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.

The market for consumer drones is growing and drones are becoming ever more pervasive than before in our life. However, drones have also brought about severe privacy violations and even safety issues. Especially, drones with cameras can snap pictures or take private videos. Researchers have designed drone detection mechanisms by passively inspecting the radio frequency (RF) signal in the communication channel between a drone and its controller. However, passive detection solutions shall fail when drones are in autopilot mode without control signals from controllers. In this paper, we seek to detect autopilot drones that transmit no RF signals by developing a proactive detection system named Dr. Defender. To this end, we resort to the Wi-Fi signals prevalent at each house and propose a proactive drone detection mechanism. To facilitate the detection of drones with Wi-Fi, we first study the motion characteristics of drones, including the shifting, moving, and spinning of propellers that can uniquely represent a drone. Then we investigate the physical layer information of Wi-Fi signals, i.e., the channel state information (CSI), to reveal specific motions of a drone. Finally, we implement our CSI-based proactive drone detection system, which requires no signal transmission from a drone or its controller. We extensively validate the feasibility and performance of our solution under different distances and directions of drones relative to a window. Results show that Dr. Defender can accurately detect drones 10 meters away.

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.

Wi-Fi-based crowd counting is predominant because of its noninvasive and ubiquitous advantages. However, the existing Wi-Fi-based crowd counting systems have the constraint that there is always a maximum number of people counted. In order to address this issue, a Wi-Fi-based cross-environment crowd counting system, which has the capability of both estimating the walking direction and crowd counting by only one single link, called WiCrowd is proposed. WiCrowd relaxes the restriction of people number counted and demonstrates its extraordinary robustness when the environment changes. The signal change trends of the people flow are theoretically analyzed and people flow moving direction is inferred. The unique features using the eigenvalue of the convariance matrix of amplitude and phase are derived to effectively detect prominent signal changes led by the crowd movement near LoS. By adopting the augmented feature representations, the robustness of WiCrowd is improved when the environment changes. The experimental results in a typical indoor environment demonstrate the superior performance of WiCrowd. This system achieves 87.4%, 85.8%, and 79.4% recognition accuracy for the flow movement direction estimation, respectively, and 82.4% and 81.6% of the overall cross-environment accuracy for the number of subjects counted in the people flow.

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.

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.

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.

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.

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.

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.

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

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.