives of cooperation among attackers and investigates how colluders form their coalitions to maximize their payoffs. We present a game-theoretic framework to model the complex dynamics among colluders, analyze when attackers cooperate with each other, and investigate how a colluder selects his/her fellow attackers to maximize his/her own payoff. We analyze multiuser collusion in two scenarios: when all attackers receive fingerprinted copies of the same resolution, and when they have copies of different resolutions. The proposed framework considers both the colluders' risk of being detected by the digital rights enforcer and the reward received from illegal usage of multimedia content. Our analysis shows that in both scenarios, colluding with more attackers does not always increase an attacker's utility, and attackers may not always want to cooperate with each other. We first examine the necessary conditions for attackers to collude together, and study how they select the collusion parameters such that cooperation benefits all colluders. We then study how the number of colluders affects each attacker's utility, and investigate the optimum strategy that an attacker should use to select fellow attackers and to form a coalition in order to maximize his or her own payoff.

Network coding has been widely used in wireless uplink to improve spectral efficiency. However, the relay decoding error may propagate to the intended receiver and thus severely degrade the diversity performance. So in this work, we develop two power scaling schemes at the relay side and two detection schemes at the receiver side, respectively, to mitigate error propagation and thus achieve full diversity for the two-user multiple access relay network. For the soft power scaling based link adaptive relaying, we develop a virtual source-relay-destination channel model and demonstrate that the relay power should be such to balance the signal-to-noise ratios of the source-relay channel and relay-destination channel. As for the hard power scaling based ON-OFF relaying, we first design a decision rule based on total pairwise error probability, and then simplify it to the threshold-based relaying strategy. At the receiver side, we show that the weighted minimum distance detection with the weight being determined by the relative link quality of source-relay channel and relay-destination channel can achieve full diversity once the global channel state information is available, otherwise the maximum likelihood detection should be employed to achieve full diversity when the receiver only knows the local channel state information.

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.

This work focuses on a two-way denoise-and-forward relaying system using non-coherent Differential Binary Phase-Shift Keying (DBPSK) modulation. The relay denoising function and source decoders are designed using Maximum Likelihood (ML) principles. As the ML denoising function is hard to manipulate, it is approximated as a multi-user detector followed by a physical layer network coding encoder, based on which the closed-form relay decoding error is obtained. It is further shown that the ML source decoder is actually equivalent to the typical DBPSK decoder to the relay-source channel, and the exact end-to-end Bit Error Rate (BER) is derived then. A power allocation problem is also formulated to minimize the average BER at high Signal-to-Noise Ratio (SNR). It is shown that the optimal source power is inversely proportional to the square root of the channel gain of the source-relay channel, and the optimal relay power decreases with SNR.

Multi-path effect makes high speed broadband communications a very challenging task due to the severe inter-symbol interference (ISI). By concentrating energy in both spatial and temporal domains, time-reversal (TR) transmission technique provides a great potential of low-complexity energy-efficient communications. In this paper, we develop a new concept of time-reversal division multiple access (TRDMA) as a wireless channel access method based on its high-resolution spatial focusing effect. We propose to use TR structure in a multi-user downlink system over large delay spread channels, where the signals of different users are separated solely by TRDMA. Both the single-transmit-antenna scheme and its enhanced version with multiple-transmit-antenna are developed and evaluated in this paper. We investigate the system performance in terms of the effective SINR and the achievable sum rate of the multi-user system. It is shown in both analytical and simulation results that satisfying performances can be achieved in the proposed TRDMA multi-user downlink system.

Utilizing channel reciprocity, the traditional time-reversal technique boosts the signal-to-noise ratio at the receiver with very low transmitter complexity. However, the large delay spread gives rise to severe inter-symbol interference (ISI) when the data rate is high, and the performance is further degraded in the multiuser downlink due to the inter-user interference (IUI). In this work, we study the weighted sum rate optimization problem in the time-reversal multiuser downlink using waveform design. By exploiting the relation between the allocated power and the SINR targets, an iterative algorithm is proposed to optimize the waveform design through a new power allocation algorithm. Simulation results are shown to demonstrate the superior performance of the proposed algorithm in comparison with other traditional methods.

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.

The recent developments of smart mobile phones and 3G networks enable users to enjoy video programs by subscribing to data plans. Due to phone-to-phone communication technologies and ubiquity of mobile phones, data-plan subscribers are able to redistribute the video content to non-subscribers. Such a redistribution mechanism is a potential competitor for the mobile service provider and is very difficult to trace given users' high mobility. The service provider has to set a reasonable price for the data plan to prevent such unauthorized re-distribution behavior to protect or maximize his/her own profit. In this paper, we investigate the evolutionarily stable ratio of mobile users who decide to subscribe to the data plan. Such an analysis can help the service provider preserve his/her profit under the threat of the redistribution networks and can improve the quality of service for end users.

Due to the ease with which digital information can be altered, many digital forensic techniques have recently been developed to authenticate multimedia content. One important digital forensic result is that adding or deleting frames from an MPEG video sequence introduces a temporally distributed fingerprint into the video can be used to identify frame deletion or addition. By contrast, very little research exists into anti-forensic operations designed to make digital forgeries undetectable by forensic techniques. In this paper, we propose an anti-forensic technique capable of removing the temporal fingerprint from MPEG videos that have undergone frame addition or deletion. We demonstrate that our proposed anti-forensic technique can effectively remove this fingerprint through a series of experiments.

Due to the broadcast nature, wireless transmissions can be overheard by any receiver within the transmission range. A physical layer approach for secure wireless cooperative communications is proposed in this paper. Considering the asynchronous nature of cooperative communications, this paper proposes an eavesdropping-resistant space-time network coding (STNC) scheme for multinode cooperative communications to prevent eavesdropping and overcome the problem of imperfect synchronization. In the proposed scheme, training symbols are transmitted by the destination D instead of the user nodes. Therefore, each user node can obtain the channel state information (CSI) between itself and D, which is unavailable to the eavesdropper E. By exploiting such CSI and the transmit information symbols, weighting coefficient is designed at each user node to prevent E from interception and ensure successful decoding at D. Based on the pairwise error probability (PEP) analysis, STNC is designed to achieve full diversity at D and improve the transmission efficiency of the asynchronous cooperative system. Simulation results are presented to verify the performance of the proposed eavesdropping-resistant STNC scheme.

In this paper, opportunistic multiple access to the under-utilized channel resources is investigated. Exploiting source burstiness, secondary cognitive nodes utilizes primary nodes' periods of silence to access the channel and transmit their packets. Cognitive relays could also make use of these silence periods to offer spatial diversity without incurring bandwidth efficiency losses. First, we consider the cognitive cooperation protocol and propose two different relay assignment schemes. Comparison between the proposed schemes is carried out through a maximum stable throughput analysis of the network. Then, secondary nodes access to the remaining idle channel resources is investigated. Queueing theoretical analysis and numerical results reveal that despite the fact that relays occupy part of the idle resources to provide cooperation, secondary nodes surprisingly achieve higher throughput in the presence of relays. The rationale is that relays help primary nodes empty their queues at faster rates, therefore, secondary nodes observe increased access opportunities to the channel.

Green wireless communications have received considerable attention recently in hope of finding novel solutions to improve energy efficiency for the ubiquity of wireless applications. In this paper, we argue and show that the time-reversal (TR) signal transmission is an ideal paradigm for green wireless communications because of its inherent nature to fully harvest energy from the surrounding environment by exploiting the multi-path propagation to re-collect all the signal energy that would have otherwise been lost in most existing communication paradigms. A green wireless technology must ensure low energy consumption and low radio pollution to others than the intended user. In this paper, we show through theoretical analysis, numerical simulations and experiment measurements that the TR wireless communications, compared to the conventional direct transmission using a Rake receiver, reveals significant transmission power reduction, achieves high interference alleviation ratio, and exhibits large multi-path diversity gain. As such it is an ideal paradigm for the development of green wireless systems. The theoretical analysis and numerical simulations show an order of magnitude improvement in terms of transmit power reduction and interference alleviation. Experimental measurements in a typical indoor environment also demonstrate that the transmit power with TR based transmission can be as low as 20% of that without TR, and the average radio interference (thus radio pollution) even in a nearby area can be up to 6dB lower. A strong time correlation is found to be maintained in the multi-path channel even when the environment is varying, which indicates high bandwidth efficiency can be achieved in TR radio communications.

Various spectrum management schemes have been proposed in recent years to improve the spectrum utilization in cognitive radio networks. However, few of them have considered the existence of cognitive attackers who can adapt their attacking strategy to the time-varying spectrum environment and the secondary users' strategy. In this paper, we investigate the security mechanism when secondary users are facing the jamming attack, and propose a stochastic game framework for anti-jamming defense. At each stage of the game, secondary users observe the spectrum availability, the channel quality, and the attackers' strategy from the status of jammed channels. According to this observation, they will decide how many channels they should reserve for transmitting control and data messages and how to switch between the different channels. Using the minimax-Q learning, secondary users can gradually learn the optimal policy, which maximizes the expected sum of discounted payoffs defined as the spectrum-efficient throughput. The proposed stationary policy in the anti-jamming game is shown to achieve much better performance than the policy obtained from myopic learning, which only maximizes each stage's payoff, and a random defense strategy, since it successfully accommodates the environment dynamics and the strategic behavior of the cognitive attackers.

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.

Due to the asynchronous nature of cooperative communications, simultaneous transmissions from two or more nodes are challenging in practice. The existing cooperative communications employing successive transmission from one user node to the other can avoid the synchronization problem but results in large transmission delay. In addition, channel estimation in multi-source cooperative communications is a challenging and costly task due to the amount of training, especially when the number of cooperative users is large. Considering these practical challenges in multi-source cooperative communications, this paper proposes a differential space-time network coding (DSTNC) scheme for narrowband multi-source cooperative communications to overcome the problems of imperfect synchronization and complex channel estimation without introducing large transmission delay. Each user in the network linearly combines the correctly decoded symbols via network coding and transmits its packet in time division multiple access (TDMA) mode. The pairwise error probability is analyzed and the design criteria of the DSTNC are derived to achieve full diversity. For broadband cooperative communications, distributed differential space-time-frequency network coding (DSTFNC), which is differentially encoded within each orthogonal frequency-division multiplexing (OFDM) block, is designed through mapping from the proposed DSTNC. When the statistical channel power-delay profile is known at the corresponding user node, each node can permutate its channel independently to improve the performance of the DSTFNC scheme. Simulation results are presented to verify the performance of the proposed schemes.

We present a physical-layer fingerprint-embedding scheme for orthogonal frequency-division-multiplexing (OFDM) transmissions, where the fingerprint signal conveys a low capacity communication suitable for authenticating the transmission and further facilitating secure communications. Our system strives to embed the fingerprint message into the noise subspace of the channel estimates obtained by the receiver, using a number of signal spreading techniques. When side information of the channel state is known and leveraged by the transmitter, the performance of the fingerprint embedding can be improved. When channel state information is not known, blind spreading techniques are applied. The fingerprint message is only visible to aware receivers who explicitly preform detection of the signal, but is invisible to receivers employing typical channel equalization. A taxonomy of overlay designs is discussed and these designs are explored through experiment using time-varying channel-state information (CSI) recorded from IEEE802.16e Mobile WiMax base stations. The performance of the fingerprint signal as received by a WiMax subscriber is demonstrated using CSI measurements derived from the downlink signal. Detection performance for the digital fingerprint message in time-varying channel conditions is also presented via simulation.

As society has become increasingly reliant upon digital images to communicate visual information, a number of forensic techniques have been developed to verify the authenticity of digital images. Amongst the most successful of these are techniques that make use of an image's compression history and its associated compression fingerprints. Little consideration has been given, however, to anti-forensic techniques capable of fooling forensic algorithms. In this paper, we present a set of anti-forensic techniques designed to remove forensically significant indicators of compression from an image. We do this by first developing a generalized framework for the design of anti-forensic techniques to remove compression fingerprints from an image's transform coefficients. This framework operates by estimating the distribution of an image's transform coefficients before compression, then adding anti-forensic dither to the transform coefficients of a compressed image so that their distribution matches the estimated one. We then use this framework to develop anti-forensic techniques specifically targeted at erasing compression fingerprints left by both JPEG and wavelet-based coders. Additionally, we propose a technique to remove statistical traces of the blocking artifacts left by image compression algorithms that divide an image into segments during processing. Through a series of experiments, we demonstrate that our anti-forensic techniques are capable of removing forensically detectable traces of image compression without significantly impacting an image's visual quality. Furthermore, we show how these techniques can be used to render several forms of image tampering such as double JPEG compression, cut-and-paste image forgery, and image origin falsification undetectable through compression-history-based forensic means.

Although cognitive radio technology improves efficiency of spectrum utilization, primary users usually do not gain from opening up the spectrum in the opportunistic spectrum access, and sometimes even suffer from collisions due to secondary users' imperfect spectrum sensing. However, in this paper, we show that if information secrecy is a concern, primary users could actually be better off by allowing secondary users to cooperatively share the spectrum. Specifically, we propose a new cooperation paradigm in cognitive radio networks that primary users improve secrecy with the help of trustworthy secondary users, in the presence of an intelligent and passive eavesdropper attempting to decode primary users' messages. After deriving the achievable pair of primary users' secrecy rate and secondary users' transmission rate under various circumstances, we model the interaction between primary users and secondary users as a Stackelberg game in which transmission power levels are the key to maximize data rates. Moreover, based on a 2-D representation of how achievable rates depend on power-level regions, we apply equilibrium analysis to understand the optimal strategy of primary and secondary users. Finally, simulation results are presented to verify the performance.

Multimedia social network is a network infrastructure in which the social network users share multimedia contents with all different purposes. Analyzing user behavior in multimedia social networks helps design more secured and efficient multimedia and networking systems. Multimedia fingerprinting protects multimedia from illegal alterations and multiuser collusion is a cost-effective attack. The colluder social network is naturally formed during multiuser collusion with which colluders gain reward by redistributing the colluded multimedia contents. Since the colluders have conflicting interest, the maximal-payoff collusion for one colluder may not be the maximal-payoff collusion for others. Hence, before a collusion being successful, the colluders must bargain with each other to reach agreements. We first model the bargaining behavior among colluders as a noncooperative game and study four different bargaining solutions of this game. Moreover, the market value of the redistributed multimedia content is often time-sensitive. The earlier the colluded copy being released, the more the people are willing to pay for it. Thus, the colluders have to reach agreements on how to distribute reward and risk among themselves as soon as possible. This paper further incorporates this time-sensitiveness of the colluders' reward and studies the time-sensitive bargaining equilibrium. The study in this paper reveals the strategies that are optimal for the colluders; thus, all the colluders have no inventive to disagree. Such understanding reduces the possible types of collusion into a small finite set.

Spectrum sensing is an essential functionality of cognitive radio networks. However, the effect of errors in the spectrum sensing process on the performance of the multiple access layer of both primary and secondary networks has not gained much attention. This paper aims at bridging the gap between the study of spectrum sensing and the multiple access of cognitive radio networks. To achieve this goal we pose and answer the question how the spectrum sensing errors affects the performance of cognitive radio networks from a multiple access protocol design point of view. The negative effects of the spectrum sensing errors on the throughput of both primary and secondary networks are characterized through queuing theory analysis of both networks. To alleviate these negative effects a novel joint design of the spectrum sensing and channel access mechanisms is proposed. This design is based on the observation that, in a binary hypothesis testing problem, the value of the test statistics could be used as a confidence measure for the test outcome. Therefore, this value will be used to define different channel access probabilities for secondary users. Results reveal a significant performance improvement in the maximum stable throughput of both primary and secondary networks by virtue of the proposed technique.

Due to the broadcast nature of wireless medium, wireless transmissions can be overheard by any undesired receivers with eavesdropping capability within source transmission range. A novel physical layer approach for secure wireless cooperative communications against eavesdropping is proposed in this paper. For an asynchronous cooperative communication network with a cluster of user nodes transmitting to a common destination, we propose an anti-eavesdropping space-time network coding (AE-STNC) scheme to prevent eavesdropping and overcome the problem of imperfect synchronization. In the proposed scheme, training symbols are first transmitted by the destination (D). Owing to channel reciprocity, each user node can obtain the channel state information (CSI) between itself and D, which is unavailable to the eavesdroppers. By exploiting such CSI, anti-eavesdropping encoding is designed for each user node to create high decoding error rate at the eavesdroppers and ensure successful decoding at D. Furthermore, the AE-STNC is designed to achieve full diversity at D. Power allocation subject to average power constraint is considered and the secure region against eavesdroppers is also investigated. Based on the proposed AE-STNC scheme, an anti-eavesdropping space-time-frequency coding (AE-STFNC) scheme is proposed for broadband asynchronous cooperative communications. Simulations are provided to verify the performance and security of the proposed transmission schemes.

Traditional cooperative communications consider dedicated-relays, while often such relays may not be available. In this paper, we consider wireless transceivers that relay signals in addition to their own primary communication mission. We consider a best-effort delivery policy, where a node is not obligated to devote energy to cooperatively relay signals, nor does it provide a guarantee of signal quality on retransmissions. Instead the relay sacrifices energy at its own discretion, with priority given to the primary communication mission. We consider one best-effort delivery problem: a system that transmits an additional relay signal within its original transmission energy budget while inducing minimal degradation to the primary-user's signal. To maintain this constraint, we consider the feasibility of reallocating energy from pilot signals used for channel estimation toward the relaying service, when channel conditions are stationary. We demonstrate that transmitter energy may be dynamically allocated between a relay component and a pilot component of the transmission using best-effort delivery. This power allocation is critical to system performance, since both the primary-user and the secondary-user may require pilot energy to correctly decode transmitted signals. Sub-optimal power allocation rules with respect to primary-user channel estimate mean-square error and pairwise error probability are derived.

A framework for introducing an extrinsic fingerprint signal to space-time coded transmissions at the physical layer is presented, where the fingerprint signal conveys a low capacity cryptographically secure authentication message of arbitrary length. The multi-bit digital fingerprint message conveyed by the fingerprint signal is available to all users within reception range and is used to authenticate the fingerprinted transmission. A novel approach is discussed where the fingerprint signaling mechanism mimics distortions similar to time-varying channel effects. Specifically, the fingerprint is detectable to receivers considering previous channel state information, but will be ignored by receivers equalizing according to current channel state information. Two example fingerprint signaling mechanisms and detection rules are presented based on pulse-amplitude keying and phase-shift keying approaches. The methods for obtaining the real (intrinsic) channel estimate, the extrinsic fingerprint message, and the primary transmission are analytically demonstrated using general pilot embedding schemes. The worst-case distortions caused by non-ideal equalization of a fingerprinted message are derived using the 2×2 Alamouti code. Simulation results including bit error rate (BER) and model mismatch error using a maximum-likelihood (ML) receiver are presented for both the primary and fingerprint signal, while authentication signal BERs lower than the primary signal are demonstrated.

This work focuses on a two-way denoise-and-forward relaying system using non-coherent Differential Binary Phase-Shift Keying (DBPSK) modulation, which has the well-defined relay denoising function when channel state information is unknown. We first design the relay denoising function and source decoders using Maximum Likelihood (ML) principles for the general case with K parallel relays. As the ML denoising function is hard to manipulate, we approximate it as a multi-user detector followed by a physical layer network coding encoder and obtain the closed-form relay decoding error. For the single-relay case, we show that the ML source decoder is actually equivalent to the typical DBPSK decoder for the relay-source channel and thus derive the exact end-to-end Bit Error Rate (BER). To minimize the average BER, we also investigate the power allocation problem by use of asymptotic analysis at high Signal-to-Noise Ratio (SNR). We show that the optimal source power is inversely proportional to the square root of the channel gain of the source-relay channel, and the optimal relay power decreases with SNR. For the multi-relay case, though the exact analysis is intractable, we develop upper bound and lower bound on BER and show that the diversity order is exactly ⌈κ/2⌉.

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.

In multinode cooperative communications, simultaneous transmissions from two or more nodes are challenging due to its asynchronous nature. In addition, channel estimation is a costly task due to the amount of training, especially when the number of cooperative users is large. Considering all these practical challenges in multinode cooperative communications, this paper propose a new transmission scheme, namely differential space-time network coding (DSTNC), to overcome the problems of imperfect synchronization and complicated channel estimation. Each user in the network linearly combines the correctly decoded symbols by a network coding vector, which is designed to achieve full diversity without introducing large time delay. The pairwise error probability (PEP) is analyzed and the design criteria of the DSTNC are derived based on the PEP. The proposed DSTNC scheme can be applied to any number of cooperative users. Simulation results are shown to verify the performance of the proposed transmission scheme.

A framework for introducing an extrinsic fingerprint signal to space-time coded transmissions at the physical-layer is presented, whereby the fingerprint signal conveys a low capacity digital communication suitable for authenticating the transmission and further facilitating secure communications. A novel approach is discussed where the fingerprint signal mimics distortions similar to time-varying channel effects. Specifically, the fingerprint signal is only visible to aware receivers considering previous channel state information (CSI) and is otherwise invisible to a receiver equalizing according to current CSI. An augmented signal is created consisting of the original transmission, or primary message, and the fingerprint message. An example fingerprint signal and detection rule are presented based on a phase-shift keying approach. Simulation results including bit error rate (BER) are presented for both the primary and fingerprint signals using the 2×2 Alamouti code, and authentication signal BERs lower than the primary signal are demonstrated.

Cooperative communications can be used to improve communication reliability. However, the transmissions from different relaying nodes result in a great challenge in practice. The use of TDMA for relaying transmissions causes large transmission delay. In contrast, FDMA and CDMA associate with the issue of imperfect frequency and timing synchronization due to the simultaneous transmissions from the relaying nodes. In this work, we propose a space-time network coding (STNC) scheme utilizing transform-based coding to improve communication reliability while maintaining a stable network throughput and overcoming the issue of imperfect frequency and timing synchronization. Based on TDMA, a node in the network forms a linearly-coded signal from the overheard symbols and transmits it to the destination in its dedicated time slot. The pairwise error probability (PEP) is analyzed and design criteria of the STNC are derived to ensure achieving full diversity. Simulations are conducted to verify the performance of the proposed scheme and to reveal its advantage over a distributed space-time block coding scheme under timing synchronization errors.

Mobile phones are among the most popular consumer devices and the recent developments of 3G networks and smart phones enable users to watch video programs by subscribing data plans from service providers. Due to the ubiquity of mobile phones and phone-to-phone communication technologies, subscribers can redistribute the video content to non-subscribers. Such a redistribution mechanism is a potential competitor for the service provider and is very difficult to trace given the users'' high mobility. The service provider has to set a reasonable price for the data plan to prevent such re-distribution behavior to protect his/her own profit. In this paper, we analyze the optimal price setting for the service provider by investigating the equilibrium between the subscribers and the secondary buyers in the content-redistribution network. We model the behavior between the subscribers and the secondary buyers as a hybrid Stackelburg auction game and find the optimal price and quantity for both groups of users. Such an analysis can help the service provider preserve their profit under the threat of the redistribution networks and can improve the quality of service for end users.

Users using the same video streaming service within a wireless network share the same limited backbone bandwidth to the Internet. These users are motivated to collaborate with each other to obtain better-quality service. The decisions and actions of users influence the performance of others, hence they form a social network. In a fully-distributed wireless network, malicious attack can cause much more damage than over the Internet since the unstable wireless channel allows hostile users to mimic as a non-malicious user. Therefore, a robust attack-resistant cooperation strategy for non-malicious user is needed to combat the challenges in wireless networks. In this paper, we incorporate trust modelling into the cooperation among users to increase attack-resistance. Simulation results show our robust cooperation strategies can against 40% more attackers.

Cognitive radio technology has become a promising approach to increase the efficiency of spectrum utilization. Since cognitive radio users are vulnerable to malicious attacks, security countermeasures are crucial to the successful deployment of cognitive radio networks in the future. In this paper, we focus on defending against the jamming attack, one of the major threats to cognitive radio networks, where several malicious attackers intend to jam the secondary user's communication link by injecting interference. We model this scenario into a jamming game, and derive the optimal strategy through the Markov decision process approach. Furthermore, a learning scheme is proposed for the secondary user to observe the wireless environment and estimate parameters such as primary users' access pattern and the number of attackers. Finally, simulation results are presented to verify the performance.

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.

Traditional decode-and-forward cooperation systems consider dedicated relays, while instead we consider wireless transceivers that cooperatively relay signals in addition to primary communication missions. A system that transmits the additional signal using a best-effort transmission policy within the original transmission energy constraint is considered. To maintain the original energy budget we consider the feasibility of reallocating energy from pilot signals toward the relaying service when channel conditions are stationary. Under the best-effort delivery policy, the node is not obligated to devote energy for relaying signals, nor does it provide a guarantee of signal quality to retransmissions. Instead the relay sacrifices energy at its own discretion, prioritizing the primary communication mission. Using the best-effort delivery policy, we derive an optimal power allocation rule that maintains a fixed symbol error rate for the relay's primary transmission, and further demonstrate cooperative communication gains using the proposed delivery method.

Traditional cooperative communications can improve communication reliability. However, the simultaneous transmissions from multiple relays are challenging in practice due to the issue of imperfect frequency and timing synchronization. In this work, we propose space-time network codes (STNCs) that overcome the issue of imperfect synchronization, reduce the large transmission delay, and still provide full spatial diversity in multipoint-to-point (M2P) and point-to-multipoint (P2M) transmissions. Relays form single linearly-coded signals from the overheard symbols and transmit them to the destination, where multiuser detection is utilized to detect the desired symbol. For a network of N client nodes, M2P and P2M STNCs result in a diversity order of N with only 2N time slots, a significant reduction from N2time slots in traditional cooperative communications using TDMA.

Due to the ease with which convincing digital image forgeries can be created, a need has arisen for digital forensic techniques capable of detecting image manipulation. Once image alterations have been identified, the next logical forensic task is to recover as much information as possible about the unaltered version of image and the operation used to modify it. Previous work has dealt with the forensic detection of contrast enhancement in digital images. In this paper we propose an iterative algorithm to jointly estimate any arbitrary contrast enhancement mapping used to modify an image as well as the pixel value histogram of the image before contrast enhancement. To do this, we use a probabilistic model of an image's pixel value histogram to determine which histogram entries are most likely to correspond to contrast enhancement artifacts. Experimental results are presented to demonstrate the effectiveness of our proposed method.

The widespread availability of photo editing software has made it easy to create visually convincing digital image forgeries. To address this problem, there has been much recent work in the field of digital image forensics. There has been little work, however, in the field of anti-forensics, which seeks to develop a set of techniques designed to fool current forensic methodologies. In this work, we present a technique for disguising an image's JPEG compression history. An image's JPEG compression history can be used to provide evidence of image manipulation, supply information about the camera used to generate an image, and identify forged regions within an image. We show how the proper addition of noise to an image's discrete cosine transform coefficients can sufficiently remove quantization artifacts which act as indicators of JPEG compression while introducing an acceptable level of distortion. Simulation results are provided to verify the efficacy of this anti-forensic technique.

Nonnegative matrix factorization (NMF) is a widely-used tool for obtaining low-rank approximations of nonnegative data such as digital images, audio signals, textual data, financial data, and more. One disadvantage of the basic NMF formulation is its inability to control the amount of dependence among the learned dictionary atoms. Enforcing dependence within predetermined groups of atoms allows objects to be represented using multiple atoms instead of only one atom. In this paper, we introduce three simple and convenient multiplicative update rules for NMF that enforce dependence among atoms. Using examples in music transcription, we demonstrate the ability of these updates to represent each musical note with multiple atoms and cluster the atoms for source separation purposes.

Dictionary learning through matrix factorization has become widely popular for performing music transcription and source separation. These methods learn a concise set of dictionary atoms which represent spectrograms of musical objects. However, there is no guarantee that the atoms learned will be perceptually meaningful, particularly when there exists significant spectral and temporal overlap among the musical sources. In this paper, we propose a novel dictionary learning method that imposes additional harmonic constraints upon the atoms of the learned dictionary while allowing the dictionary size to grow appropriately during the learning procedure. When there is significant spectral-temporal overlap among the musical sources, our method outperforms popular existing matrix factorization methods as measured by the recall and precision of learned dictionary atoms.

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.

Because digital images can be modified with relative ease, considerable effort has been spent developing image forensic algorithms capable of tracing an image's processing history. In contrast to this, relatively little consideration has been given to anti-forensic operations designed to mislead forensic techniques. In this paper, we propose an anti-forensic technique capable of removing artifacts indicative of wavelet-based image compression from an image. Our technique operates by adding anti-forensic dither to a previously compressed image's wavelet coefficients so that the anti-forensically modified wavelet coefficient distribution matches a model of the coefficient distribution before compression. Simulation results show that our algorithm is capable of fooling current forensic image compression detection algorithms 100% of the time.

Recently, a number of digital image forensic techniques have been developed which are capable of identifying an image's origin, tracing its processing history, and detecting image forgeries. Though these techniques are capable of identifying standard image manipulations, they do not address the possibility that anti-forensic operations may be designed and used to hide evidence of image tampering. In this paper, we propose an anti-forensic operation capable of removing blocking artifacts from a previously JPEG compressed image. Furthermore, we show that by using this operation along with another anti-forensic operation which we recently proposed, we are able to fool forensic methods designed to detect evidence of JPEG compression in decoded images, determine an image's origin, detect double JPEG compression, and identify cut-and-paste image forgeries.

In this paper, the mutual cooperative communication between multiple nodes in a wireless network is efficiently achieved through a novel concept of Space-Time Network Coding (STNC). Unlike the conventional point-to-point cooperative communications between two nodes with N relay nodes deployed in between, simultaneous transmissions from the different N nodes acting as source/relay nodes are performed within 2N time-slots. In particular, the communication is split into two phases: 1) Broadcasting Phase and 2) Cooperation Phase. In the Broadcasting Phase, each node broadcasts its data symbol to the other nodes in the network in its own time-slot, alternatively; while in the Cooperation Phase, in each time-slot, a set of (N-1) nodes transmit while a single destination node receives the other nodes' transmissions. Specifically, each node employing a selective Decode-and-Forward (DF) cooperative protocol, performs a linear combination of the other nodes data symbols and all the (N-1) nodes simultaneously transmit their signals to a single receiving node; which then performs joint multiuser detection to separate the different nodes' symbols. Exact symbol-error-rate (SER) expressions for arbitrary order M-ary Phase Shift Keying (M-PSK) modulation are derived. In addition, an asymptotic SER approximation is also provided which is shown to be tight at high signal-to-noise ratio (SNR). Finally, the analytical results confirm that for a network of N nodes, a full diversity order of (N-1) per node is achieved by the proposed STNC cooperative communication scheme.

This paper aims at answering the questions of how to enable cooperative communications in random access networks. And, since cooperation introduces extra transmissions in the channel, what are the benefits and possible tradeoffs associated with cooperation? To achieve this goal, a novel cooperative protocol for wireless networks based on IEEE 802.11 random access protocol is proposed. The proposed protocol guarantees an uncontested channel access to the relay. Interaction between different network nodes is characterized and performance analysis is conducted through Markov chain models in conjunction with queueing theoretical analysis. Results demonstrate significant gains in terms of throughput and delay performance compared with the non cooperative CSMA/CA protocol.

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.

Cooperative spectrum sensing has been shown to be able to greatly improve the sensing performance in cognitive radio networks. However, if cognitive users belong to different service providers, they tend to contribute less in sensing in order to increase their own throughput. In this paper, we propose an evolutionary game framework to answer the question of "how to collaborate" in multiuser de-centralized cooperative spectrum sensing, because evolutionary game theory provides an excellent means to address the strategic uncertainty that a user/player may face by exploring different actions, adaptively learning during the strategic interactions, and approaching the best response strategy under changing conditions and environments using replicator dynamics. We derive the behavior dynamics and the evolutionarily stable strategy (ESS) of the secondary users. We then prove that the dynamics converge to the ESS, which renders the possibility of a de-centralized implementation of the proposed sensing game. According to the dynamics, we further develop a distributed learning algorithm so that the secondary users approach the ESS solely based on their own payoff observations. Simulation results show that the average throughput achieved in the proposed cooperative sensing game is higher than the case where secondary users sense the primary user individually without cooperation. The proposed game is demonstrated to converge to the ESS, and achieve a higher system throughput than the fully cooperative scenario, where all users contribute to sensing in every time slot.

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).

As the use of digital images has increased, so has the means and the incentive to create digital image forgeries. Accordingly, there is a great need for digital image forensic techniques capable of detecting image alterations and forged images. A number of image processing operations, such as histogram equalization or gamma correction, are equivalent to pixel value mappings. In this paper, we show that pixel value mappings leave behind statistical traces, which we shall refer to as a mapping's intrinsic fingerprint, in an image's pixel value histogram. We then propose forensic methods for detecting general forms globally and locally applied contrast enhancement as well as a method for identifying the use of histogram equalization by searching for the identifying features of each operation's intrinsic fingerprint. Additionally, we propose a method to detect the global addition of noise to a previously JPEG-compressed image by observing that the intrinsic fingerprint of a specific mapping will be altered if it is applied to an image's pixel values after the addition of noise. Through a number of simulations, we test the efficacy of each proposed forensic technique. Our simulation results show that aside from exceptional cases, all of our detection methods are able to correctly detect the use of their designated image processing operation with a probability of 99% given a false alarm probability of 7% or less.

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.

Human behavior analysis in video sharing social networks is an emerging research area, which analyzes the behavior of users who share multimedia content and investigates the impact of human dynamics on video sharing systems. Users watching live streaming in the same wireless network share the same limited bandwidth of backbone connection to the Internet, thus, they might want to cooperate with each other to obtain better video quality. These users form a wireless live-streaming social network. Every user wishes to watch video with high quality while paying as little as possible cost to help others. This paper focuses on providing incentives for user cooperation. We propose a game-theoretic framework to model user behavior and to analyze the optimal strategies for user cooperation simulation in wireless live streaming. We first analyze the Pareto optimality and the time-sensitive bargaining equilibrium of the two-person game. We then extend the solution to the multiuser scenario. We also consider potential selfish users' cheating behavior and malicious users' attacking behavior and analyze the performance of the proposed strategies with the existence of cheating users and malicious attackers. Both our analytical and simulation results show that the proposed strategies can effectively stimulate user cooperation, achieve cheat free and attack resistance, and help provide reliable services for wireless live streaming applications.

In autonomous mobile ad hoc networks (MANETs) where each user is its own authority, the issue of cooperation enforcement must be solved first to enable networking functionalities such as packet forwarding, which becomes very difficult under noisy and imperfect monitoring. In this paper, we consider cooperation enforcement in autonomous mobile ad hoc networks under noisy and imperfect observation and study the basic packet forwarding among users through the repeated game models with imperfect information. A belief evaluation framework is proposed to obtain cooperation-enforcement packet forwarding strategies only based on each node's private information including its own past actions and imperfect observation of other nodes' information. More importantly, we not only show that the proposed strategy with belief system can maintain the cooperation paradigm but also establish its performance bounds. The simulation results illustrate that the proposed belief evaluation framework can enforce the cooperation with only a small performance degradation compared with the unconditionally cooperative outcomes when noisy and imperfect observation exist.

One important application of cooperative communications is to extend coverage area in wireless networks without increasing infrastructure. However, a crucial challenge in implementing cooperation protocols is how to select relay-source pairs. In this paper, we address this problem based on the knowledge of the users' spatial distribution which determines the channel statistics. We consider two scenarios at the destination node, when the receiver uses MRC and when no-MRC is used. First, we characterize the optimal relay location to minimize the outage probability. Then, we propose and analyze the performance of two schemes: a distributed nearest neighbor relay assignment in which users can act as relays, and an infrastructure-based relay-assignment protocol in which fixed relay nodes are deployed in the network to help the users forward their data. The outage probabilities of these two schemes are derived. We also derive universal lower bounds on the performance of relay-assignment protocols to serve as a benchmark for our proposed protocols. Numerical results reveal significant gains when applying the proposed simple distributed algorithms over direct transmission in terms of coverage area, transmit power, and spectral efficiency. At 1 percent outage probability, more than 200 percent increase in coverage area can be achieved, 7 dBW savings in the transmitted power, and the system can operate at 2 b/s/Hz higher spectral efficiency.

A key challenge in the design of real-time wireless multimedia systems is the presence of fading coupled with strict delay constraints. A very effective answer to this problem is the use of diversity achieving techniques to overcome the fading nature of the wireless channels caused by the mobility of the nodes. The mobility of the nodes gives rise to the need of cooperation among the nodes to enhance the system performance. This paper focuses on comparing systems that exhibit diversity of three forms: source coding diversity, channel coding diversity, and user cooperation diversity implemented through multihop or relay channels with amplify-and-forward or decode-and-forward protocols. Commonly used in multimedia communications, performance is measured in terms of the distortion exponent, which measures the rate of decay of the end-to-end distortion at asymptotically high signal-to-noise ratio (SNR). For the case of repetition coding at the relay nodes, we prove that having more relays is not always beneficial. For the general case of having a large number of relays that can help the source using repetition coding, the optimum number of relay nodes that maximizes the distortion exponent is determined in this paper. This optimum number of relay nodes will depend on the system bandwidth as well as the channel quality. The derived result shows a trade-off between the quality (resolution) of the source encoder and the amount of cooperation (number of relay nodes). Also, the performances of the channel coding diversity-based scheme and the source coding diversity-based scheme are compared. The results show that for both relay and multihop channels, channel coding diversity provides the best performance, followed by the source coding diversity.

Cognitive radio technologies have become a promising approach to increase the efficiency of spectrum utilization. Although cognitive radio has been intensively studied in recent years, only a few works have discussed security aspects. In this paper, we focus on the jamming attack, one of major threats to cognitive radio networks, where a malicious user wants to jam the communications of secondary users by injecting interference. Aware of the absence of several primary users and the presence of a malicious user, a secondary user can allocate power to those fallow bands with a randomized strategy, in hope of alleviating the damage caused by the malicious user. We model this scenario into a two-player zero-sum game, and derive its unique Nash Equilibrium under certain conditions using the Colonel Blotto game approach, which provides a minimax strategy that the secondary user should adopt in order to minimize the worst-case damage caused by the malicious user. Simulation results are presented to verify the performance.

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.

Multimedia social network is a network infrastructure in which the social network members share multimedia contents with all different purposes. Analyzing user behavior in multimedia social networks help design more secured and efficient multimedia and networking systems. In this paper, we focus on the colluder social network in multimedia fingerprinting systems in which colluders gain reward by redistributing the colluded multimedia signal. However, the market value of the redistributed multimedia content is time sensitive. The earlier the colluded copy being released, the more the people who are willing to pay for it. Thus the colluders have to reach agreement on how to distribute reward and the probability being detected among themselves as soon as possible. This paper incorporates the time-sensitiveness of the colluders' reward, and models the dynamics among colluders as a noncooperative game, and studies the time-restricted bargaining equilibrium. We provide the solution to the equilibrium that all the colluders have no inventive to disagree in order to maximize their own payoff.

Multimedia social network analysis is an emerging research area, which analyzes the behavior of users who share multimedia content and investigates the impact of human dynamics on multimedia systems. Users watching live streaming in the same wireless network share the same backbone connection to the Internet, thus they might want to cooperate with each other to obtain better video quality. These users form a wireless live-streaming social network and every user wishes to watch video with as high as possible quality while paying as less as cost for cooperation. Thus full cooperation cannot be guaranteed and the cooperation strategy must give incentives to the users. This paper proposes a game-theoretic framework to model user behavior and designs incentive-based strategies to stimulate user cooperation in wireless live streaming. We analyze the Pareto optimality and time-restricted bargaining equilibrium of the game. We also take into consideration selfish users' cheating behavior and propose cheat-proof strategies. Both our analytical and simulation results show that the proposed strategies can effectively stimulate user cooperation, achieve cheat free and help provide reliable services.

In this paper, a binary hypothesis distributed detection problem in correlated wireless sensor networks with cooperative relays deployment is considered. In particular, the effect of correlation between sensor nodes is modeled and analyzed in Rayleigh flat fading channels in order to explore the natural tradeoffs between the number of sensor/relay nodes and the detection error performance in the network. Specifically, two communication protocols are utilized; in protocol I, each sensor node communicates its observation directly to the fusion center while in protocol II, amplify-and-forward (AF) cooperative relays are deployed and a fewer number of sensors is used. Based on the theoretical analysis and simulations, it is revealed that employing less sensor nodes and instead deploying relay nodes results in significant performance gains under strict network power constraint It is concluded that with cooperative distributed detection and exploitation of spatial diversity, better detection error performance is achieved as well as reduction in the required number of sensor nodes.

Channel estimation error problem is among the main causes of performance degradation in wireless networks. In this paper, we investigate the impact of cooperative communications on mitigating the effect of channel estimation error. Two main performance criteria, namely, the traditional outage probability and the proposed signal-to-noise ratio (SNR) gap ratio, are utilized to characterize such impact. The SNR gap ratio measures the reduction in the SNR due to channel estimation error. Taking into consideration the channel estimation error, we show that the outage probability is reduced by utilizing cooperative transmission. We also show that cooperative transmission results in lower SNR gap ratio compared to that of the direct transmission. Thus, cooperative transmission is less susceptible to the effect of channel estimation error compared to direct transmission. Finally, we illustrate that increasing the number of cooperating relays reduces the effect of the channel estimation error more.

Dynamic spectrum access (DSA), enabled by cognitive radio technologies, has become a promising approach to improve efficiency in spectrum utilization, and the spectrum auction is one important DSA approach, in which secondary users lease some unused bands from primary users. However, spectrum auctions are different from existing auctions studied by economists, because spectrum resources are interference-limited rather than quantity-limited, and it is possible to award one band to multiple secondary users with negligible mutual interference. To accommodate this special feature in wireless communications, in this paper, we present a novel multi-winner spectrum auction game not existing in auction literature. As secondary users may be selfish in nature and tend to be dishonest in pursuit of higher profits, we develop effective mechanisms to suppress their dishonest/collusive behaviors when secondary users distort their valuations about spectrum resources and interference relationships. Moreover, in order to make the proposed game scalable when the size of problem grows, the semi-definite programming (SDP) relaxation is applied to reduce the complexity significantly. Finally, simulation results are presented to evaluate the proposed auction mechanisms, and demonstrate the complexity reduction as well.

Recent development in multimedia processing and network technologies has facilitated the distribution and sharing of multimedia through networks, and increased the security demands of multimedia contents. Traditional image content protection schemes use extrinsic approaches, such as watermarking or fingerprinting. However, under many circumstances, extrinsic content protection is not possible. Therefore, there is great interest in developing forensic tools via intrinsic fingerprints to solve these problems. Source coding is a common step of natural image acquisition, so in this paper, we focus on the fundamental research on digital image source coder forensics via intrinsic fingerprints. First, we investigate the unique intrinsic fingerprint of many popular image source encoders, including transform-based coding (both discrete cosine transform and discrete wavelet transform based), subband coding, differential image coding, and also block processing as the traces of evidence. Based on the intrinsic fingerprint of image source encoders, we construct an image source coding forensic detector that identifies which source encoder is applied, what the coding parameters are along with confidence measures of the result. Our simulation results show that the proposed system provides trustworthy performance: for most test cases, the probability of detecting the correct source encoder is over 90%.

In multimedia social networks, there exists complicated dynamics among users who share and exchange multimedia content. Using multimedia fingerprinting as an example, this paper investigates the human behavior dynamics in the multimedia social networks with side information. Side information is the information other than the colluded multimedia content that can help increase the probability of detection. We study the impact of side information in multimedia fingerprinting and show that the statistical means of the detection statistics can help the fingerprint detector significantly improve the collusion resistance. We then investigate how to probe the side information and model the dynamics between the fingerprint detector and the colluders as a two-stage extensive game with perfect information. We model the colluder-detector behavior dynamics as a two-stage game and find the equilibrium of the colluder-detector game using backward induction and show that the min-max solution is a Nash equilibrium, which gives no incentive for everyone in the multimedia fingerprint social network to deviate. This paper demonstrates that the proposed side information can significantly help improve the system performance to almost the same as the optimal correlation-based detector. Such result opens up a new scope in the research of fingerprinting system that given any fingerprint code, leveraging side information can improve the collusion resistance. Also, we provide the solutions to how to reach optimal collusion strategy and the corresponding detection, thus lead to a better protection of the multimedia content.

Orthogonal designs have received considerable attention in the development of efficient modulation and coding methods for future multi-antenna wireless communication systems due to their special properties. In this paper, we propose a class of space-time block codes constructed by combining orthogonal designs with sphere packing for an arbitrary number of transmit antennas. The structure of the orthogonal designs is exploited to guarantee full diversity, and sphere packing is used to improve the coding advantage. Space-time block code construction from block-orthogonal designs is also considered: the full-diversity property is ensured by rotating the sphere packing underlying the code, and the optimal rotation angle is determined for a class of sphere packing. Code design examples are provided for two and four transmit antennas and various transmission rates. The simulation results show that by jointly designing the symbols in the orthogonal designs, the performance of the block codes can be significantly increased.

Dynamic positron emission tomography (PET) imaging technique enables the measurement of neuroreceptor distributions corresponding to anatomic structures, and thus, allows image-wide quantification of physiological and biochemical parameters. Accurate quantification of the concentration of neuroreceptor has been the objective of many research efforts. Compartment modeling is the most widely used approach for receptor binding studies. However, current compartment-model-based methods often either require intrusive collection of accurate arterial blood measurements as the input function, or assume the existence of a reference region. To obviate the need for the input function or a reference region, in this paper, we propose to estimate the input function. We propose a novel concept of activity subspace, and estimate the input function by the analysis of the intersection of the activity subspaces. Then, the input function and the distribution volume (DV) parameter are refined and estimated iteratively. Thus, the underlying parametric image of the total DV is obtained. The proposed method is compared with a blind estimation method, iterative quadratic maximum-likelihood (IQML) via simulation, and the proposed method outperforms IQML. The proposed method is also evaluated in a brain PET dataset.

The performance in cooperative communication depends on careful resource allocation such as relay selection and power control, but the traditional centralized resource allocation requires precise measurements of channel state information (CSI). In this paper, we propose a distributed game-theoretical framework over multiuser cooperative communication networks to achieve optimal relay selection and power allocation without knowledge of CSI. A two-level Stackelberg game is employed to jointly consider the benefits of the source node and the relay nodes in which the source node is modeled as a buyer and the relay nodes are modeled as sellers, respectively. The proposed approach not only helps the source find the relays at relatively better locations and "buy” an optimal amount of power from the relays, but also helps the competing relays maximize their own utilities by asking the optimal prices. The game is proved to converge to a unique optimal equilibrium. Moreover, the proposed resource allocation scheme with the distributed game can achieve comparable performance to that employing centralized schemes.

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.

Multimedia social networks have become an emerging research area, in which analysis and modeling of the behavior of users who share multimedia are of ample importance in understanding the impact of human dynamics on multimedia systems. In peer-to-peer live-streaming social networks, users cooperate with each other to provide a distributed, highly scalable and robust platform for live streaming applications. However, every user wishes to use as much bandwidth as possible to receive a high-quality video, while full cooperation cannot be guaranteed. This paper proposes a game-theoretic framework to model user behavior and designs incentive-based strategies to stimulate user cooperation in peer-to-peer live streaming. We first analyze the Nash equilibrium and the Pareto optimality of two-person game and then extend to multiuser case. We also take into consideration selfish users' cheating behavior and malicious users' attacking behavior. Both our analytical and simulation results show that the proposed strategies can effectively stimulate user cooperation, achieve cheat free, attack resistance and help to provide reliable services.

Cooperative communications represent a potential candidate to combat the effects of channel fading and to increase the transmit energy efficiency in wireless sensor networks with the downside being the increased complexity. In sensor networks the power consumed in the receiving and processing circuitry can constitute a significant portion of the total consumed power. By taking into consideration such overhead, an analytical framework for studying the energy efficiency trade-off of cooperation in sensor networks is presented. This trade-off is shown to depend on several parameters such as the receive and processing power, the required quality-of-service, the power amplifier loss, and several other factors. The analytical and numerical results reveal that for small distance separation between the source and destination, direct transmission is more energy efficient than relaying. The results also reveal that equal power allocation performs as well as optimal power allocation for some scenarios. The effects of the relay location and the number of employed relays on energy efficiency are also investigated in this work. Moreover, there are experimental results conducted to verify the channel model assumed in the article.

A novel cooperative multiple access protocol for packet speech communications is proposed, where cooperation is achieved through the deployment of a relay node that exploits the silence periods typical of speech communications. The relay forwards speech packets for active calls using a subset of the free time slots left available by users that are silent. No new channel resources are needed for cooperation and the system encounters no bandwidth losses. Because the resources allocated to the relay are not drawn from the pool of reused resources but from those potentially used for random channel access. Thus, the use of cooperation introduces a tradeoff between the amount of help offered to active calls and the probability of a successful contention for channel access. Such cooperation tradeoff is investigated and guidelines for the choice of protocol parameters are developed. The throughput and delay performance of the proposed protocol are characterized and compared to a similar non-cooperative packet speech protocol. Results demonstrate significant gains achieved by the proposed cooperative protocol. Moreover, the speech quality under the cooperative protocol was measured using a perceptual model, and results reveal significant improvement over the non-cooperative protocol especially in the low signal-to-noise ratio regime.

In this paper we address the network maintenance problem, in which we aim to maximize the lifetime of a sensor network by adding a set of relays to it. The network lifetime is defined as the time until the network becomes disconnected. The Fiedler value, which is the algebraic connectivity of a graph, is used as an indicator of the network health. The network maintenance problem is formulated as a semi-definite programming (SDP) optimization problem that can be solved efficiently in polynomial time. First, we propose a network maintenance algorithm that obtains the SDP-based locations for a given set of relays. Second we propose a routing algorithm, namely, Weighted Minimum Power Routing (WMPR) algorithm, that significantly increases the network lifetime due to the efficient utilization of the deployed relays. Third, we propose an adaptive network maintenance algorithm that relocates the deployed relays based on the network health indicator. Further, we study the effect of two different transmission scenarios, with and without interference, on the network maintenance algorithm. Finally, we consider the network repair problem, in which we find the minimum number of relays along with their SDPbased locations to reconnect a disconnected network. We propose an iterative network repair algorithm that utilizes the network maintenance algorithm.

In wireless networks, reducing aggregate transmit power and in many cases, having even power distribution increase the network lifetime. Conventional direct transmission (DTX) scheme results in high aggregate transmit power and uneven power distribution. In this paper, we consider location-aware cooperation-based schemes namely immediate-neighbor cooperation (INC), maximal cooperation (MAX), and wireless network cocast (WNC) that achieve spatial diversity to reduce aggregate transmit power and even power distribution. INC utilizes twouser cooperative communication, resulting in good reduction of aggregate transmit power and low transmission delay; however, power distribution is still uneven. MAX utilizes multi-node cooperative communication, providing incremental diversity to achieve even power distribution and substantial reduction in aggregate transmit power. Transmission delay in MAX, however, grows quadratically with network sizes. As a result, the novel WNC is proposed to achieve incremental diversity as in MAX and low transmission delay as in INC. In WNC, mobile units acting as relays form unique linearly-coded signals from overheard signals and transmit them to the destination, where a multiuser detector jointly detects the symbols from all received signals. Performance evaluation in uniformly distributed networks shows that INC, MAX, and WNC substantially reduce aggregate transmit power while MAX and WNC also provide even power distribution.

Dynamic spectrum access has become a promising approach to fully utilize the scarce spectrum resources. In a dynamically changing spectrum environment, it is very important to consider the statistics of different users' spectrum access so as to achieve more efficient spectrum allocation. In this paper, we propose a primary-prioritized Markov approach for dynamic spectrum access through modeling the interactions between the primary and the secondary users as continuous-time Markov chains (CTMC). Based on the CTMC models, to compensate the throughput degradation due to the interference among secondary users, we derive the optimal access probabilities for the secondary users, by which the spectrum access of the secondary users is optimally coordinated, and the spectrum dynamics are clearly captured. Therefore, a good tradeoff can be achieved between the spectrum efficiency and fairness. The simulation results show that the proposed primary-prioritized dynamic spectrum access approach under proportional fairness criterion achieves much higher throughput than the CSMA-based random access approaches and the approach achieving max-min fairness. Moreover, it provides fair spectrum sharing among secondary users with only small performance degradation compared to the approach maximizing the overall average throughput.

Dynamic spectrum access has become a promising approach to improve spectrum efficiency by adaptively coordinating different users' access according to spectrum dynamics. However, users who are competing with each other for spectrum may have no incentive to cooperate, and they may even exchange false private information about their channel conditions in order to get more access to the spectrum. In this paper, we propose a repeated spectrum sharing game with cheat-proof strategies. By using the punishment-based repeated game, users get the incentive to share the spectrum in a cooperative way; and through mechanism-design-based and statistics-based approaches, user honesty is further enforced. Specific cooperation rules have been proposed based on the maximum total throughput and proportional fairness criteria. Simulation results show that the proposed scheme can greatly improve the spectrum efficiency by alleviating mutual interference.

The coexistence and resource sharing between a primary network utilizing cognitive relays and a secondary network are investigated. Two multiple access protocols are proposed to coordinate idle time slots sharing between the two networks in a TDMA setting. While relays in the primary network are using these free time slots to help users in the primary network forward their unsuccessfully transmitted packets, users of the secondary network use these time slots to transmit their own packets. The diversity-multiplexing sort of tradeoff imposed by this resource sharing problem and optimum resource allocation strategy are studied through the characterization of the maximum stable throughput region of both networks.

Cooperative communication can be used to reduce the transmit power of distant mobile units, compared to conventional direct transmission, given the same quality-of-service. However, imposing the constraint of having orthogonal transmission for the source and relays leads to large delay in TDMA systems. For a network of N mobile units, the transmission delay would be N(N+1)/2. In this work, we propose a location-aware cooperation-based scheme that aims to reduce transmit power of distant mobile units while maintaining a low transmission delay. The scheme utilizes a linear network coding protocol, where each mobile unit applies linear network coding to a set of transmit symbols that it has received previously. At the base station, multiuser detection is used to decouple the transmit symbols. Both decode-and-forward and amplify-and-forward cooperative protocols are considered. We show that our proposed scheme achieves a comparable bit-error-rate performance with the conventional cooperation-based TDMA scheme while requiring a delay of (2N-1) time slots, a substantial reduction in the transmission delay.

In this paper, the problem of deploying relay nodes in wireless sensor networks will be considered. A system consisting of a set of sensor nodes communicating to a fusion center, where decisions are made, is considered. Based on our system model assumptions, some sensor nodes will provide "less- informative" measurements to the fusion center about the state of nature; we consider relay nodes deployment in the sensor network instead of the less-informative sensor nodes to forward the measurements of the other, "more-informative" sensor nodes. This introduces a new tradeoff in the system design between the number of measurements sent to the fusion center and the reliability of the more-informative measurements, which is enhanced by deploying more relay nodes in the network. We will analyze the performance of two protocols over Rayleigh flat-fading channels. In Protocol I, each sensor node directly transmits its measurement to the fusion center. In Protocol II, relay nodes will be used instead of the less-informative sensor nodes to forward the measurements of the more-informative sensor nodes. Hence, in Protocol II, the reliability of the more-informative measurements is enhanced at the expense of having fewer measurements sent to the fusion center and this creates the tradeoff between the number of measurements available at the fusion center and the reliability of the measurements.

Cooperative spectrum sensing has been shown to greatly improve the sensing performance in cognitive radio networks. However, if the cognitive users belong to different service providers, they tend to contribute less in sensing in order to achieve a higher throughput. In this paper, we propose an evolutionary game framework to study the interactions between selfish users in cooperative sensing. We derive the behavior dynamics and the stationary strategy of the secondary users, and further propose a distributed learning algorithm that helps the secondary users approach the Nash equilibrium with only local payoff observation. Simulation results show that the average throughput achieved in the cooperative sensing game with more than two secondary users is higher than that when the secondary users sense the primary user individually without cooperation.

In order to fully utilize spectrum, auction-based dynamic spectrum allocation has become a promising approach which allows unlicensed wireless users to lease unused bands from spectrum license holders. Because spectrum resources are reusable by users far apart, in some scenarios, spectrum is more efficiently utilized by awarding one band to multiple secondary users simultaneously, which distinguishes it from traditional auctions where only one user can be the winner. However, the multi-winner auction is a new concept posing new challenges in the traditional auction mechanisms, because such mechanisms may yield low revenue and are not robust to some newly-emerging collusion. Therefore, in this paper, we propose an efficient mechanism for the multi-winner spectrum auction with collusion- resistant pricing strategies, in which the optimal spectrum allocation can be solved by binary linear programming and the pricing is formulated as a convex optimization problem. Furthermore, a greedy algorithm is proposed to reduce complexity for multi- band auctions. Simulation results are presented to evaluate our proposed auction mechanisms.

Modulation forensics is to detect the modulation type in wireless communications without any prior information. It finds both military and civilian applications such as surveillance and cognitive radio. It is a challenging task, especially in a non-cooperative environment, as no prior information on the incoming signal is available at the receiver. In this paper, we investigate the modulation forensics of linear digital modulations and space-time orthogonal code in slowly varying frequency-selective fading channels. With unknown channel vector, and phase distortion at the receive-side, we derive a composite test consisting second-moment nonlinearity and maximum likelihood test, and discuss the performance and forensic system confidence measure. It is shown that the proposed algorithm achieves almost perfect identification of the space-time coding, and high accuracy rate of modulation type detection.

Multimedia social network analysis is an emerging research area, which analyzes the behavior of users who share multimedia content and investigates the impact of human dynamics on multimedia systems. In peer-to-peer live-streaming social networks, user cooperate with each other to provide a distributed, highly scalable and robust platform for live streaming applications. However, every user wishes to use as much bandwidth as possible to receive a high-quality video, and full cooperation cannot be guaranteed. This paper proposes a game-theoretic framework to model user behavior and designs incentive-based strategies to stimulate user cooperation in peer-to-peer live streaming. We analyze the Nash equilibrium and the Pareto optimality of the game. We also take into consideration selfish users' cheating behavior and propose cheat-proof strategies. Both our analytical and simulation results show that the proposed strategies can effectively stimulate user cooperation, achieve cheat free and help provide reliable services.

In this paper, we propose a robust method based on the modified p-spectrum to detect heart beats in ECG signals, which is also referred as QRS detection in the literature. QRS detection is an old problem that has been studied for several decades. In the literature, there are many methods based on various forms of transformations and thresholding, which require predetermined or fine-tuned thresholds. Another class of existing methods are based on machine learning, which require carefully labeled training data. In our study, we propose the modified p-spectrum for QRS detection, which does not require either predetermined thresholds or training data, and can operate in real-time. The proposed method is evaluated using the MIT-BIH arrhythmia database.

Component forensics is an emerging methodology for forensic analysis that aims at estimating the algorithms and parameters in each component of a digital device. This paper proposes a theoretical foundation to examine the performance limits of component forensics. Using ideas from pattern classification theory, we define formal notions of identifiability of components in the information processing chain. We show that the parameters of certain device components can be accurately identified only in controlled settings through semi non-intrusive forensics, while the parameters of some others can be computed directly from the available sample data via complete non-intrusive analysis. We then extend the proposed theoretical framework to quantify and improve the accuracies and confidence in component parameter identification for several forensic applications.

In this paper, a spectrum sharing scheme that will coordinate among different co-existing cellular operators competing for the same spectrum band is proposed. Based on this scheme, the cell of an operator can be divided into several sub-regions, and mobile stations (MSs) inside each sub-region form one subset. The whole frequency band assigned to a cell is partitioned into slots dedicated to the subsets based on the quality of service (QoS) demand. When interference from other operators is detected, the victim operator can switch the frequency of the interfered MSs with the MSs in the safe region, and/or switch to the reserved band. In this way, the inter-operator interference (IOI) can be reduced. From the simulation results, it is shown that with the proposed protocol, the total power consumption of both operators can be reduced significantly. Furthermore, it has been demonstrated that in order to reduce the IOI in a high-density area, the operator should reserve more bandwidth for potential frequency-switching.

Dynamic spectrum access has become a promising approach that can coordinate different users' access to adapt to spectrum dynamics to improve spectrum efficiency. However, users competing for an open spectrum may have no incentive to cooperate with each other, and they may even exchange false private information about their channel conditions in order to get more access to the spectrum. Therefore, in this paper, we propose a self-enforcing truth-telling mechanism by modeling the distributed spectrum access as a repeated game. In this game, if any greedy user deviates from cooperation, punishment will be triggered. Through the Bayesian mechanism design, users have no incentive to reveal false channel conditions, and the competing users are enforced to cooperate with each other honestly. The simulation results show that the proposed scheme can greatly improve the spectrum efficiency by alleviating mutual interference; furthermore, the best strategy for each user is demonstrated to be reporting the actual channel condition.

Multimedia social network analysis is a research area with growing importance, in which the social network members share multimedia contents with all different purposes and analyzing their behavior help design more secured and efficient multimedia and networking systems. In this paper, we focus on multimedia fingerprinting social network, in which multi-user collusion being a powerful attack, where a group of attackers collectively undermine the traitor tracing capability. During collusion, different colluders have different objective thus, the colluders form a social network and an how to achieve agreement on distributing the risk/profit among colluders and ensure fairness of the attack is a crucial question. This paper models the dynamics among colluders as a non-cooperative game, propose a general model of utility functions and study four different bargaining solutions of this game.

Digital images have seen increased use in applications where their authenticity is of prime importance. This proves to be problematic due to the widespread availability of digital image editing software. As a result, there is a need for the development of reliable techniques for verifying an image's authenticity. In this paper, a blind forensic algorithm is proposed for detecting the use of global contrast enhancement operations to modify digital images. Furthermore, a separate algorithm is proposed to identify the use of histogram equalization, a commonly implemented contrast enhancement operation. Both algorithms perform detection by seeking out unique artifacts introduced into an image's histogram as a result of the particular operation examined. Additionally, results are presented showing the effectiveness of both proposed algorithms.

Multiuser collusion is an effective attack against traitor-tracing multimedia fingerprinting, where a group of attackers collectively mount attacks to reduce their risk of being detected. During collusion, each attacker wishes to maximize his or her own payoff. To resolve the conflict, colluders have to negotiate with each other and achieve fair collusion. An attacker also needs to decide with whom he or she wants to collude. Though colluding with more people helps further reduce the risk, it also makes an attacker share with more people the rewards from illegal usage of multimedia. This paper uses game theory to model the complex colluder dynamics and analyzes the tradeoff between the risk and the rewards. We study how the selection of fellow attackers affects each colluder's utility, and analyze the optimum strategies that maximize colluders' payoffs.

In this paper, we address the network maintenance problem, in which we aim to maximize the lifetime of a sensor network by adding a set of relays to it. The network lifetime is defined as the time until the network becomes disconnected. The Fiedler value, which is the algebraic connectivity of a graph, is used as an indicator of the network health. The network maintenance problem is formulated as a standard semi- definite programming (SDP) optimization problem that can be solved efficiently in polynomial time. First, we present a network maintenance algorithm that obtains the near-optimum locations for a given set of relays. Second we propose a routing algorithm, namely, Weighted Minimum Power Routing (WMPR) algorithm, that significantly increases the network lifetime due to the efficient utilization of the deployed relays. Third, we propose an adaptive network maintenance algorithm that relocates the deployed relays based on the network health indicator. Finally, we consider the network repair problem, in which we find the minimum number of relays along with their near-optimum locations to reconnect a disconnected network. We propose an iterative network repair algorithm that utilizes the network maintenance algorithm.

Diversity techniques are an effective answer to the challenges presented by fading channels. This paper focuses on studying the performance of systems with diversity of three forms: source coding diversity, channel coding diversity, and user-cooperation diversity. To best reflect a focus on real-time multimedia communications, performance is measured through the distortion exponent, which measures the rate of decay of the end-to-end distortion at high SNRs. User-cooperation diversity takes the form of a relay channel implemented with amplify-and-forward processing at the relay. The results show that channel coding diversity provides the best performance, followed by source coding diversity. The results also show a tradeoff between the quality (resolution) of the source encoder and the amount of cooperation (number of relay nodes).

In this paper, the design of distributed space- frequency codes (DSFCs) for wireless relay networks employing the amplify-and-forward (AAF) protocol is considered. The term distributed comes from the fact that the space-frequency code is distributed among randomly located relay nodes. DSFCs are designed to achieve the multi-path (frequency) and cooperative diversities of the wireless relay channels. We derive sufficient conditions for the proposed code design to achieve full diversity based on minimizing the pairwise error probability (PEP). We prove that the proposed DSFC can achieve full diversity of order LN, where L is the number of paths of the channel and N is the number of relay nodes, for any N and for the cases of L = 1 (flat, frequency-nonselective fading channel) and L = 2 (two-ray fading channel).

In order to fully utilize scarce spectrum resources, dynamic spectrum allocation becomes a promising approach to increase the spectrum efficiency for wireless networks. However, the collusion among selfish network users may seriously deteriorate the efficiency of dynamic spectrum sharing. The network users' behaviors and dynamics need to be taken into consideration for efficient and robust spectrum allocation. In this paper, we model the spectrum allocation in wireless networks with multiple selfish legacy spectrum holders and unlicensed users as multi-stage dynamic games. In order to combat user collusion, we propose a pricing-based collusion-resistant dynamic spectrum allocation approach to optimize overall spectrum efficiency, while not only keeping the participating incentives of the selfish users but also combating possible user collusion. The simulation results show that the proposed scheme achieves high efficiency of spectrum usage even with the presence of severe user collusion.

In self-organized mobile ad hoc networks (MANETs) where each user is its own authority, fully cooperative behaviors, such as unconditionally forwarding packets for each other or honestly revealing its private information, cannot be directly assumed. The pricing mechanism is one way to provide incentives for the users to act cooperatively by awarding some payment for cooperative behaviors. In this paper, we consider efficient routing in self-organized MANETs and model it as multi-stage dynamic pricing games. A game theoretical framework for dynamic pricing-based routing in MANETs is proposed to maximize the sender/receiveriquests payoff by considering the dynamic nature of MANETs. Meanwhile, the forwarding incentives of the relay nodes can also be maintained by optimally pricing their packet-forwarding services based on the auction rules and introducing the cartel maintenance enforcing mechanism. The simulation results illustrate that the proposed dynamic pricing-based routing approach has significant performance gains over the existing static pricing approaches.

The study of joint source-channel coding (JSCC) systems faces one major challenge in obtaining an analytical expression for the function that links end-to-end distortion with channel signal-to-noise ratio, the D-SNR curve. In this paper, for certain multimedia systems using practical source and channel codes in a JSCC bit rate allocation design, the D-SNR curve is shown to be well approximated by a set of carefully selected points where the relative contribution of channel errors to end-to-end distortion is small. This approach has the potential advantage that it could be applied to represent performance of many practical systems using JSCC bit rate allocation for which it is shown that the D-SNR function is approximately linear in log-log scales. A unified framework for the modeling, analysis and performance measurement of these systems is proposed by considering a view of diversity more general than its usual interpretation. This view extends that of diversity to include redundant information so coding and diversity gain are still used to characterize performance. Furthermore, the proposed approach is applied to study issues arising from using practical source and channel codes, including the effects on performance of channel codes of different strength or source codes with different compression efficiency.

This paper addresses the problem of joint optimization of transmit beamforming and space-frequency (SF) coding for MIMO-OFDM systems with spatial correlation feedback in broadband communications. This problem is challenging in the sense that the transmitter should be designed to beamform across multiple eigenspaces associated with the multipath environment simultaneously. With arbitrary transmit spatial correlation, the performance analysis for SF-coded MIMO-OFDM systems with beamforming is provided, and a general optimization problem for the beamforming design is formulated. Three suboptimal approaches to design the beamformer based on the derived design criteria are proposed: i) Eigenvalue selection scheme; ii) Eigenspace selection scheme; and iii) Per-subcarrier approach based on decoding at each subcarrier. The proposed schemes take into account the multiple eigenspace information associated with the multipath-delay channel. Improvement in the performance over SF coding without beamforming is shown through simulations in terms of bit error rate. The Eigenvalue selection scheme provides the best performance among the proposed algorithms. This scheme locates the subspace associated with the largest eigenvalues in the eigenspace of the covariance matrices. With the Eigenvalue selection scheme, the performance improvement is about 3 dB over the SF coding without beamforming for highly correlated channels as shown in our simulations.

Digital imaging has experienced tremendous growth in recent decades, and digital camera images have been used in a growing number of applications. With such increasing popularity and the availability of low-cost image editing software, the integrity of digital image content can no longer be taken for granted. This paper introduces a new methodology for the forensic analysis of digital camera images. The proposed method is based on the observation that many processing operations, both inside and outside acquisition devices, leave distinct intrinsic traces on digital images, and these intrinsic fingerprints can be identified and employed to verify the integrity of digital data. The intrinsic fingerprints of the various in-camera processing operations can be estimated through a detailed imaging model and its component analysis. Further processing applied to the camera captured image is modelled as a manipulation filter, for which a blind deconvolution technique is applied to obtain a linear time-invariant approximation and to estimate the intrinsic fingerprints associated with these postcamera operations. The absence of camera-imposed fingerprints from a test image indicates that the test image is not a camera output and is possibly generated by other image production processes. Any change or inconsistencies among the estimated camera-imposed fingerprints, or the presence of new types of fingerprints suggest that the image has undergone some kind of processing after the initial capture, such as tampering or steganographic embedding. Through analysis and extensive experimental studies, this paper demonstrates the effectiveness of the proposed framework for nonintrusive digital image forensics.

In autonomous mobile ad-hoc networks, one major challenge is to stimulate cooperation among selfish nodes, especially when some nodes may be malicious. In this paper, we address cooperation stimulation in realistic yet challenging contexts where the environment is noisy and the underlying monitoring is imperfect. We have first explored the underlying reasons why stimulating cooperation under such scenarios is difficult. Instead of trying to enforce all nodes to act fully cooperatively, our goal is to stimulate cooperation in a hostile environment as much as possible through playing conditional altruism. To formally address the problem, we have modeled the interactions among nodes as secure routing and packet forwarding games under noise and imperfect observation, and devised a set of reputation-based attack-resistant cooperation strategies without requiring any tamper-proof hardware or central banking service. The performance of the devised strategies has also been evaluated analytically. The limitations of the game-theoretic approaches and the practicability of the devised strategies have also been investigated through theoretical analysis and extensive simulation studies. The results have demonstrated that although sometimes a gap may exist between the ideal game model and the reality, game-theoretic analysis can still provide thoughtful insights and useful guidelines when designing cooperation strategies.

In distributed wireless networks without centralized control, each user is its own authority to maximize its own performance. This distributed characteristic provides the users with the incentives of greedy competitions for the network resources such as bandwidth and transmission time. Such competitions deteriorate the system performance dramatically and result in low non-cooperative performances. Ensuring cooperation among selfish users can improve the performances and thus becomes an important issue for such wireless networks. In this paper, a Cartel Maintenance repeated game framework is proposed to enforce the cooperation among selfish users. The soul of Cartel Maintenance is to construct contracts among independent individuals for cooperative benefits and non-cooperative punishment, so as to limit inefficient competitions. In the proposed framework, a trigger-punishment game rule is designed to encourage the users to follow the cooperative strategy. First, all users agree to cooperate. Then in the following time slot, if users observe the others play cooperatively, cooperation will be played. If some users detect that others may defect based on the observed information, these users will play punishment phases for a certain period of time. This punishment is optimized so that the gain obtained by the deviation users is outweighed by the future punishments. Therefore, no user has the incentives to deviate, and the cooperation among selfish users is enforced. Then, the framework is employed to the multiple random access scenarios in wireless networks with selfish users, where the closed-form optimal solutions of cooperation enforcement are derived. The simulation results show that the proposed scheme can achieve significant performance gains over the non-cooperation scheme by having enough punishment threat to keep the cooperation among users.

Recently, the merits of cooperative communication in the physical layer have been explored. However, the impact of cooperative communication on the design of the higher layers has not been well-understood yet. Cooperative routing in wireless networks has gained much interest due to its ability to exploit the broadcast nature of the wireless medium in designing power efficient routing algorithms. Most of the existing cooperation based routing algorithms are implemented by finding a shortest path route first and then improving the route using cooperative communication. As such, these routing algorithms do not fully exploit the merits of cooperative communications, since the optimal cooperative route might not be similar to the shortest path route. In this paper, we propose a cooperation-based routing algorithm, namely, the minimum power cooperative routing (MPCR) algorithm, which makes full use of the cooperative communications while constructing the minimum-power route. The MPCR algorithm constructs the minimum-power route, which guarantees certain throughput, as a cascade of the minimum-power single-relay building blocks from the source to the destination. Thus, any distributed shortest path algorithm can be utilized to find the optimal cooperative route with polynomial complexity. Using analysis, we show that the MPCR algorithm can achieve power saving of 65.61% in regular linear networks and 29.8% in regular grid networks compared to the existing cooperation-based routing algorithms, where the cooperative routes are constructed based on the shortest-path routes. From simulation results, MPCR algorithm can have 37.64% power saving in random networks compared to those cooperation-based routing algorithms.

In this paper; we propose a new cooperative communication protocol, which achieves higher bandwidth efficiency while guaranteeing the same diversity order as that of the conventional cooperative schemes. The proposed scheme considersrelayselectionvia the available partial channel state information (CSI) at the source and the relays. In particular, we discuss the multi-node decode-and-forward cooperative scenarios, where arbitrary N relays are available. The source determines when it needs to cooperate with one relay only, and which relay to cooperate with in case of cooperation, i.e., "Whentocooperate?" and "Whomtocooperatewith?". An optimal relay is the one which has the maximum instantaneous scaled harmonic mean functionof its source-relay and relay-destination channel gains. For the symmetric scenario, we derive an approximate expression of the bandwidth efficiency and obtain an upper bound on the symbol error rate (SER) performance. We show that full diversity is guaranteed and that a significant increase of the bandwidth efficiency is achieved. Moreover, we present the tradeoff between the achievable bandwidth efficiency and the corresponding SER. Finally, the obtained analytical results are verified through computer simulations.

Transmit power limitations and quality-impairing channels present key challenges to wireless communications. A possible solution to these problems is the application of user-cooperation techniques so as to improve link quality. This paper studies the tradeoffs involved in combining user cooperation with practical source and channel coding in systems featuring rate-adaptive source and channel coding of conversational traffic. Performance is measured through the D-SNR curve, which measures the relation between end-to-end distortion and channel signal-to-noise ratio (SNR). The D-SNR curve is accurately characterized as a linear function in log-log scales. Also, it is shown that the tradeoffs involved in combining amplify-and-forward or decode-and-forward cooperation with source and channel coding translates into cooperative schemes showing a decrease in distortion at approximately half the rate as non-cooperative schemes but with larger coding gain. Because of this, the studied non-cooperative schemes show better performance only at high SNR. In addition, the D-SNR characterization is used to compare amplify-and-forward and decode-and-forward cooperation for channel codes of different strength and to study the effects of source codec efficiency, where it is shown that diversity gain is reduced proportionally to the source codec loss of efficiency.

In wireless ad hoc networks, autonomous nodes are reluctant to forward others' packets because of the nodes' limited energy. However, such selfishness and noncooperation deteriorate both the system efficiency and nodes' performances. Moreover, the distributed nodes with only local information may not know the cooperation point, even if they are willing to cooperate. Hence, it is crucial to design a distributed mechanism for enforcing and learning the cooperation among the greedy nodes in packet forwarding. In this paper, we propose a self- learning repeated-game framework to overcome the problem and achieve the design goal. We employ self-transmission efficiency as the utility function of individual autonomous node. The self transmission efficiency is defined as the ratio of the power for self packet transmission over the total power for self packet transmission and packet forwarding. Then, we propose a framework to search for good cooperation points and maintain the cooperation among selfish nodes. The framework has two steps: First, an adaptive repeated game scheme is designed to ensure the cooperation among nodes for the current cooperative packet forwarding probabilities. Second, self-learning algorithms are employed to find the better cooperation probabilities that are feasible and benefit all nodes. We propose three learning schemes for different information structures, namely, learning with perfect observability, learning through flooding, and learning through utility prediction. Starting from noncooperation, the above two steps are employed iteratively, so that better cooperating points can be achieved and maintained in each iteration. From the simulations, the proposed framework is able to enforce cooperation among distributed selfish nodes and the proposed learning schemes achieve 70% to 98% performance efficiency compared to that of the optimal solution.

In this paper, we consider a class of energy-aware routing algorithm that explicitly takes into account the connectivity of the remaining sensor network. In typical sensor network deployments, some nodes may be more important than other nodes because the failure of these nodes causes the network disintegration, which results in early termination of information delivery. To mitigate this problem, we propose a class of routing algorithms called keep-connect algorithms, that use computable measures of network connectivity in determining how to route packets. The proposed algorithms embed the importance of the nodes in the routing cost/metric. The importance of a node is characterized by the algebraic connectivity of the remaining graph when that node fails. We prove several properties of the proposed routing algorithm including the energy consumption upper bound. Using extensive simulations, we demonstrate that the proposed algorithm achieves significant performance improvement compared to the existing routing algorithms. More importantly, we show that our proposed algorithm is more robust in terms of algebraic network connectivity compared to the existing algorithm. Finally, we present the distributed implementation of our proposed algorithm.

Designing diversity achieving schemes over the wireless broadband fading relay channels is crucial to achieve higher diversity gains. These gains are achieved by exploiting the multipath (frequency) and cooperative diversities to combat the fading nature of wireless channels. The challenge is how to design space frequency codes, distributed among randomly located nodes that can exploit the frequency diversity of the wireless broadband channels. In this paper, the design of distributed space-frequency codes (DSFCs) for wireless relay networks is considered. The proposed DSFCs are designed to achieve the frequency and cooperative diversities of the wireless relay channels. The use of DSFCs with the decode-and-forward (DAF) and amplify-and forward (AAF) protocols is considered. The code design criteria to achieve full diversity, based on the pairwise error probability (PEP) analysis, are derived. For DSFC with the DAF protocol, a two-stage coding scheme, with source node coding and relay nodes coding, is proposed. We derive sufficient conditions for the proposed code structures at the source and relay nodes to achieve full diversity of order NL, where N is the number of relay nodes and L is the number of paths per channel. For the case of DSFC with the AAF protocol, a structure for distributed space-frequency coding is proposed.

This paper proposes a relay-based scheme where a fixed number of relays are utilized to design an OFDM cooperative protocol. By exploiting limited feedback from the destination terminal (central node) such that each relay is able to help forward information of multiple sources in one OFDM symbol, the proposed cooperative protocol not only achieves full diversity but also efficiently utilizes available bandwidth. A practical relay assignment scheme is also proposed to specify the pairing of sources and relays in cooperative networks. The outage probability analysis of the proposed cooperative protocol is provided, and the analytical results are validated via simulations. Moreover, a closed-form lower bound on the outage probability of any relay-assignment schemes is established to provide a performance benchmark of the proposed cooperative protocol. Based on the outage probability analysis, the optimum relay location for the proposed relay-assignment scheme is determined. Both theoretical and simulation results show that in WLAN scenario the proposed cooperative protocol can achieve significant coverage extension compared to the direct transmission.

A novel cooperative multiple access protocol for packet speech communications is proposed. Cooperation is achieved through a relay node and by exploiting the silence periods during speech communication. The relay forwards packets for the active calls during some of the time slots that are free from those users which are silent. Therefore, no extra channel resources are needed for cooperation and the system encounters no bandwidth losses. A Markov model characterizing the network operation is built and thoroughly analyzed. The throughput and delay performance of the proposed protocol are characterized and compared to a similar non-cooperative protocol for speech communications.

Recently, cooperative routing in wireless networks has gained much interest due to its ability to exploit the broadcast nature of the wireless medium in designing power-efficient routing algorithms. Most of the existing cooperation-based routing algorithms are implemented by finding a shortest-path route first. As such, these routing algorithms do not fully exploit the merits of cooperative communications at the physical layer. In this paper, we propose a cooperation-based routing algorithm, namely, minimum power cooperative routing (MPCR) algorithm, which makes full use of the cooperative communications while constructing the minimum-power route. The MPCR algorithm constructs the minimum-power route as a cascade of the minimum-power single-relay building blocks from the source to the destination. Hence, any distributed shortest-path algorithm can be utilized to find the optimal route with polynomial complexity, while guaranteeing certain throughput. We show that the MPCR algorithm can achieve power saving of 57.36% compared to the conventional shortest-path routing algorithms. Furthermore, the MPCR algorithm can achieve power saving of 37.64% compared to the existing cooperative routing algorithms, in which the selected routes are constructed based on the noncooperative routes.

Enhancing the connectivity of wireless sensor networks is necessary to avoid the occurrence of coverage gaps. In this paper, we aim at improving the network connectivity of a given network by adding a set of relays to it. We characterize the network connectivity by the Fiedler value, which is the second smallest eigenvalue of the Laplacian matrix representing the network graph. We propose a network-maintenance algorithm, which finds the best locations for a given set of relays. The proposed algorithm obtains the best relays' locations through a multi-level approach. In each level, the search problem can be formulated as a standard semi-definite programming (SDP) optimization problem. We show that the proposed algorithm can increase the average Fiedler value by 35% by adding one relay only.

This paper studies the function that links end-to-end distortion with channel signal-to-noise ratio, the D-SNR curve, for systems using Joint Source-Channel Coding (JSCC) bit rate allocation. The D-SNR curve is shown to be well approximated for certain multimedia systems using practical source and channel codes, by a linear function in log-log scales. The D-SNR curve is characterized by following an approach that considers a set of carefully selected points, which are those where the relative contribution of channel errors to end-to-end distortion is small. The D-SNR characterization is applied to study the effects on performance of channel codes of different strength or source codes with different compression efficiency. The approach followed to characterize the D-SNR curve has the potential advantage that it could be applied to represent performance of many practical systems using JSCC bit rate allocation, as it is illustrated for a scheme carrying voice traffic.

In this paper, the design of distributed space- frequency codes (DSFCs) implementing the decode-and-forward (DAF) protocol for wireless relay channels is considered. The proposed DSFCs are designed to achieve the frequency and cooperative diversities of the wireless relay channels. A two-hop system model, where there is no direct link from the source node to the destination node, is considered. We propose to use two stages of coding, namely, source node coding and relay nodes coding. The proposed DSFCs are proved to achieve full diversity of order NL where N is the number of relay nodes and L is the number of paths per channel.

In this paper, we consider the design of synchronization-aware distributed space-time codes, which we denote as diagonal distributed space-time codes (DDSTC), for N relay nodes helping the source. We impose the diagonal structure of the distributed space-time code to simplify synchronization among the different relay nodes because it is very difficult to synchronize simultaneous transmissions of randomly located relay nodes. We derive an upper bound on the outage probability of the system, which shows that a diversity of order N can be achieved using the diagonal structure of the code. Then, we derive the code design criterion for the DDSTC based on minimizing the pairwise error probability (PEP) to achieve full diversity.

Concealed weapons detection is one of the greatest challenges facing national security nowadays. Recently, it has been shown that each weapon can have a unique fingerprint, which is a set of electromagnetic (EM) resonant frequencies determined by its size, shape, and physical composition. Extracting the resonant frequencies of each weapon is one of the major tasks of any detection system. In this paper, we model the reflected signal from each object as a summation of sinusoidal signals, each at certain frequency equal to one of the object's resonant frequencies. Using this model, we propose a detection approach that is based on a modified version of the multiple signal classification (MUSIC) algorithm. We show by simulations that each object can be represented using a two-dimensional vector, which consists of its two major resonant frequencies.

This paper provides performance analysis of multiband OFDM UWB system that takes into consideration the effect of intersymbol interference and imperfect frequency and timing synchronization. The system performance is analyzed under UWB multipath fading channels, as specified in the IEEE 802.15.3a channel standard. Average signal-to-noise ratio is first derived for the system. Then an approximation technique is employed to obtain a closed-form average bit error probability that provides a profound understanding of the performance of the multiband OFDM UWB system. Simulation results validate the theoretical analysis.

Digital fingerprinting is an emerging technology in media security to identify the source of illicit copies and trace traitors. Collusion is a powerful attack, in which a group of attacker collectively mount attacks against digital fingerprinting. In multimedia fingerprinting, there exists complex dynamics between the colluders and the fingerprint detector, who have conflicting objectives and influence each other's performance and decisions. This paper proposes a game-theoretic framework to formulate and analyze the colluder-detector dynamics, in an effort to understand its impact on the traitor tracing performance of multimedia fingerprints. We investigate how colluders adjusts the collusion attacks to minimize their risk under the fairness constraint; and study how the fingerprint detector adapts his/her detection strategy accordingly to improve the collusion resistance, which is shown to be the min-max solution.

This paper considers the problem of semi non-intrusive component forensics and proposes a methodology to identify the algorithms and parameters employed by various processing modules inside a digital camera. The proposed analysis techniques assume the availability of the camera; and introduce a forensic methodology to estimate the parameters of the color interpolation and white balancing algorithms employed in cameras. We devise testing conditions, and design good input patterns to improve the overall accuracy in parameter estimation. As demonstrated by the results in the paper, the proposed techniques provide a much lower estimation bias and variance compared to non-intrusive analysis. The features obtained from component forensic analysis provide useful evidence for such applications as analyzing technology evolution trend, detecting technology infringement/licensing, protecting intellectual property rights, and determining camera source.

In non-intrusive forensic analysis, we wish to find information and properties about a piece of data without any reference to the original data prior to processing. An important first step to forensic analysis is the detection and estimation of block processing. Most existing work in block measurement uses strong assumptions on the data related to the block size or the method of compression. In this paper, we propose a new method to estimate the block size in digital images in a blind manner for use in a forensic context. We make no assumptions on the block size or the nature of any previous processing. Our scheme can accurately estimate block sizes in images up to a PSNR of 42 dB where block artifacts are perceptually invisible. We also offer a measure of detection accuracy which correctly classifies an image as block-processed with a probability of 95.0% while keeping the probability of false alarm at 7.4%.

During multi-user collusion attacks against digital fingerprinting, an important issue that colluders have to address is to distribute the risk evenly among all colluders and achieve fairness of the attack. Although they might agree so, some selfish colluders may break their agreement and process their fingerprinted copies before collusion in order to further reduce their own risk. To protect their own interest, other colluders have to detect these selfish colluders and exclude them from multi-user collusion. This paper studies this problem of traitors within traitors. We propose an autonomous selfish colluder detection and identification algorithm, in which colluders help each other detect selfish behavior. We show that the proposed algorithm can correctly identify all selfish colluders without falsely accusing any others, even when a small group of selfish colluders collaborate with each other to change the detection results.

The performance limits of multimedia systems combining source (multiple description) coding and channel coding with user cooperation diversity over multi-hop channels is studied. Performance is measured through the distortion exponent, which measures the rate of decay of the end-to-end distortion at asymptotic high SNRs. Two implementations for user cooperation are considered: amplify-and-forward and decode-and-forward. Results comparing different source and channel coding schemes show that optimum channel coding diversity provides the best performance, followed by source coding diversity. The results also show that at low bandwidth expansion factor, source encoding distortion is the main limiting factor. As the bandwidth expansion factor increases, user cooperation diversity is the main limiting factor, thus, the distortion exponent could be improved by increasing the number of relays.

Multi-user collusion is an cost-effective attack against digital fingerprinting, in which a group of attackers collectively undermine the traitor tracing capability of digital fingerprints. However, during multi-user collusion, each colluder wishes to minimize his/her own risk and maximize his/her own profit, and different colluders have different objectives. Thus, an important issue during collusion is to agree on how to distribute the risk/profit among colluders and ensure fairness of the attack. To have a better understanding of the attackers' behavior during collusion to achieve fairness, this paper models the dynamics among colluders as a non-cooperative game. We then study the Pareto-optimal set, where no colluder can further increase his/her own payoff without decreasing others', and analyze the Nash bargaining solution of this game.

The area of non-intrusive forensic analysis has found many applications in the area of digital imaging. One unexplored area is the identification of source coding in digital images. In other words, given a digital image, can we identify which compression scheme was used, if any? This paper focuses on the aspect of transform coder classification, where we wish to determine which transform was used during compression. This scheme analyzes the histograms of coefficient subbands to determine the nature of the transform method. By obtaining the distance between the obtained histogram and the estimate of the original histogram, we can determine if the image was compressed using the transform tested. Results show that this method can successfully classify compression by transform as well as detect whether any compression has occurred at all in an image.

In this digital era, digital multimedia contents are often transmitted over networks without any protection. This raises serious security concerns since the receivers/subscribers do not know what processes have been applied to multimedia data, and neither do they know whether this copy comes from a trusted source. Therefore, it is critical to provide forensic tools to identify the history of operations applied to multimedia data. In this paper, we focus on the identification of source coding techniques applied to multimedia, and we investigate the forensic analysis of transform based coding (both DCT and DWT based), subband coding, and linear predictive coding. Using the intrinsic fingerprints as trace of evidences, we construct an image source coding forensic system that analyzes which source encoder is used to compress the image and provides confidence measurements. Our simulation results show that the proposed system provides trustworthy performance: the probability of detecting the correct source encoder is 0.82 when PSNR = 40 dB, and it can correctly identify the source encoder with probability 0.98 with PSNR = 20dB.

The performances in cooperative communications depend on careful resource allocation such as relay selection and power control, but traditional centralized resource allocation needs considerable overhead and signaling to exchange the information for channel estimations. In this paper, we propose a distributed buyer/seller game theoretic framework over multiuser cooperative communication networks to stimulate cooperation and improve the system performance. By employing a two-level game to jointly consider the benefits of source nodes as buyers and relay nodes as sellers, the proposed approach not only helps the source smartly find the relays at relatively better locations and buy optimal amount of power from them, but also helps the competing relays maximize their own utilities by asking the reasonable prices. The game is proved to converge to a unique optimal equilibrium. From the simulation results, the relays in good locations can play more important roles in increasing source node's utility, so the source would like to buy more power from these preferred relays. On the other hand, the relays have to set the proper prices to attract the source's buying because of competition from other relays and selections from the source. Moreover, the distributed game resource allocation can achieve comparable performance compared with the centralized one.

With a rapid growth of imaging technologies and an increasingly widespread usage of digital images and videos for a large number of high security and forensic applications, there is a strong need for techniques to verify the source and integrity of digital data. Component forensics is new approach for forensic analysis that aims to estimate the algorithms and parameters in each component of the digital device. In this paper, we develop a novel theoretical foundation to understand the fundamental performance limits of component forensics. We define formal notions of identifiability of components in the information processing chain, and present methods to quantify the accuracies at which the component parameters can be estimated. Building upon the proposed theoretical framework, we devise methods to improve the accuracies of component parameter estimation for a wide range of forensic applications.

Cognitive radio has been proposed as a novel approach for improving the utilization of the precious limited radio resources by dynamically accessing the spectrum. One of the major design challenges is to coordinate and cooperate in accessing the spectrum opportunistically among multiple distributive users with only local information. In this paper, we propose a game theoretical approach with a new solution concept, the correlated equilibrium, which is better compared to the non-cooperative Nash equilibrium in terms of spectrum utilization efficiency and fairness among the distributive users. To achieve this correlated equilibrium, we construct an adaptive algorithm based on no-regret learning that guarantees convergence. From the simulation results, the optimal correlated equilibria achieve better fairness and 5% ~15% performance gain, compared to the Nash equilibria.

MOTIVATION: Our purpose is to develop a statistical modeling approach for cancer biomarker discovery and provide new insights into early cancer detection. We propose the concept of dependence network, apply it for identifying cancer biomarkers, and study the difference between the protein or gene samples from cancer and non-cancer subjects based on mass-spectrometry (MS) and microarray data. RESULTS: Three MS and two gene microarray datasets are studied. Clear differences are observed in the dependence networks for cancer and non-cancer samples. Protein/gene features are examined three at one time through an exhaustive search. Dependence networks are constructed by binding triples identified by the eigenvalue pattern of the dependence model, and are further compared to identify cancer biomarkers. Such dependence-network-based biomarkers show much greater consistency under 10-fold cross-validation than the classification-performance-based biomarkers. Furthermore, the biological relevance of the dependence-network-based biomarkers using microarray data is discussed. The proposed scheme is shown promising for cancer diagnosis and prediction. AVAILABILITY: See supplements: http://dsplab.eng.umd.edu/~genomics/dependencenetwork/

In this paper, a distributive non-cooperative game is proposed to perform sub-channel assignment, adaptive modulation, and power control for multi-cell multi-user orthogonal frequency division multiplexing access (OFDMA) networks. Each individual user's goal is to minimize his/her own transmitted power in a distributed manner under the constraints that the desirable rate is achieved and the transmitted power is bounded. The pure non-cooperative game may result in non-convergence or some undesirable Nash Equilibriums with low system and individual performances. To enhance the performances, a virtual referee is introduced to the networks and is in charge of monitoring and improving the outcome of non-cooperative competition for resources among the distributed users. If the game outcome is not desirable, either the required transmission rates should be reduced or some users should be prevented from using some radio resources such as sub-channels, so that the rest of users can share the limited resources more efficiently. Moreover, it can be shown that the introduction of the virtual referee does not increase the complexity of the networks. From the simulation results in a two-cell case, the proposed scheme reduces the transmitted power by 80% and 25% compared with the fixed channel assignment algorithm and the iterative water-filling algorithm in the literature, respectively. The achievable rate can be improved by 10%. In a multi-cell case, the proposed scheme can have up to 40% power reduction compared with the iterative water-filling algorithm when the co-channel interferences are severe.

Extending lifetime of battery-operated devices is a key design issue that allows uninterrupted information exchange among distributed nodes in wireless networks. Cooperative communications has recently emerged as a new communication paradigm that enables and leverages effective resource sharing among cooperative nodes. In this paper, a general framework for lifetime extension of battery-operated devices by exploiting cooperative diversity is proposed. The framework efficiently takes advantage of different locations and energy levels among distributed nodes. First, a lifetime maximization problem via cooperative nodes is considered and performance analysis for M-ary PSK modulation is provided. With an objective to maximize the minimum device lifetime under a constraint on bit-error-rate performance, the optimization problem determines which nodes should cooperate and how much power should be allocated for cooperation. Since the formulated problem is NP hard, a closed-form solution for a two-node network is derived to obtain some insights. Based on the two-node solution, a fast suboptimal algorithm is developed for multi-node scenarios. Moreover, the device lifetime is further improved by a deployment of cooperative relays in order to help forward information of the distributed nodes in the network. Optimum location and power allocation for each cooperative relay are determined with an aim to maximize the minimum device lifetime. A suboptimal algorithm is developed to solve the problem with multiple cooperative relays and cooperative nodes. Simulation results show that the minimum device lifetime of the network with cooperative nodes improves 2 times longer than the lifetime of the non-cooperative network. In addition, deploying a cooperative relay in a proper location leads up to 12 times longer lifetime than that of the non-cooperative network.

In this letter, a novel approach for outage probability analysis of the multinode amplify-and-forward relay network is provided. It is shown that the harmonic mean of two exponential random variables can be approximated, at high signal-to-noise ratio (SNR), to be an exponential random variable. The single relay case considered before is a special case of our analysis. Based on that approximation, an outage probability bound is derived which proves to be tight at high SNR. Based on the derived outage probability bound, optimal power allocation is studied. Simulation results show a performance improvement, in terms of symbol error rate, of the optimal power allocation compared to the equal power-allocation scheme

The study of joint source-channel Coding (JSCC) systems faces one major challenge in obtaining an analytical expression for the function that links end-to-end distortion with channel signal-to-noise ratio, the D-SNR curve. In this paper, for certain multimedia systems using practical source and channel codes in a JSCC bit rate allocation design, the D-SNR curve is shown to be well approximated by a set of carefully selected points where the relative contribution of channel errors to end-to-end distortion is small. This approach has the potential advantage that it could be applied to represent the performance of many practical systems using JSCC bit rate allocation for which it is shown that the D-SNR function is approximately linear in log-log scales. A unified framework for the modeling, analysis, and performance measurement of these systems is proposed by considering a view of diversity more general than its usual interpretation. This view extends that of diversity to include redundant information so that coding and diversity gain are still used to characterize performance. Furthermore, the proposed approach is applied to study issues arising from using practical source and channel codes, including the effects on the performance of channel codes of different strength or source codes with different compression efficiency.

Although a contributory group key agreement is a promising solution to achieve access control in collaborative and dynamic group applications, the existing schemes have not achieved the performance lower bound in terms of time, communication, and computation costs. In this paper, we propose a contributory group key agreement that achieves the performance lower bound by utilizing a novel logical key tree structure, called PFMH, and the concept of phantom user position. In particular, the proposed scheme only needsO(1) rounds of the two-party Diffie-Hellman (DH) upon any single-user join event andO(logn) rounds of the two-party DH upon any single-user leave event. Both the theoretical bound analysis and simulation show that the proposed scheme achieves a lower rekeying cost than the existing tree-based contributory group key agreement schemes.

In secure group-oriented applications, key management schemes are employed to distribute and update keys such that unauthorized parties cannot access group communications. Key management, however, can disclose information about the dynamics of group membership, such as the group size and the number of joining and departing users. This is a threat to applications with confidential group membership information. This paper investigates techniques that can stealthily acquire group dynamic information from key management. We show that insiders and outsiders can successfully obtain group membership information by exploiting key establishment and key updating procedures in many popular key management schemes. Particularly, we develop three attack methods targeting tree-based centralized key management schemes. Further, we propose a defense technique utilizing batch rekeying and phantom users, and derive performance criteria that describe security level of the proposed scheme using mutual information. The proposed defense scheme is evaluated based on the data from MBone multicast sessions. We also provide a brief analysis on the disclosure of group dynamic information in contributory key management schemes.

Rapid technology development and the widespread use of visual sensors have led to a number of new problems related to protecting intellectual property rights, handling patent infringements, authenticating acquisition sources, and identifying content manipulations. This paper introduces nonintrusive component forensics as a new methodology for the forensic analysis of visual sensing information, aiming to identify the algorithms and parameters employed inside various processing modules of a digital device by only using the device output data without breaking the device apart. We propose techniques to estimate the algorithms and parameters employed by important camera components, such as color filter array and color interpolation modules. The estimated interpolation coefficients provide useful features to construct an efficient camera identifier to determine the brand and model from which an image was captured. The results obtained from such component analysis are also useful to examine the similarities between the technologies employed by different camera models to identify potential infringement/licensing and to facilitate studies on technology evolution

In cooperative ad-hoc networks, nodes belong to the same authority and pursue the common goals, and will usually unconditionally help each other. Consequently, without necessary countermeasures, such networks are extremely vulnerable to insider attacks, especially under noise and imperfect monitoring. In this paper, we present a game theoretic analysis of securing cooperative ad-hoc networks against insider attacks in the presence of noise and imperfect monitoring. By focusing on the most basic networking function, namely routing and packet forwarding, we model the interactions between good nodes and insider attackers as secure routing and packet forwarding games. The worst case scenarios are studied where initially good nodes do not know who the attackers are while insider attackers know who are good. The optimal defense strategies have been devised in the sense that no other strategies can further increase the good nodes' payoff under attacks. Meanwhile, the optimal attacking strategies and the maximum possible damage that can be caused by attackers have been discussed. Extensive simulation studies have also been conducted to evaluate the effectiveness of the proposed strategies

In ad-hoc networks, nodes need to cooperatively forward packets for each other. Without necessary countermeasures, such networks are extremely vulnerable to injecting traffic attacks, especially those launched by insider attackers. Injecting an overwhelming amount of traffic into the network can easily cause network congestion and decrease the network lifetime. In this paper, we focus on those injecting traffic attacks launched by insider attackers. After investigating the possible types of injecting traffic attacks, we have proposed two sets of defense mechanisms to combat such attacks. The first set of defense mechanisms is fully distributed, while the second is centralized with decentralized implementation. The detection performance of the proposed mechanisms has also been formally analyzed. Both theoretical analysis and experimental studies have demonstrated that under the proposed defense mechanisms, there is almost no gain to launch injecting traffic attacks from the attacker's point of view

In this paper, a novel cognitive multiple-access strategy in the presence of a cooperating relay is proposed. Exploiting an important phenomenon in wireless networks, source burstiness, the cognitive relay utilizes the periods of silence of the terminals to enable cooperation. Therefore, no extra channel resources are allocated for cooperation and the system encounters no bandwidth losses. Two protocols are developed to implement the proposed multiple-access strategy. The maximum stable throughput region and the delay performance of the proposed protocols are characterized. The results reveal that the proposed protocols provide significant performance gains over conventional relaying strategies such as selection and incremental relaying, specially at high spectral efficiency regimes. The rationale is that the lossless bandwidth property of the proposed protocols results in a graceful degradation in the maximum stable throughput with increasing the required rate of communication. On the other hand, conventional relaying strategies suffer from catastrophic performance degradation because of their inherent bandwidth inefficiency that results from allocating specific channel resources for cooperation at the relay. The analysis reveals that the throughput region of the proposed strategy is a subset of its maximum stable throughput region, which is different from random access, where both regions are conjectured to be identical.

In autonomous mobile ad hoc networks, nodes belong to different authorities and pursue different goals; therefore, cooperation among them cannot be taken for granted. Meanwhile, some nodes may be malicious, whose objective is to damage the network. In this paper, we present a joint analysis of cooperation stimulation and security in autonomous mobile ad hoc networks under a game theoretic framework. We first investigate a simple yet illuminating two-player packet forwarding game and derive the optimal and cheat-proof packet forwarding strategies. We then investigate the secure routing and packet forwarding game for autonomous ad hoc networks in noisy and hostile environments and derive a set of reputation-based cheat-proof and attack-resistant cooperation stimulation strategies. When analyzing the cooperation strategies, besides Nash equilibrium, other optimality criteria, such as Pareto optimality, subgame perfection, fairness, and cheat-proofing, have also been considered. Both analysis and simulation studies have shown that the proposed strategies can effectively stimulate cooperation among selfish nodes in autonomous mobile ad hoc networks under noise and attacks, and the damage that can be caused by attackers is bounded and limited

Future wireless networks will support the growing demands of heterogeneous and delay sensitive applications. In this paper, a users' satisfaction factor (USF) is defined to quantify quality of service (QoS) for different types of services such as voice, data, and multimedia, as well as for different delay constraints. This USF not only predicts the final delivered QoS during transmission, but also take advantages of the fact that different packets can be decoded at different time in the receivers. Based on this USF, four types of scheduling schemes considering tradeoffs between system performance and individual fairness are proposed. These schemes explore the time, channel, and multi-user diversity to guarantee quality of service and enhance the network performance. From the simulation results, the proposed scheduling schemes achieve different tradeoffs between individual fairness and high system performance for the heterogeneous and delay sensitive applications, compared with the weighted round-robin and the modified proportional fairness scheduling schemes

Emerging ultra-wideband (UWB) technology offers a great potential for the design of high-speed short-range communications. However, in order for a UWB device to coexist with other devices, the transmitted power level of UWB is strictly limited by the FCC spectral mask. Such limitation poses a significant design challenge to any UWB system. An efficient management of the limited power is thus a key feature to fully exploit the advantages of UWB. In this paper, a cross layer multiuser multiband UWB scheme is proposed to obtain the optimal subband and power allocation strategy. Optimization criteria involve minimization of power consumption under the constraints on the packet error rate, the data rate, and the FCC limit. To ensure the system feasibility in variable channel conditions, an algorithm to jointly manage the rate assignment of UWB devices, subband allocation, and power control is proposed. A computationally inexpensive suboptimal approach is also developed to reduce the complexity of the problem, which is found to be NP hard. Simulation results under UWB channel model specified in the IEEE 802.15.3a standard show that the proposed algorithm achieves comparable performances to those of the complex optimal full search approach, and it can save up to 61% of transmit power compared to the current multiband scheme in the standard proposal. Moreover, the proposed algorithm can obtain the feasible solutions adaptively when the initial system is not feasible for the rate requirements of the users.

In this paper, we propose a new cooperative communication protocol, which achieves high bandwidth efficiency while guaranteeing full diversity order. The proposed scheme considers relay selection via the available partial channel state information (CSI) at the source and the relays. More precisely, the source determines when it needs to cooperate with one relay only among arbitrary N relays and which relay to cooperate with in case of cooperation, i.e., "When to cooperate?" and "Whom to cooperate with?". In case of cooperation, the source employs the optimal relay, which has the maximum instantaneous scaled harmonic mean function of its source-relay and relay-destination channels' gains. For the symmetric scenario, we prove that full diversity is guaranteed and that a significant increase of the bandwidth efficiency is achieved. Furthermore, we show the tradeoff between the achievable bandwidth efficiency and the corresponding error rate. Finally, the obtained analytical results are verified through computer simulations.

Dynamic spectrum allocation becomes a promising approach to increase the spectrum efficiency for wireless networks. In this paper, we consider the spectrum allocation in wireless networks with multiple selfish legacy spectrum holders and unlicensed users as multi-stage dynamic games. A dynamic pricing approach is proposed to optimize overall spectrum efficiency while keeping the participating incentives of the users based on double-auction rules. Moreover, a belief system is developed to assist selfish users to dynamically update their strategies adaptive to the network dynamics and substantially decrease the pricing overhead. The simulation results show that our proposed scheme not only approaches optimal outcomes but also has low overhead.

In this paper, we consider the design of distributed space-time codes for wireless networks. Distributed space-time coding (DSTC) can be achieved through node cooperation to emulate multiple transmit antennas. We derive the distributed space-time codes design criteria for different scenarios based on the pairwise error probability (PEP) analysis. First, we consider the decode-and-forward (DAF) protocol and prove that space-time codes, designed to achieve full diversity and maximum coding gain in the MIMO channels, will achieve full diversity but not necessarily maximizing the coding gain if used with the DAF protocol. Next, we consider the amplify-and-forward (AAF) protocol and prove that a space-time code designed to achieve full diversity and maximum coding gain in MIMO channels will achieve the same if used with the AAF protocol.

In this paper, we propose a relay-based scheme where a fixed number of relays are utilized to design a OFDM cooperative protocol. By exploiting limited feedback from the destination such that each relay is able to help forward information of multiple sources in one OFDM symbol, the proposed cooperative protocol not only achieves full diversity but also efficiently utilizes available bandwidth. A practical relay assignment scheme is also proposed to specify the pairing of sources and relays in cooperative networks. The outage probability analysis of the proposed cooperative protocol is provided, and the analytical results are validated via computer experiments. Moreover, a closed-form lower bound on the outage probability of any relay-assignment schemes is established to provide a performance benchmark of the proposed cooperative protocol. Based on the outage probability analysis, the optimum relay location for the proposed relay-assignment scheme is determined.

In this paper, we propose a Stackelberg game theoretic framework for distributive resource allocation over multiuser cooperative communication networks to improve the system performance and stimulate cooperation. Two questions of who should relay and how much power for relaying are answered, by employing a two-level game to jointly consider the benefits of source nodes as buyers and relay nodes as sellers in cooperative communication. From the derived results, the proposed game not only helps the source smartly find relays at relatively better locations but also helps the competing relays ask reasonable prices to maximize their own utilities. From the simulation results, the relays in good locations or good channel conditions can play more important roles in increasing source node's utility, so the source would like to buy power from these preferred relays. On the other hand, because of competition from other relays and selections from the source, the relays have to set proper prices to attract the source's buying so as to optimize their utility values.

This paper investigates the protocol design issue for cooperation systems in wireless communications. A tight approximate symbol error rate (SER) for such systems is derived and analyzed. Based on such analysis, an optimum power allocation scheme is proposed by optimizing the derived approximate SER subject to fixed transmission rate and total transmit power constraints. Then, a novel adaptive protocol is proposed for cooperative communications based on minimizing the asymptotic SER (i.e. in an averaging sense under high-enough SNR regimes) of such systems. This proposed adaptive protocol is able to achieve the maximum achievable diversity gain available in such systems without sacrificing any transmission rate or the total transmit power, and optimally adapts the number of cooperation partners under the changing environments. Simulation results show that the proposed adaptive protocol provides a lower SER compared with existing protocols. In addition, the proposed adaptive protocol with optimum power allocation can remarkably enhance the SER performance in comparison with the equal power allocation scheme.

This paper presents an optimum power allocation strategy for the maximum likelihood based channel estimation in the space-time coded multiple-input multiple-output systems employing a data-bearing approach for pilot-embedding. The corresponding channel estimation error, the Chernoff's upper bound on the detection error probability, and a lower bound on channel capacity of such systems are analyzed. Based on such analysis, the relationship between these two bounds are revealed, then a unified optimum power allocation scheme is proposed based on jointly optimizing both bounds subject to an acceptable channel estimation error. Simulation results indicate that the proposed power allocation scheme yields a better performance in terms of the error probability, whereas the equal power allocation scheme can be reasonably used as a suboptimum approach with an acceptable performance degradation. Furthermore, the unequal power allocation with more power constantly allocated to the data part yields a much better performance than the one with more power constantly allocated to the pilot part

This paper considers the problem of non-intrusive forensic analysis of the individual components in visual sensors and its implementation. As a new addition to the emerging area of forensic engineering, we present a framework for analyzing technologies employed inside digital cameras based on output images, and develop a set of forensic signal processing algorithms for visual sensors based on color array sensor and interpolation methods. We show through simulations that the proposed method is robust against compression and noise, and can help identify various processing components inside the camera. Such a non-intrusive forensic framework would provide useful evidence for analyzing technology infringement and evolution for visual sensors.

A crucial challenge in the implementation of a cooperative diversity protocol is how to assign source-relay pairs. In this paper, we address this problem under the knowledge of the users' spatial distribution and we propose a distributed relay-assignment algorithm for cooperative communications. In the proposed algorithm, the relay is chosen to be the nearestneighbor to the user towards the base-station (access-point). An outage analysis for the proposed scheme is provided under a random spatial distribution for the users, and an approximate expression for the outage probability is derived. Simulation results for indoor wireless local area networks (WLAN) are provided. By utilizing the proposed protocol, simulation results indicate a significant gain in coverage area over the direct transmission scheme under fairly the same bandwidth efficiency and fixed average transmitted power. A 350% increase in the coverage area can be achieved by the distributed Nearest-neighbor protocols. This coverage increase can also be translated to energy efficiency over direct transmission when fixing the total coverage area.

In this paper, a new multiple access approach is proposed that takes into account the broadcast nature of the wireless channel. The new approach employs a relay to boost the system throughput. This approach is based on a new idea in which the relay utilizes the empty time slots available in a TDMA frame. The relay stores the packets that failed transmissions previous time slots. At each time slot, the relay listens to the channel and retransmits the packet at the head of its queue if the channel is free. This will better utilize the channel resources and will introduce on-demand spatial diversity into the network. Two different protocols are proposed to implement this new multiple-access scheme. The stability criteria of the associated queueing systems are studied and analytical expressions for the maximum stable throughput are provided for the symmetrical users case. Numerical results indicate a significant increase in the maximum stable throughput by using the new multiple-access protocol over pure TDMA.

This paper studies the transmission of images encoded using an embedded source encoder over a wireless network allowing user cooperation. A dynamic programming algorithm is applied to optimally allocate to each source coded packet error protection in the form of a choice of channel code and a decision on the use of cooperation. The results show that for degraded source-destination channels, the use of user cooperation on all source coded packets significantly improves the reconstructed quality. As the source-destination channel improves the optimal policy becomes a hybrid that uses user cooperation only for the most important source packets. When the source destination channel is sufficiently good, all source packets are sent without cooperation. Thus, a test is presented to estimate the channels states when cooperation is first used.

Digital fingerprinting uniquely labels each distributed copy with user's ID and provides a proactive means to track the distribution of multimedia. Multi-user collusion is a powerful attack against digital fingerprinting, in which a group of attackers collectively mount attacks to remove the embedded identification information. To resist such multi-user collusion and support multimedia forensics, this paper investigates the side information based multimedia fingerprinting. We explore techniques to utilize side information of collusion attacks during colluder identification process, and show that the means of the detection statistics at the detector's side can significantly improve the traitor tracing capability. We also investigate how the fingerprint detector can probe such side information from the colluded copy, and our simulation results show that the proposed scheme helps the fingerprint detector achieve the optimum detection performance.

A cost effective attack against multimedia forensics is the multiuser collusion attack, in which several attackers mount attacks collectively to remove traces of the identifying fingerprints and hinder traitor tracing. An important issue in collusion is to ensure that all colluders have the same probability of being detected. While they might agree so, some selfish colluders may wish to further lower their own risk of being caught. This paper investigates this problem of "traitors within traitors", in an effort to formulate the dynamics among attackers during collusion. We consider scalable multimedia forensic systems where users receive fingerprinted copies of different quality due to network and device heterogeneity, and explore the techniques that a selfish colluder can use to minimize his/her probability of being detected. Our results show that changing the resolution of their received copies before multi-user collusion can help selfish colluders further reduce their risk, especially when the colluded copy has high resolution.

Digital images have been used in growing number of applications from law enforcement and surveillance, to medical diagnosis and consumer photography. With such widespread popularity and the presence of low-cost image editing softwares, the integrity of image content can no longer be taken for granted. In this paper, we propose a novel technique based on blind deconvolution to verify image authenticity. We consider the direct output images of a camera as authentic, and introduce algorithms to detect further processing such as tampering applied to the image. Our proposed method is based on the observation that many tampering operations can be approximated as a combination of linear and non-linear components. We model the linear part of the tampering process as a filter, and obtain its coefficients using blind deconvolution. These estimated coefficients are then used to identify possible manipulations. We demonstrate the effectiveness of the proposed image authentication technique and compare our results with existing works.

During collusion attacks against multimedia forensics, an important issue that colluders need to address is the fairness of the attack, i.e., whether all colluders take the same risk of being detected. Although they might agree so, some selfish colluders may break away from their fair-collusion agreement and process their fingerprinted copies before collusion to further lower their risk. On the other hand, to protect their own interests, other attackers may wish to detect and prevent such selfish pre-collusion processing. It is important to study this problem of traitors within traitors, formulate the dynamics among colluders and build a complete model of multi-user collusion. This paper investigates techniques that attackers can use to detect and identify selfish colluders without revealing the secrecy of any fingerprinted copies. Our simulation results show that the proposed scheme accurately identifies all selfish colluders without falsely accusing any others.

In autonomous mobile ad hoc networks (MANET) where each user is its own authority, fully cooperative behaviors, such as unconditionally forwarding packets for each other, cannot be directly assumed. The pricing mechanism is one way to provide incentives for the users to act cooperatively by rewarding some payment for cooperative behaviors. Here, we model the pricing and routing in autonomous MANETs as multi-stage dynamic games. A dynamic pricing framework is proposed to maximize the sender/receiver's payoff by considering the dynamic nature of MANETs. Meanwhile, the forwarding incentives of the relay nodes can also be maintained by providing the optimal payments based on the auction rules. The simulation results illustrate that the proposed dynamic pricing schemes have significant performance gains over the existing static pricing algorithms.

The performance of distributed networks depends on collaboration among distributed entities. To enhance security in distributed networks, such as ad hoc networks, it is important to evaluate the trustworthiness of participating entities since trust is the major driving force for collaboration. In this paper, we present a framework to quantitatively measure trust, model trust propagation, and defend trust evaluation systems against malicious attacks. In particular, we address the fundamental understanding of trust, quantitative trust metrics, mathematical properties of trust, dynamic properties of trust, and trust models. The attacks against trust evaluation are identified and defense techniques are developed. The proposed trust evaluation system is employed in ad hoc networks for securing ad hoc routing and assisting malicious node detection. The implementation is fully distributed. Simulations show that the proposed system can significantly improve network throughput as well as effectively detect malicious behaviors in ad hoc networks. Further, extensive simulations are performed to illustrate various attacks and the effectiveness of the proposed defense techniques.

In order to fully utilize the scarce spectrum resources, with the development of cognitive radio technologies, dynamic spectrum allocation becomes a promising approach to increase the efficiency of spectrum usage. In this paper, we consider the spectrum allocation in wireless networks with multiple selfish legacy spectrum holders and unlicensed users as multi-stage dynamic games. A belief-assisted dynamic pricing approach is proposed to optimize overall spectrum efficiency while keeping the participating incentives of the users based on double auction rules. Moreover, considering the budget constraints of the unlicensed users, a dynamic programming approach is further developed to optimize the spectrum allocation over time. The simulation results show that our proposed scheme not only approaches optimal outcomes with low overhead compared to general continuous double auction mechanisms, but also fully exploits the time diversity of spectrum resources when budget constraints exist

The gains of cooperative communications in wireless networks have been explored recently under the ideal assumption of negligible receiving and processing power. In sensor networks, the power spent for listening and computing can constitute a significant portion of the total consumed power, and such an overhead can reduce the gains promised by cooperation. In this paper, cooperation gains are investigated by taking into consideration such overheads in the analytical framework. The performance metric considered is the energy efficiency of the system measured by the total power required to achieve a certain quality of service requirement. The analytical and numerical results reveal very interesting threshold behavior below which direct transmission is more energy efficient, and above which cooperation provides more gains. Such a tradeoff is shown to depend on many parameters such as the relative locations of the source and destination, the values of the receive and processing powers, the application, and many other factors. Moreover, there are experimental results conducted to verify the channel model assumed in the paper

In autonomous mobile ad hoc networks (MANET) where each user is its own authority, the issue of cooperation enforcement must be solved first to enable network functioning, such as packet forwarding, which becomes very difficult under noise and imperfect monitoring. In this paper, we focus on cooperation enforcement in autonomous mobile ad hoc networks under noise and imperfect observation and study the basic packet-forwarding function using the repeated game models with imperfect information. A belief-based packet forwarding framework is proposed to obtain cooperation-enforcement strategies solely based on each node's own past actions and its private imperfect observation of other nodes' information. The simulation results illustrate that the proposed belief-based packet forwarding approach can enforce the cooperation with only a small performance degradation compared with the unconditionally cooperative outcomes when noise and imperfect observation exist

In this paper, we study the effects of model mismatch error inherently in the FFT-based channel estimation approach when considering the multipath delay profiles with non-integer delays. We propose an adaptive FFT-based channel estimator that employs the optimum number of significant taps such that the average total energy of the channels dissipating in each tap is completely captured in order to compensate the model mismatch error. Furthermore, our proposed pilot-embedded data-bearing approach is employed for joint channel estimation and data detection. Simulation results reveal that the adaptive FFT-based channel estimator is superior to the FFT-based and LS channel estimators; however, in nonquasi-static fading channels under high Doppler's shift regimes, the performances of all three channel estimators are quite close resulting from the influence of the channel mismatch error that dominates all factors causing the detection error

This paper presents a novel technique for pilot-based channel estimation and data detection by exploiting a null-space property and an orthogonality property of the data bearer and pilot matrices. The data and pilot extraction procedures can be done independently by a simple linear transformation exploiting the null-space property. The maximum-likelihood (ML) receiver employed for data detection and the unconstrained-ML estimator employed for channel estimation can be designed separately by using the orthogonality property. In addition, the linear minimum mean-squared error (LMMSE) channel estimator is also proposed to improve the performance of channel estimation. The simulation results show that, among three data bearer and pilot structures including time-multiplexing (TM)-based, ST-block-code (STBC)-based, and code-multiplexing (CM)-based structures, the CM-based structure shows superior performance over the TM-based and the STBC-based structures in term of the probability of detection error, e.g. BER, for nonquasi-static flat Rayleigh fading channels, while the performances of these three structures are quite close for quasi-static flat Rayleigh fading channels.

Unmanned air vehicles (UAVs) can provide important communication advantages to ground-based wireless ad hoc networks. In this paper, the location and movement of UAVs are optimized such that the network connectivity can be improved. Two types of network connectivity are quantified: global message connectivity and worst-case connectivity. The problems of UAV deployment and movement are formulated for these applications. The optimization problems are NP hard and some heuristic adaptive schemes are proposed in order to yield simple solutions. From the simulation results, by deploying only a single UAV, the global message network connectivity and worst-case network connectivity can be improved by up to 109% and 60%, respectively.

In this paper, cooperative sensor protocol and relay deployment in wireless sensor networks (WSNs) is proposed to improve the network lifetime. First, cooperative transmission is employed among sensor nodes. With an objective of maximizing the network lifetime under a constraint on bit-error-rate performance, we determine which sensors should cooperate and how much power to allocate for cooperation. A closed form solution is provided for a two-sensor WSN. Based on the obtained two-sensor solution, a fast suboptimal algorithm is developed for multiple sensor case. Moreover, the network lifetime is further improved by deploying cooperative relays in the network to help sensors transmit their information. The network lifetime is maximized by choosing the optimum location for each relay and optimally allocating the power that the relay helps each sensor. A suboptimal algorithm is proposed for the WSN with multiple relays. Simulation results show that the network lifetime of the proposed WSN with cooperative sensor employment improves 2.3 times compared with the non-cooperative WSN. In addition, deploying a cooperative relay in the proper location improves network lifetime up to 3 times of that for the non-cooperative WSN.

In this paper, we propose a multi-node differential amplify-and-forward scheme for cooperative communications. The proposed scheme efficiently combines signals from the direct and multiple relay links to improve communication reliability. Bit-error-rate (BER) analysis for M-ary differential phase shift keying is provided as performance measure of the proposed scheme, and optimum power allocation is investigated. While the exact BER formulation of the proposed scheme is not available currently, we provide as a performance benchmark a tight BER formulation based on optimum combining weights. A simple BER upper bound and a tight BER approximation show that the proposed scheme can achieve the full diversity which equals to the number of cooperating nodes. We further provide simple BER approximation in order to provide analytical result on power allocation scheme. A closed-form optimum power allocation based on the tight simple BER approximation is obtained for single-relay scenario. An approximate optimum power allocation scheme is provided for multi-relay systems. The provided BER formulations are shown to closely match to the simulation results. Moreover, simulation results show that the optimum power allocation scheme achieves up to 2 dB performance gain over the equal power allocation scheme.

This paper proposes a threshold-based differential decode-and-forward cooperative scheme that efficiently exploits the cooperative relay channels via the use of a pre-determined decision threshold. In the proposed scheme, the source information is forwarded by the relay only if it is correctly decoded. The properly-designed threshold enables the destination to decide whether the received signal from the relay contains information such that the received signals from the source and the relay can be efficiently combined and jointly decoded. The bit error rate (BER) analysis of the proposed scheme is analyzed in case of differential M-ary phase shift keying signals. A tight BER approximation is established, and BER upper bound and lower bound are determined. Based on the tight BER approximation, joint optimum decision threshold and power allocation is numerically evaluated. Both analytical and simulation results reveal that the decision threshold and the power allocation depend on channel link qualities. Interestingly, when the link quality between the relay and the destination is very good, the effect of the threshold dominates the effect of the power allocation at high signal-to-noise ratio. Extensive simulation results are provided to validate the merit of the proposed scheme and confirm the theoretical analysis

In self-organized mobile ad hoc networks (MANET) where each user is its own authority, fully cooperative behaviors, such as unconditionally forwarding packets for each other, cannot be directly assumed. In this paper, we focus on cooperation enforcement in the self-organized mobile ad hoc networks with noise and imperfect observation and study the basic packet-forwarding function using the repeated game models with imperfect information. A belief-based packet forwarding framework is proposed to obtain cooperation-enforcement strategies solely based on each node's own past actions and its private imperfect observation of other nodes' information. The simulation results illustrate that the proposed belief-based packet forwarding approach can enforce the cooperation with only a small performance degradation compared to the unconditionally cooperative outcomes in the ad hoc networks with noise and imperfect observation.

Cooperative diversity exploits the broadcast nature of wireless channels by allowing users to relay information for each other so as to create multiple signal paths. This technique could be used together with joint source-channel bit rate allocation to transmit conversational multimedia traffic. This paper studies, following a methodology that avoids the use of high or low SNR approximations, the interaction between joint source-channel bit rate allocation with AF and DF cooperation. Furthermore, the effect of QoS constraints on performance is studied. In addition to the advantages of user-cooperation at medium and low source-destination channel signal-to-noise ratio, the results in this paper point at the fact that significant extra performance gains could be obtained by careful selection of the relay node. Guidelines for choosing the relay are also discussed.

In this paper, we study the impact of cooperative routing for maximizing the network lifetime in sensor network applications. We assume nodes in the network are equipped with a single omnidirectional antenna and they perform cooperative transmission to achieve transmit diversity. We propose a joint cooperative transmission and energy aware routing algorithm to prolong the network lifetime. In contrast to the previous works, our approach uses the maximum lifetime power allocation, instead of minimum power allocation. Using the maximum lifetime power allocation, the cooperative nodes allocate their transmit power according to the channel condition and the residual energy in the nodes. Our proposed scheme combines the maximum lifetime power allocation and the energy aware routing to maximize the network lifetime. We study the performance of the cooperative routing in terms of network lifetime (defined as the time until the first node dies) and the total delivered packets before the first node dies. We demonstrate that the proposed solution achieves 1 ~ 3.5 and 1 ~ 2 times longer network lifetime and more total delivered packets compared to noncooperative routing, when it is used with MTE and FA algorithms, respectively. Furthermore, the maximum lifetime power allocation achieves 1 ~ 2 times longer lifetime, compared to minimum power allocation in MTE and FA routing schemes. We also provide distributed implementation of the proposed algorithm.

Multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) is one prominent communication system for realizing high speed data transmission services. One critical issue for such systems is channel estimation. In this paper, we first develop a pilot-embedded data-bearing (PEDB) approach for joint channel estimation and data detection. Then we propose a least square (LS) FFT-based channel estimator by employing the concept of FFT-based channel estimation to improve the performance of the PEDB-LS channel estimation. Also, the effects of model mismatch error when considering non-integer multipath delay profiles, and its performance are investigated. We further propose an adaptive LS FFT-based channel estimator that employs the optimum number of significant taps. Simulation results reveal that the adaptive LS FFT-based estimator provides superior performance under quasi-static channels or low Doppler's shift regimes

In this paper, we propose a bandwidth efficient cooperation protocol to extend coverage area in cellular networks. An important question, however, in implementing any cooperation protocol is how to assign cooperating partners. We introduce a BS-controlled relay-assignment algorithm to assign cooperating users. First, we derive a lower bound on the outage performance of any practical relay assignment protocol. The lower bound can be achieved by a hypothetical Genie-aided protocol that places a relay at the optimal theoretical location for every source in the network. Then we describe and analyze the performance of the base-station/access-point (BS/AP) controlled protocol. The BS/AP-controlled protocol can be implemented at the BS or the AP, and it tries to emulate the Genie-aided protocol. Simulation results for rural cellular systems are provided. By utilizing the proposed protocol, simulation results indicate a significant gain in coverage area over the direct transmission scheme under fairly the same bandwidth efficiency and fixed average transmitted power. For the rural cellular system case, coverage extension of about 250% can be achieved by the BS/AP-controlled protocol.

In this paper, we consider the outage probability analysis of multi-node amplify-and-forward relay network with N relay nodes helping the source. We consider a system in which each relay node amplifies the source signal only. We obtain an approximation for the outage probability which is tight at high signal-to-noise ratio (SNR). This tight outage approximation shows that the system can achieve a maximum diversity of order N+1. For the case of N = 1, our approach gives the same result obtained previously by Laneman et al. for the single relay scenario.

This paper proposes a threshold-selection relaying scheme, in which each relay decides whether to forward the source information by comparing the received signal power with a decision threshold. The challenging problem is to design the threshold such that the relay is able to forward only correctly decoded information. In this work, bit-error-rate (BER) performance analysis is provided for the proposed scheme with BPSK signals. We develop a BER upper bound which is tight over the entire range of signal-to-noise ratio values. Based on the established BER formulation, we jointly determine optimum decision threshold and power allocation. An interesting result is that the effect of optimum threshold dominates that of optimum power allocation, especially when the relay is close to the destination. For example, in case of equal power allocation, the proposed scheme with optimum threshold yields 10 dB performance improvement over the fixed relaying scheme without Q threshold at a BER of 10-2. If both the power allocation and the threshold are jointly optimized, then the performance of the proposed scheme can be further improved by 2 dB.

Due to limitation on transmitted power level, any UWB system faces major design challenges in achieving wide coverage while assuring an adequate system performance. In this paper, an employment of cooperative communications in UWB is proposed to enhance the performance and the coverage of UWB by exploiting the broadcasting nature of wireless channels and the cooperation among UWB devices. Symbol-error-rate (SER) performance analysis and optimum power allocation are provided for cooperative UWB multiband OFDM systems with decode-and-forward cooperative protocol. To capture the multipath-clustering phenomenon of UWB channels, the SER performance is characterized in terms of cluster and ray arrival rates. An optimum power allocation is determined based on two different objectives, namely minimizing the overall transmitted power and maximizing the system coverage. Furthermore, an improved cooperative UWB multiband OFDM scheme is proposed to take advantage of unoccupied subbands. Simulation results are shown to validate the theoretical analysis.

This paper provides a novel performance analysis for UWB systems that successfully captures the unique multipath-rich property and multipath-clustering phenomenon of UWB channels. Using the Saleh-Valenzuela model, we characterize the pairwise error probability and the outage probability for UWB systems employing multiband OFDM based on the cluster arrival rate, the ray arrival rate within a cluster, and the cluster and ray decay factors. Furthermore, the effect of random-clustering phenomenon on the performance of UWB-MIMO systems is analyzed. The theoretical results reveal that regardless of the clustering behavior of UWB channels, the diversity gain can be improved by increasing the number of jointly encoded subcarriers, the number of jointly encoded OFDM symbols, or the number of antennas. The coding gain on the other hand, depends heavily on the cluster-arriving channels. Extensive simulation results are provided to support the theoretical analysis.

In this paper, we propose an OFDM cooperative protocol that not only achieves full diversity but also efficiently utilizes available bandwidth. The proposed protocol exploits limited feedback from the destination terminal (central node) such that each relay is able to help forward information of multiple sources in one OFDM symbol. To specify how relay-source pairs should be assigned, we propose two practical relay-assignment schemes, including fixed-location scheme in which the relays are fixed at optimum locations, and centralized-control scheme in which the relays are assigned by the central node. We provide outage probability analysis of the proposed protocol in wireless indoor environment. Moreover, a lower bound on the outage probability of any relay-assignment schemes is established, and the performance of the proposed relay-assignment schemes is analyzed. We also investigate the application of the proposed protocol to enhance the performance of UWB systems. In UWB wireless indoor scenarios, both theoretical and simulation results show that the proposed cooperative protocol can achieve 75% power saving and 200% coverage extension compared to the noncooperative UWB system proposed in the IEEE 802.15.3a standard.

MOTIVATION: Identification of genes expressed in a cell-cycle-specific periodical manner is of great interest to understand cyclic systems which play a critical role in many biological processes. However, identification of cell-cycle regulated genes by raw microarray gene expression data directly is complicated by the factor of synchronization loss, thus remains a challenging problem. Decomposing the expression measurements and extracting synchronized expression will allow to better represent the single-cell behavior and improve the accuracy in identifying periodically expressed genes. RESULTS: In this paper, we propose a resynchronization-based algorithm for identifying cell-cycle-related genes. We introduce a synchronization loss model by modeling the gene expression measurements as a superposition of different cell populations growing at different rates. The underlying expression profile is then reconstructed through resynchronization and is further fitted to the measurements in order to identify periodically expressed genes. Results from both simulations and real microarray data show that the proposed scheme is promising for identifying cyclic genes and revealing underlying gene expression profiles. AVAILABILITY: Contact the authors. SUPPLEMENTARY INFORMATION: Supplementary data are available at: http://dsplab.eng.umd.edu/~genomics/syn/

This paper provides a novel performance analysis for ultra-wideband (UWB) systems that successfully captures the unique multipath-rich property and multipath-clustering phenomenon of UWB channels. Using the Saleh-Valenzuela model, we characterize pairwise error probability and outage probability for UWB systems employing multiband OFDM based on the cluster arrival rate, the ray arrival rate within a cluster, and the cluster and ray decay factors. Furthermore, an approximation technique is established, which allows us to obtain closed-form performance formulations that provide insightful understanding of the effect of channel characteristics on the performances of UWB systems. Finally, we characterize the effect of random-clustering phenomenon on the performance of UWB-multiple-input-multiple-output systems. The theoretical results reveal that regardless of the clustering behavior of UWB channels, the diversity gain can be improved by increasing the number of jointly encoded subcarriers, the number of jointly encoded orthogonal frequency-division multiplexing symbols, or the number of antennas. The coding gain on the other hand, depends heavily on the cluster-arriving channels. Extensive simulation results are provided to support the theoretical analysis.

The performance of ad hoc networks depends on cooperation and trust among distributed nodes. To enhance security in ad hoc networks, it is important to evaluate trustworthiness of other nodes without centralized authorities. In this paper, we present an information theoretic framework to quantitatively measure trust and model trust propagation in ad hoc networks. In the proposed framework, trust is a measure of uncertainty with its value represented by entropy. We develop four Axioms that address the basic understanding of trust and the rules for trust propagation. Based on these axioms, we present two trust models: entropy-based model and probability-based model, which satisfy all the axioms. Techniques of trust establishment and trust update are presented to obtain trust values from observation. The proposed trust evaluation method and trust models are employed in ad hoc networks for secure ad hoc routing and malicious node detection. A distributed scheme is designed to acquire, maintain, and update trust records associated with the behaviors of nodes' forwarding packets and the behaviors of making recommendations about other nodes. Simulations show that the proposed trust evaluation system can significantly improve the network throughput as well as effectively detect malicious behaviors in ad hoc networks.

In downlink of code-division multiple-access (CDMA) networks, the maximal number of real-time calls can be increased by smoothly increasing the end-to-end distortions. In this paper, a cross-layer optimization system is developed to control each user's distortion by adapting source coding rates, channel coding rates, and transmit powers. In addition, the channel-induced distortion is controlled to be only a small proportion of the total end-to-end distortion, such that the subjective quality of the received signal is high. The formulated problem is to reduce the overall end-to-end distortion in downlink single-cell systems, under the constraints of users' maximal acceptable distortions and maximal total transmit power from the base station. To solve this problem, a near-optimal algorithm is constructed to allocate resources. A performance upper bound is developed and compared with the performance of the proposed algorithm. A dynamic system considering speech activities and different offered loads is also analyzed. From the simulation results, the proposed algorithm significantly reduces distortion and the necessary maximal transmit power when the number of users is large, compared with the traditional voice over CDMA schemes.

In this paper, the effect of a general spatial and temporal fading correlation structure on the performance of coded multiple-input multiple-output (MIMO)-orthogonal frequency-division multiplexing (OFDM) systems is studied. The analysis handles an arbitrary joint transmit-receive spatial correlation model, including the non-Kronecker model. An upper bound on the maximum achievable diversity order for frequency-selective MIMO-OFDM systems with general temporal and spatial correlation is derived. Furthermore, a space-time-frequency code design that can achieve the upper bound for any arbitrarily correlated channel scenario is provided. The general framework of the analysis includes space-frequency (SF)-coded systems as a special case. For the SF-coded MIMO-OFDM system, it is shown that any SF code designed to achieve full diversity in the independent fading channel can achieve full diversity in an arbitrary spatially correlated channel. The derived analytical results are consistent with those in the existing literature for special correlation structures. Extensive simulation results are provided to confirm the theoretical analysis.

In this paper, we propose a framework to transmit multiple scalable video programs over downlink multiuser orthogonal frequency division multiplex (OFDM) networks in real time. The framework explores the scalability of the video codec and multidimensional diversity of multiuser OFDM systems to achieve the optimal service objectives subject to constraints on delay and limited system resources. We consider two essential service objectives, namely, the fairness and efficiency. Fairness concerns the video quality deviation among users who subscribe the same quality of service, and efficiency relates to how to attain the highest overall video quality using the available system resources. We formulate the fairness problem as minimizing the maximal end-to-end distortion received among all users and the efficiency problem as minimizing total end-to-end distortion of all users. Fast suboptimal algorithms are proposed to solve the above two optimization problems. The simulation results demonstrated that the proposed fairness algorithm outperforms a time division multiple (TDM) algorithm by 0.5 ~ 3 dB in terms of the worst received video quality among all users. In addition, the proposed framework can achieve a desired tradeoff between fairness and efficiency. For achieving the same average video quality among all users, the proposed framework can provide fairer video quality with 1 ~ 1.8 dB lower PSNR deviation than a TDM algorithm

Multimedia security systems involve many users with different objectives and users influence each other's performance. To have a better understanding of multimedia security systems and offer stronger protection of multimedia, behavior forensics formulate the dynamics among users and investigate how they interact with and respond to each other. This paper analyzes the behavior forensics in multimedia fingerprinting and formulates the dynamics among attackers during multi-user collusion. In particular, this paper focuses on how colluders achieve the fair play of collusion and guarantee that all attackers share the same risk (i.e., the probability of being detected). We first analyze how to distribute the risk evenly among colluders when they receive fingerprinted copies of scalable resolutions due to network and device heterogeneity. We show that generating a colluded copy of higher resolution puts more severe constraints on achieving fairness. We then analyze the effectiveness of fair collusion. Our results indicate that the attackers take a larger risk of being captured when the colluded copy has higher resolution, and they have to take this tradeoff into consideration during collusion. Finally, we analyze the collusion resistance of the scalable fingerprinting systems in various scenarios with different system requirements, and evaluate the maximum number of colluders that the fingerprinting systems can withstand

Multimedia security systems have many users with different objectives and they influence each other's performance and decisions. Behavior forensics analyzes how users with conflicting interests interact with and respond to each other. Such investigation enables a thorough understanding of multimedia security systems and helps the digital rights enforcer offer stronger protection of multimedia. This paper analyzes the dynamics among attackers during multiuser collusion. The colluders share not only the profit from the redistribution of multimedia but also the risk of being detected by the content owner, and an important issue in collusion is fairness of the attack (i.e., whether all attackers share the same risk) (e.g., whether they have the same probability of being detected). While they might agree so, some selfish colluders may break their fair-play agreement in order to further lower their risk. This paper investigates the problem of "traitors within traitors" in multimedia forensics, in an effort to formulate the dynamics among attackers and understand their behavior to minimize their own risk and protect their own interests. As the first work on the analysis of this colluder dynamics, this paper explores some possible strategies that a selfish colluder can use to minimize his or her probability of being caught. We show that processing his or her fingerprinted copy before multiuser collusion helps a selfish colluder further lower his or her risk, especially when the colluded copy has high resolution and good quality. This paper also investigates the optimal precollusion processing strategies for selfish colluders to minimize their risk under the quality constraints

Digital fingerprinting is an emerging technology to protect multimedia content from illegal redistribution, where each distributed copy is labeled with unique identification information. In video streaming, huge amount of data have to be transmitted to a large number of users under stringent latency constraints, so the bandwidth-efficient distribution of uniquely fingerprinted copies is crucial. This paper investigates the secure multicast of anticollusion fingerprinted video in streaming applications and analyzes their performance. We first propose a general fingerprint multicast scheme that can be used with most spread spectrum embedding-based multimedia fingerprinting systems. To further improve the bandwidth efficiency, we explore the special structure of the fingerprint design and propose a joint fingerprint design and distribution scheme. From our simulations, the two proposed schemes can reduce the bandwidth requirement by 48% to 87%, depending on the number of users, the characteristics of video sequences, and the network and computation constraints. We also show that under the constraint that all colluders have the same probability of detection, the embedded fingerprints in the two schemes have approximately the same collusion resistance. Finally, we propose a fingerprint drift compensation scheme to improve the quality of the reconstructed sequences at the decoder's side without introducing extra communication overhead.

Transmitting multiple real-time encoded videos to multiple users over wireless cellular networks is a key driving force for developing broadband technology. We propose a new framework to transmit multiple users' video programs encoded by MPEG-4 FGS codec over downlink multicode CDMA networks in real time. The proposed framework jointly manages the rate adaptation of source and channel coding, CDMA code allocation, and power control. Subject to the limited system resources, such as the number of pseudo-random codes and the maximal power for CDMA transmission, we develop an adaptive scheme of distortion management to ensure baseline video quality for each user and further reduce the overall distortion received by all users. To efficiently utilize system resources, the proposed scheme maintains a balanced ratio between the power and code usages. We also investigate three special scenarios where demand, power, or code is limited, respectively. Compared with existing methods in the literature, the proposed algorithm can reduce the overall system's distortion by 14% to 26%. In the demand-limited case and the code-limited but power-unlimited case, the proposed scheme achieves the optimal solutions. In the power-limited but code-unlimited case, the proposed scheme has a performance very close to a performance upper bound

In this paper, we propose a differential encoding and decoding scheme for multiband UWB systems. The proposed scheme incorporates frequency-domain differential en/decoding with the hopping multiband OFDM modulation. To capture the effect of multipath-rich clustering property of UWB channels, we characterize the pairwise error probability performance of the proposed scheme in terms of cluster and ray arrival rates. It turns out that the diversity advantage does not strongly depend on the random-clustering of UWB channels, and we can achieve the same diversity gain in different channel environments. However, the system performance relies on the clustering behavior through the coding gain. Simulation results show that the proposed differential scheme achieves good performance in the short-range line-of-sight scenarios. In addition, the jointly encoded differential multiband UWB scheme is able to yield superior performance to the uncoded coherent multiband UWB system at high SNR

In this paper we propose a new cooperative protocol, which takes into consideration the partial channel state information (CSI) available at the source. With such protocol a significant improvement in the transmission rate can be achieved in decode-and-forward cooperative transmission, while guaranteeing full diversity order. We derive closed-form expressions for the transmission rate and the symbol error rate (SER) for the M-PSK and the M-QAM signalling. Moreover, we consider two optimization metrics in the protocol design to enhance the system performance; the first is based on minimizing the SER only, while the second is based on minimizing a joint function of both the SER and the transmission rate. Finally, the obtained analytical results are verified through computer simulations

In self-organized mobile ad hoc networks (MANET) where each user is its own authority, fully cooperative behaviors, such as unconditionally forwarding packets for each other, cannot be directly assumed. The pricing mechanism is one way to provide incentives for the users to act cooperatively by rewarding some payment for cooperative behaviors. In this paper, we model the pricing and routing in self-organized MANETs as multistage dynamic games. A dynamic pricing framework is proposed to maximize the sender/receiver's payoff by considering the dynamic nature of MANETs, meanwhile, keeping the forwarding incentives of the relay nodes by providing the optimal payments based on the auction rules. The simulation results illustrate that the proposed dynamic pricing schemes have significant performance gains over the existing static pricing algorithms.

Cooperative diversity exploits the broadcast nature of wireless channels by allowing users to relay information for each other so as to create multiple signal paths. This paper analyzes what is the best strategy from the viewpoint of a resource allocation protocol, to match source coding with cooperation diversity for conversational multimedia communications by studying the distortion performance for different schemes. The results show that the best performance is obtained when all layers of a layered-coded source are sent with user cooperation (using decode-and-forward in most cases) if the source-destination channel is bad, and with no user cooperation, if the source-destination channel is good. The results also show that the gains from cooperative diversity outweigh the loss due to the sacrifice in overall bandwidth and that cooperation performance is sensitive to the proportion of communication capacity allocated for cooperation.

In this paper, we consider the energy-aware routing algorithm that explicitly takes into account the connectivity of the sensor networks. In typical sensor network deployment, some nodes may be more important than other nodes because the death of these nodes cause the network disintegration. The network disintegration causes early termination of information delivery. To overcome this problem, we propose a routing algorithm called keep connect algorithm that explicitly considers the connectivity of the network while making the routing decision. The algorithm can be used along with the existing routing algorithms. When doing the routing decision, the keep connect algorithm embeds the importance of the nodes in the routing cost. The importance of a node is quantified by how severe the remaining network becomes disconnected/disintegrated when that particular node dies. In particular, the importance of a node is characterized by the algebraic connectivity of the remaining graph. Compared to the existing routing algorithms, the proposed method achieves up to two times improvement in terms of the network lifetime and the number of successfully delivered packets in the special grid network. In random network, the proposed algorithm achieves 20% improvement of network lifetime, 30% less energy per packet, and 33% more successfully delivered packets.

It is of great importance for service providers to improve the coverage area without cost of more infrastructure in wireless networks especially in rural areas. Recently, cooperative communication has brought a new communication paradigm. In this paper, multi-node cooperative resource allocation is studied to schedule the transmission time slots so as to maximize the network overall rate (or the coverage area) under the quality of services constraints. To optimize the performance, first, the performance of the multi-node cooperative transmission is analyzed. Then a protocol is constructed between the mobiles and the base station for the resource allocation. From the simulation results, the proposed scheme can increase the user's rate by 5% for large cell sizes and improve the coverage by 180%, compared with the traditional scheme without cooperative transmission

In this paper, a cluster-based cooperative communication scheme is introduced in which each cluster acts as a virtual node with multiple antennas. The presented model considers the effects of random deployment of nodes and hence their random distribution across the network. Taking these effects into account, a pairwise-error probability analysis for a generic space-time code structure is provided. The analysis reveals that the diversity and coding gain of the system depends on both the code structure as well as the nodes' distribution, and hence new code design criteria are developed. Simulation results are presented to confirm the theoretical analysis.

The performance of ad hoc networks depends on the cooperative and trust nature of the distributed nodes. To enhance security in ad hoc networks, it is important to evaluate the trustworthiness of other nodes without central authorities. In this paper, we present an information theoretic framework to quantitatively measure trust and model trust propagation in ad hoc networks. In the proposed information theoretic framework, trust is a measure of uncertainty with its value represented by entropy. We develop Axioms that address the basic rules for trust propagation. Based on these Axioms, we present two trust models: entropy-based model and probability-based model, which satisfy all the Axioms. The proposed trust evaluation method and trust models are employed in ad hoc networks for secure ad hoc routing and malicious node detection. Simulations show that the proposed framework can significantly improve network throughput as well as effectively detect malicious behaviors in ad hoc networks.

In this paper we investigate how to defend against injecting traffic attacks in cooperative ad hoc networks. By injecting an overwhelming amount of packets into the network, the attackers can easily consume valuable network resources and reduce nodes' lifetime. Since in cooperative ad hoc networks nodes will usually unconditionally forward packets for other nodes, such networks are extremely vulnerable to injecting traffic attacks, especially those launched by inside attackers. In this paper, the possible types of injecting traffic attackers are studied, and a set of mechanisms are proposed to protect cooperative ad hoc network against such attacks. The performance of the proposed mechanisms is analyzed, which show that from attackers' point of view, the best strategy is not to launch injecting traffic attacks. Simulation studies also confirm the theoretical analysis.

In self-organized ad hoc networks, nodes usually belong to different authorities and pursue different goals. To save their valuable resources, they tend to be selfish. Meanwhile, some nodes may be malicious whose objective is to degrade the network performance. In this paper, we present an anti-attack cooperation stimulation system for self-organized ad hoc networks to stimulate cooperation among selfish nodes and to defend against attacks. In the proposed system, the damage that can be caused by attackers is bounded, and the cooperation among selfish nodes is enforced. Simulation studies also verify the effectiveness of the proposed system. Another key property of the proposed system is that it is fully distributive, and does not require any tamper-proof hardware or central management point.

While contributory group key agreement is a promising solution to achieve access control in collaborative and dynamic group applications, the existing schemes have not achieved the performance lower bound in terms of rekeying overhead. In this paper we introduce a contributory group key agreement that achieves the performance lower bound by utilizing a novel logical key tree structure, called PFMH, and the concept of phantom user position. In particular, the proposed scheme only needs O(1) rounds of two-party Diffie-Hellman upon any single user join event and O(log n) rounds of two-party Diffie-Hellman upon any single user leave event. Both theoretical bound analysis and simulation studies show that the proposed scheme achieves much lower rekeying cost than the existing tree-based contributory group key agreement schemes.

This paper evaluates the performance of the data-bearing approach for pilot-embedding for joint data detection and channel estimation in space-time (ST) coded MIMO systems. Performance measures, such as the MMSE of channel estimation, Cramer-Rao lower bound (CRLB), and the Chernoff's bound of the estimated-channel bit error rate (BER) for ST codes, are explored to examine the proposed scheme. The power allocation problem for data and pilot parts is also addressed by optimizing the probability-of-error upper-bound (PEUB) mismatched factor subject to certain constraints. Three kinds of data bearer and pilot structures are investigated via simulations, including time-multiplexing (TM)-based, ST-block-code (STBC)-based, and code-multiplexing (CM)-based data bearer and pilot matrices. Among these three structures, the CM-based scheme provides superior detection performance over the TM-based and the STBC-based schemes for nonquasistatic flat Rayleigh fading channels, while the performances of these three structures are quite close for quasi-static flat Rayleigh fading channels.

We provide symbol-error-rate (SER) performance analysis for a multi-node wireless network employing a decode-and-forward cooperation strategy. An approximate expression for the SER of an N relay network with M-ary phase-shift-keying (M-PSK) signalling is derived at high enough signal-to-noise ratio (SNR). The approximation hinges on ignoring terms in the SER which are of order higher than (N+1) in the SNR. The validity of the derived approximate SER is justified through computer simulations for networks with different numbers of relays. The simulation results show that the approximation is tight at high SNR and that the cooperation protocol can achieve full diversity order equal to the number of cooperating terminals.

A real-time video conferencing framework is proposed for multiple conferencing pairs by jointly considering the uplink and downlink conditions within IEEE 802.11 networks. We formulate this system so as to minimize the maximal end-to-end expected distortion received by all users by selecting the PHY modes and transmission time. Compared with the strategy of individually optimizing uplink and downlink, the proposed framework outperforms by 3.67-8.65 dB for the average received PSNR among all users.

Digital fingerprinting inserts identification information in the content to track the usage of digital data and protect content security. To trace traitors for multimedia over heterogeneous networks, this paper studies scalable multimedia fingerprinting systems in which users receive fingerprinted multimedia of different quality. We investigate the cost-effective multi-user collusion on fingerprinting systems and focus on fair collusion attacks in which colluders share the same risk of being captured. In this paper, we examine the fairness constraints on collusion when attackers receive copies of different quality and analyze the performance of scalable fingerprinting systems under fair collusion attacks.

Digital fingerprinting is an important tool in multimedia forensics to trace traitors and protect multimedia content after decryption. This paper addresses the enforcement of digital rights when distributing multimedia over heterogeneous networks and studies the scalable multimedia fingerprinting systems in which users receive copies of different quality. We investigate the traitor tracing capability of such scalable fingerprinting systems, in particular, the robustness of the embedded fingerprints against multi-user collusion attacks. Under the fairness constraints on collusion that all attackers share the same risk of being captured, we analyze the maximum number of colluders that the fingerprinting systems can withstand, and our results show that multimedia fingerprints can survive collusion attacks by a few dozen colluders.

In digital fingerprinting and multimedia forensic systems, it is possible that multiple adversaries mount attacks collectively and effectively to undermine the forensic system's traitor tracing capability. During this collusion attack, an important issue that the adversaries need to address is the fairness of attack and ensuring that all colluders share the same risk of being caught. This paper studies the dynamics among attackers in enforcing the fairness of collusion and investigates the problem of traitors within traitors, in which some selfish colluders wish to minimize their own risk while still profiting from collusion. We explore the strategies that these selfish colluders can use to further lower their probability of being detected and analyze their performance. We show that by processing their fingerprinted copies before multi-user collusion, the selfish colluders can further reduce their risk at the cost of quality degradation of their fingerprinted copies.

HADOF is a set of mechanisms to protect mobile ad hoc networks against routing disruption attacks launched by inside attackers. First, each node launches a route traffic observer to monitor the behavior of each valid route in its route cache, and to collect the packet forwarding statistics submitted by the nodes on this route. Since malicious nodes may submit false reports, each node also keeps cheating records for other nodes. If a node is detected as dishonest, this node will be excluded from future routes, and the other nodes will stop forwarding packets for it. Third, each node will try to build friendship with other nodes to speed up malicious node detection. Route diversity will be explored by each to discover multiple routes to the destination, which can increase the chance of defeating malicious nodes who aim to prevent good routes from being discovered. In addition, adaptive route rediscovery will be applied to determine when new routes should be discovered. HADOF can handle various attacks and introduces little overhead to the existing protocols. Both analysis and simulation studies have confirmed the effectiveness of HADOF.

We investigate how to defend against injecting traffic attacks in cooperative mobile ad hoc networks where nodes belong to the same authority and pursue the common goals. By injecting an overwhelming amount of traffic into the network, the attackers can easily consume good nodes' valuable network resources and reduce the network's lifetime. Since in cooperative mobile ad hoc networks nodes will usually unconditionally forward packets for other nodes, such networks are extremely vulnerable to injecting traffic attacks, especially those launched by inside attackers. In this paper, the possible types of injecting traffic attackers are studied, and a set of mechanisms are proposed to protect cooperative mobile ad hoc network against such attacks. The performance of the proposed mechanisms is analyzed. Both theoretical and experimental bounds are derived, which show that from attackers' point of view, the best strategy is not to launch injecting traffic attacks. Simulation studies have also verified the theoretical analysis.

In self-organized mobile ad hoc networks, nodes belong to different authorities and pursue different goals, so they tend to be selfish, that is, they try to maximize the benefits they can get from the network, but are not willing to forward packets for the benefits of other nodes. Meanwhile, some nodes may be malicious whose objective is to degrade the network's performance and waste other nodes' valuable resource. Here we propose an Attack-Resilient Cooperation Stimulation (ARCS) system for self-organized mobile ad hoc networks to stimulate cooperation among selfish nodes and to defend against attacks. In the ARCS system, the damage that can be caused by malicious nodes is bounded, and the cooperation among selfish nodes is enforced. Both theoretical analysis and simulation studies have confirmed the effectiveness of the ARCS system. Another key property of the ARCS system lies in that it is completely self-organizing and fully distributed, and does not require any tamper-proof hardware or central management points.

Cooperative transmission has been shown to be able to greatly improve the system performance by exploring the broadcasting nature of wireless channels and cooperation among users. While most existing works concentrate on improving the peer-to-peer link quality, we focus, in this paper, on resource allocation among users such that the system performance can be improved. In this work, two important questions are answered: who should help whom among the distributively located users; and how the users should cooperate to improve the performance. To quantify the questions, a power management problem is formulated over a multiuser OFDM network to minimize the overall system transmit power under the constraint of each user's desired transmission rate. Then, we develop an algorithm to find solutions for a two user case. From the simulation results, the proposed scheme achieves up to 50% overall power saving for the two-user system.

For networks with packet forwarding, distributed control to enforce cooperation for node packet forwarding probabilities is essential to maintain the connectivity. In this paper, we propose a novel self-learning repeated game framework to optimize packet forwarding probabilities of distributed users. The framework has two major steps: first, an adaptive repeated game scheme ensures the cooperation among users for the current cooperative packet forwarding probabilities; second, a self-learning scheme tries to find better cooperation probabilities. Some special cases are analyzed to evaluate the proposed framework. From the simulation results, the proposed framework demonstrates the near optimal solutions in both symmetrical and asymmetrical networks.

In this paper, we propose a differential encoding and decoding scheme for MIMO-OFDM systems under frequency-selective fading channels. We differentially encode the signal within each OFDM symbol period. This scheme not only reduces encoding and decoding delay, but also relaxes the restriction on channel assumptions. The successful differential decoding of the proposed scheme depends on the assumption that fading channels keep constant over two OFDM symbol periods rather than multiple periods as required in previous schemes. We provide the pairwise error probability formulation, and quantify the performance criteria in terms of diversity and coding advantages. Our design criteria reveal that the existing diagonal cyclic codes can be applied to achieve full diversity with high coding gain. Performance simulations in various channel conditions show that our proposed scheme yields superior performance to the previously proposed differential schemes.

The emerging ultra-wideband (UWB) system offers a great potential for the design of high-speed short-range communications. However, UWB faces a significant challenge in achieving a low transmit power level, while assuring an adequate system performance. An efficient management of the limited power is thus a key feature to fully exploit the advantages of UWB. In this paper, a cross layer multiuser multiband UWB scheme is proposed to obtain the optimal subband and power allocation strategy. Optimization criteria involve minimization of power consumption under the constraints on the packet error rate, transmission rate, and FCC regulations. To reduce the problem complexity, which is found to be NP hard a computationally inexpensive suboptimal approach is developed. Simulation results under UWB channel model specified in the IEEE 802.15.3a standard show that the proposed algorithm achieves comparable performance to that of the complex optimal full search approach, and it can save up to 61% of transmit power compared to the multiband scheme in the standard proposal.

In this paper, we propose a general framework to analyze the performance of multiband UWB-MIMO systems regardless of specific coding schemes. A combination of space-time-frequency (STF) coding and hopping multiband OFDM modulation is also proposed to fully exploit all of the available spatial and frequency diversities, richly inherent in UWB environments. We quantify the performance merits of multiband UWB-MIMO systems in case of Nakagami-m frequency-selective fading channels. We show that the maximum achievable diversity of the proposed system is the product of the number of transmit and receive antennas, the number of multipath components, and the number of jointly encoded OFDM blocks. Interestingly, theoretical result shows that the diversity gain does not severely depend on the fading parameter m.. Finally, simulation results are presented to support the theoretical analysis.

We derive a closed-form symbol-error-rate (SER) formulation for the cooperation system with PSK and QAM signals. Moreover, two SER upper bounds are established to show the asymptotic performance of the cooperation protocol, in which one of them is tight at a high signal-to-noise ratio. Based on the SER performance analysis, we also determine the optimum power allocation for the cooperation systems. It turns out that an equal power strategy is, in general, not optimum in cooperation communications, and the optimum power allocation depends on the channel link quality. An interesting result is that, in the case that all channel links are available, the optimum power allocation does not depend on the direct link between source and destination, it depends only on the channel links related to the relay. Extensive simulations are performed to validate the theoretical results.

MOTIVATION: DNA microarray technologies make it possible to simultaneously monitor thousands of genes' expression levels. A topic of great interest is to study the different expression profiles between microarray samples from cancer patients and normal subjects, by classifying them at gene expression levels. Currently, various clustering methods have been proposed in the literature to classify cancer and normal samples based on microarray data, and they are predominantly data-driven approaches. In this paper, we propose an alternative approach, a model-driven approach, which can reveal the relationship between the global gene expression profile and the subject's health status, and thus is promising in predicting the early development of cancer. RESULTS: In this work, we propose an ensemble dependence model, aimed at exploring the group dependence relationship of gene clusters. Under the framework of hypothesis-testing, we employ genes' dependence relationship as a feature to model and classify cancer and normal samples. The proposed classification scheme is applied to several real cancer datasets, including cDNA, Affymetrix microarray and proteomic data. It is noted that the proposed method yields very promising performance. We further investigate the eigenvalue pattern of the proposed method, and we discover different patterns between cancer and normal samples. Moreover, the transition between cancer and normal patterns suggests that the eigenvalue pattern of the proposed models may have potential to predict the early stage of cancer development. In addition, we examine the effects of possible model mismatch on the proposed scheme.

We consider the problem of average throughput maximization per total consumed energy in packetized sensor communications. Our study results in a near-optimal transmission strategy that chooses the optimal modulation level and transmit power while adapting to the incoming traffic rate, buffer condition, and the channel condition. We investigate the point-to-point and multinode communication scenarios. Many solutions of the previous works require the state transition probability, which may be hard to obtain in a practical situation. Therefore, we are motivated to propose and utilize a class of learning algorithms [called reinforcement learning (RL)] to obtain the near-optimal policy in point-to-point communication and a good transmission strategy in multinode scenario. For comparison purpose, we develop the stochastic models to obtain the optimal strategy in the point-to-point communication. We show that the learned policy is close to the optimal policy. We further extend the algorithm to solve the optimization problem in a multinode scenario by independent learning. We compare the learned policy to a simple policy, where the agent chooses the highest possible modulation and selects the transmit power that achieves a predefined signal-to-interference ratio (SIR) given one particular modulation. The proposed learning algorithm achieves more than twice the throughput per energy compared with the simple policy, particularly, in high packet arrival regime. Beside the good performance, the RL algorithm results in a simple, systematic, self-organized, and distributed way to decide the transmission strategy.

In autonomous ad hoc networks, nodes usually belong to different authorities and pursue different goals. In order to maximize their own performance, nodes in such networks tend to be selfish, and are not willing to forward packets for the benefits of other nodes. Meanwhile, some nodes might behave maliciously and try to disrupt the network and waste other nodes' resources. In this paper, we present an attack-resilient cooperation stimulation (ARCS) system for autonomous ad hoc networks to stimulate cooperation among selfish nodes and defend against malicious attacks. In the ARCS system, the damage that can be caused by malicious nodes can be bounded, the cooperation among selfish nodes can be enforced, and the fairness among nodes can also be achieved. Both theoretical analysis and simulation results have confirmed the effectiveness of the ARCS system. Another key property of the ARCS system lies in that it is completely self-organizing and fully distributed, and does not require any tamper-proof hardware or central management points.

We consider the design of matrix rotation based space-time signals based on the design criterion of minimizing the union bound on block error probability. We further propose to design the signal parameters via noninteger searching to get better signals. Superior performance of our improved design over the previous design are demonstrated through numerical calculations and performance simulations. With our proposed design for two transmit antennas and one or two receive antennas, we achieve the coding gain of about 1 dB over that of the previous design.

We propose a differential amplify-and-forward (AF) transmission scheme for a two-user cooperative communications system. By efficiently combining signals from both direct and relay links, the proposed scheme provides superior performance compared to those of direct transmissions with either differential detection or coherent detection. While the exact bit-error-rate (BER) formulation of the proposed scheme is not available currently, we provide, as a performance benchmark, an exact BER formulation and its simple bounds for a case of optimum-combining cooperation system with differential M-ary phase-shift keying (DMPSK) signals. The optimum power allocation is also determined based on the provided BER formulations. We show that the proposed differential cooperative transmission scheme together with the optimum power allocation yields comparable performance to the optimum-combining scheme. Simulation results show that the proposed differential scheme with optimum power allocation yields significant performance improvement over that with an equal power allocation scheme.

In this paper, a fair scheme to allocate subcarrier, rate, and power for multiuser orthogonal frequency-division multiple-access systems is proposed. The problem is to maximize the overall system rate, under each user's maximal power and minimal rate constraints, while considering the fairness among users. The approach considers a new fairness criterion, which is a generalized proportional fairness based on Nash bargaining solutions and coalitions. First, a two-user algorithm is developed to bargain subcarrier usage between two users. Then a multiuser bargaining algorithm is developed based on optimal coalition pairs among users. The simulation results show that the proposed algorithms not only provide fair resource allocation among users, but also have a comparable overall system rate with the scheme maximizing the total rate without considering fairness. They also have much higher rates than that of the scheme with max-min fairness. Moreover, the proposed iterative fast implementation has the complexity for each iteration of only O(K/sup 2/Nlog/sub 2/N+K/sup 4/), where N is the number of subcarriers and K is the number of users.

Resource allocation is an important means to increase system performance in wireless networks. In this letter, a game-theory approach for distributed resource allocation is proposed. Observing the bilinear matrix inequality nature of resource allocation, we construct two interrelated games: a power-control game at the user level, and a throughput game at the system level, respectively, to avoid local optima. An optimal complex centralized algorithm is developed as a performance bound. The simulations show that the proposed games have near-optimal system performance.

The performance of a multiuser wireless network using orthogonal frequency-division modulation (OFDM), combined with power control and adaptive beamforming for uplink transmission is presented here. A network-wide adaptive power control algorithm is used to achieve the desired signal-to-interference-and-noise-ratio at each OFDM subcarrier and increase the power efficiency of the network. As a result, we can achieve a better overall error probability for a fixed total transmit power. With the assumption of fixed-modulation for all subcarriers, transmit powers and beamforming weight vectors at each subcarrier are updated jointly, using an iterative algorithm that converges to the optimal solution for the entire network. Unlike most of the loading algorithms, this approach considers fixed bit allocation and optimizes the power allocation and reduces the interference for the entire network, rather than a single transmitter. We also propose joint time-domain beamforming and power control to reduce the complexity resulting from the number of beamformers and fast Fourier transformed blocks. The proposed algorithm is also extended to COFDM and we show that it improves the performance of those systems.

In this letter, a differential space-frequency modulation (DSFM) scheme is proposed for multiple input multiple-output (MIMO)-orthogonal frequency-division multiplexing (OFDM) systems in broadband wireless communications. We assume that the fading channels keep constant only within each OFDM block, and may change independently from one OFDM block to another. The differential schemes proposed for MIMO-OFDM systems in the literature cannot successfully decode with such a rapidly fading channel, since the successful decoding of the previously existing schemes relies on the assumption that the fading channel keeps constant within a period of several OFDM blocks, and it changes slowly from a period of several OFDM blocks to another. In our proposed DSFM scheme, the transmitted signals are differentially encoded in the frequency domain within each OFDM block. Thus, the differential decoding can be performed over subcarriers within each single OFDM block. Furthermore, if a statistical channel power-delay profile (PDP) is known at the transmitter, we propose to create a smooth logical channel to improve the performance of the DSFM scheme. We obtain the smooth logical channel by sorting the channel frequency responses over subcarriers from a statistical point of view. If the logical channel is not smooth enough, we further consider a pruning process in which we use only the "good" part of the channel and get rid of the "bad" part of the channel. Simulation results show that the proposed DSFM scheme over a smooth logical channel (with pruning, if necessary) performs well for various channel PDPs.

Digital fingerprinting is a method for protecting digital data in which fingerprints that are embedded in multimedia are capable of identifying unauthorized use of digital content. A powerful attack that can be employed to reduce this tracing capability is collusion, where several users combine their copies of the same content to attenuate/remove the original fingerprints. In this paper, we study the collusion resistance of a fingerprinting system employing Gaussian distributed fingerprints and orthogonal modulation. We introduce the maximum detector and the thresholding detector for colluder identification. We then analyze the collusion resistance of a system to the averaging collusion attack for the performance criteria represented by the probability of a false negative and the probability of a false positive. Lower and upper bounds for the maximum number of colluders K(max) are derived. We then show that the detectors are robust to different collusion attacks. We further study different sets of performance criteria, and our results indicate that attacks based on a few dozen independent copies can confound such a fingerprinting system. We also propose a likelihood-based approach to estimate the number of colluders. Finally, we demonstrate the performance for detecting colluders through experiments using real images.

Digital fingerprinting is a technology for tracing the distribution of multimedia content and protecting them from unauthorized redistribution. Unique identification information is embedded into each distributed copy of multimedia signal and serves as a digital fingerprint. Collusion attack is a cost-effective attack against digital fingerprinting, where colluders combine several copies with the same content but different fingerprints to remove or attenuate the original fingerprints. In this paper, we investigate the average collusion attack and several basic nonlinear collusions on independent Gaussian fingerprints, and study their effectiveness and the impact on the perceptual quality. With unbounded Gaussian fingerprints, perceivable distortion may exist in the fingerprinted copies as well as the copies after the collusion attacks. In order to remove this perceptual distortion, we introduce bounded Gaussian-like fingerprints and study their performance under collusion attacks. We also study several commonly used detection statistics and analyze their performance under collusion attacks. We further propose a preprocessing technique of the extracted fingerprints specifically for collusion scenarios to improve the detection performance.

A general space-frequency (SF) block code structure is proposed that can guarantee full-rate (one channel symbol per subcarrier) and full-diversity transmission in multiple-input multiple-output-orthogonal frequency-division multiplexing (MIMO-OFDM) systems. The proposed method can be used to construct SF codes for an arbitrary number of transmit antennas, any memoryless modulation and arbitrary power-delay profiles. Moreover, assuming that the power-delay profile is known at the transmitter, we devise an interleaving method to maximize the overall performance of the code. We show that the diversity product can be decomposed as the product of the "intrinsic" diversity product, which depends only on the used signal constellation and the code design, and the "extrinsic" diversity product, which depends only on the applied interleaving method and the power delay profile of the channel. Based on this decomposition, we propose an interleaving strategy to maximize the "extrinsic" diversity product. Extensive simulation results show that the proposed SF codes outperform the previously existing codes by about 3-5 dB, and that the proposed interleaving method results in about 1-3-dB performance improvement compared to random interleaving.