arXiv subjects

K. J. Ray Liu

Publications and source records attributed to K. J. Ray Liu.

At least 19 recordsLinked to original sources

arXiv2026

AM-FM: A Foundation Model for Ambient Intelligence Through WiFi

Ambient intelligence, continuously understanding human presence, activity, and physiology in physical spaces, is fundamental to smart environments, health monitoring, and human-computer interaction. WiFi infrastructure provides a ubiquitous, always-on, privacy-preserving substrate for this capability across billions of IoT devices. Yet this potential remains largely untapped, as wireless sensing has typically relied on task-specific models that require substantial labeled data and limit practical deployment. We present AM-FM, the first foundation model for ambient intelligence and sensing through WiFi. AM-FM is pre-trained on 9.2 million unlabeled Channel State Information (CSI) samples collected over 439 days from 20 commercial device types deployed worldwide, learning general-purpose representations via contrastive learning, masked reconstruction, and physics-informed objectives tailored to wireless signals. Evaluated on public benchmarks spanning nine downstream tasks, AM-FM shows strong cross-task performance with improved data efficiency, demonstrating that foundation models can enable scalable ambient intelligence using existing wireless infrastructure.

cs.LG↗

arXiv2025

CSI-Bench: A Large-Scale In-the-Wild Dataset for Multi-task WiFi Sensing

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.

eess.SP↗

arXiv2025

Scaling WiFi Sensing for Ubiquitous Home Monitoring: Lessons from Real-World Deployment on Millions of Devices

WiFi-based home monitoring offers compelling advantages over traditional camera and sensor solutions by leveraging existing wireless infrastructure for contactless, privacy-preserving and through-the-wall detection. This paper presents insights from developing and deploying a WiFi-based human monitoring system across real-world residential environments, addressing the gap between academic research and practical deployment. Through a two-year study involving 280 edge devices across 15 homes in 11 U.S. states, collecting over 4 million motion samples, we identify and address four critical deployment challenges previously underexplored in academic settings: (1) false positives from non-human motion sources (pets, robots) that degrade system reliability, (2) hardware heterogeneity in commercial IoT devices causing inconsistent CSI quality, (3) signal interference in multi-user environments limiting individual tracking capabilities, and (4) computational and bandwidth constraints preventing real-time edge processing. The deployed system integrates a biomechanics-based classifier that reduces non-human false alarms from 63.1\% to 8.4\%, a multi-layer sensing quality metric validating device suitability without environment-specific calibration, proximity-based multi-user detection leveraging distributed IoT devices, and a hybrid edge-cloud architecture with ACF-based compression achieving 99.72\% data reduction. The integrated system achieves 92.61\% human motion detection accuracy across diverse uncontrolled home environments. We have successfully deployed home monitoring technology on millions of WiFi routers nationwide and smart IoT devices (e.g., bulbs, plugs) worldwide, demonstrating its viability for large-scale real-world applications. We share these findings to guide future research toward deployable WiFi sensing systems.

eess.SP↗

arXiv2025

DeepCPD: Deep Learning Based In-Car Child Presence Detection Using WiFi

Child presence detection (CPD) is a vital technology for vehicles to prevent heat-related fatalities or injuries by detecting the presence of a child left unattended. Regulatory agencies around the world are planning to mandate CPD systems in the near future. However, existing solutions have limitations in terms of accuracy, coverage, and additional device requirements. While WiFi-based solutions can overcome the limitations, existing approaches struggle to reliably distinguish between adult and child presence, leading to frequent false alarms, and are often sensitive to environmental variations. In this paper, we present DeepCPD, a novel deep learning framework designed for accurate child presence detection in smart vehicles. DeepCPD utilizes an environment-independent feature-the auto-correlation function (ACF) derived from WiFi channel state information (CSI)-to capture human-related signatures while mitigating environmental distortions. A Transformer-based architecture, followed by a multilayer perceptron (MLP), is employed to differentiate adults from children by modeling motion patterns and subtle body size differences. To address the limited availability of in-vehicle child and adult data, we introduce a two-stage learning strategy that significantly enhances model generalization. Extensive experiments conducted across more than 25 car models and over 500 hours of data collection demonstrate that DeepCPD achieves an overall accuracy of 92.86%, outperforming a CNN baseline by a substantial margin (79.55%). Additionally, the model attains a 91.45% detection rate for children while maintaining a low false alarm rate of 6.14%.

eess.SP↗

arXiv2024

Robust Proximity Detection using On-Device Gait Monitoring

Proximity detection in indoor environments based on WiFi signals has gained significant attention in recent years. Existing works rely on the dynamic signal reflections and their extracted features are dependent on motion strength. To address this issue, we design a robust WiFi-based proximity detector by considering gait monitoring. Specifically, we propose a gait score that accurately evaluates gait presence by leveraging the speed estimated from the autocorrelation function (ACF) of channel state information (CSI). By combining this gait score with a proximity feature, our approach effectively distinguishes different transition patterns, enabling more reliable proximity detection. In addition, to enhance the stability of the detection process, we employ a state machine and extract temporal information, ensuring continuous proximity detection even during subtle movements. Extensive experiments conducted in different environments demonstrate an overall detection rate of 92.5% and a low false alarm rate of 1.12% with a delay of 0.825s.

eess.SP↗

arXiv2024

mmID: High-Resolution mmWave Imaging for Human Identification

Achieving accurate human identification through RF imaging has been a persistent challenge, primarily attributed to the limited aperture size and its consequent impact on imaging resolution. The existing imaging solution enables tasks such as pose estimation, activity recognition, and human tracking based on deep neural networks by estimating skeleton joints. In contrast to estimating joints, this paper proposes to improve imaging resolution by estimating the human figure as a whole using conditional generative adversarial networks (cGAN). In order to reduce training complexity, we use an estimated spatial spectrum using the MUltiple SIgnal Classification (MUSIC) algorithm as input to the cGAN. Our system generates environmentally independent, high-resolution images that can extract unique physical features useful for human identification. We use a simple convolution layers-based classification network to obtain the final identification result. From the experimental results, we show that resolution of the image produced by our trained generator is high enough to enable human identification. Our finding indicates high-resolution accuracy with 5% mean silhouette difference to the Kinect device. Extensive experiments in different environments on multiple testers demonstrate that our system can achieve 93% overall test accuracy in unseen environments for static human target identification.

cs.CV↗

arXiv2022

RadioSES: mmWave-Based Audioradio Speech Enhancement and Separation System

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 propose RadioSES, an audioradio speech enhancement and separation system that overcomes inherent problems in audio-only systems. By fusing a complementary radio modality, RadioSES can estimate the number of speakers, solve 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 that RadioSES outperforms a variety of state-of-the-art baselines, with consistent performance gains in different environmental settings. Compared with the audiovisual methods, RadioSES provides similar improvements (e.g., ~3 dB gains in SiSDR), along with the benefits of lower computational complexity and being less privacy concerning.

eess.AS↗

arXiv2021

RadioMic: Sound Sensing via mmWave Signals

Voice interfaces has become an integral part of our lives, with the proliferation of smart devices. Today, IoT 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 paper, 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 or human throat) 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 of RF data. We thoroughly evaluate 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

eess.SP↗

arXiv2017

WiBall: A Time-Reversal Focusing Ball Method for Indoor Tracking

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 re-calibration. 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.

eess.SP↗

arXiv2017

WiSpeed: A Statistical Electromagnetic Approach for Device-Free Indoor Speed Estimation

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 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 line-of-sight 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.

eess.SP↗

arXiv2017

A Graphical Evolutionary Game Approach to Social Learning

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

cs.GT↗

arXiv2017

Dynamic Sharing Through the ADMM

In this paper, we study a dynamic version of the sharing problem, in which a dynamic system cost function composed of time-variant local costs of subsystems and a shared time-variant cost of the whole system is minimized. A dynamic alternating direction method of multipliers (ADMM) is proposed to track the varying optimal points of the dynamic optimization problem in an online manner. We analyze the convergence properties of the dynamic ADMM and show that, under several standard technical assumptions, the iterations of the dynamic ADMM converge linearly to some neighborhoods of the time-varying optimal points. The sizes of these neighborhoods depend on the drifts of the dynamic objective functions: the more drastically the dynamic objective function evolves across time, the larger the sizes of these neighborhoods. We also investigate the impact of the drifts on the steady state convergence behaviors of the dynamic ADMM. Finally, two numerical examples, namely a dynamic sharing problem and the dynamic least absolute shrinkage and selection operator (LASSO), are presented to corroborate the effectiveness of the proposed dynamic ADMM. It is observed that the dynamic ADMM can track the time-varying optimal points quickly and accurately. For the dynamic LASSO, the dynamic ADMM has competitive performance compared to the benchmark offline optimizor while the former possesses significant computational advantage over the latter.

math.OC↗

arXiv2017

Distributed Linearized ADMM for Network Cost Minimization

In this work, we study a generic network cost minimization problem, in which every node has a local decision vector to determine. Each node incurs a cost depending on its decision vector and each link also incurs a cost depending on the decision vectors of its two end nodes. All nodes cooperate to minimize the overall network cost. The formulated network cost minimization problem has broad applications in distributed signal processing and control over multi-agent systems. To obtain a decentralized algorithm for the formulated problem, we resort to the distributed alternating direction method of multipliers (DADMM). However, each iteration of the DADMM involves solving a local optimization problem at each node, leading to intractable computational burden in many circumstances. As such, we propose a distributed linearized ADMM (DLADMM) algorithm for network cost minimization. In the DLADMM, each iteration only involves closed-form computations and avoids local optimization problems, which greatly reduces the computational complexity compared to the DADMM. We prove that the DLADMM converges to an optimal point when the local cost functions are convex and have Lipschitz continuous gradients. Linear convergence rate of the DLADMM is also established if the local cost functions are further strongly convex. Numerical experiments are conducted to corroborate the effectiveness of the DLADMM and we observe that the DLADMM has similar convergence performance as DADMM does while the former enjoys much lower computational overhead. The impact of network topology, connectivity and algorithm parameters are also investigated through simulations.

math.OC↗

arXiv2017

Decentralized Sparse Multitask RLS over Networks

Distributed adaptive signal processing has attracted much attention in the recent decade owing to its effectiveness in many decentralized real-time applications in networked systems. Because many natural signals are highly sparse with most entries equal to zero, several decentralized sparse adaptive algorithms have been proposed recently. Most of them is focused on the single task estimation problems, in which all nodes receive data associated with the same unknown vector and collaborate to estimate it. However, many applications are inherently multitask oriented and each node has its own unknown vector different from others'. The related multitask estimation problem benefits from collaborations among the nodes as neighbor nodes usually share analogous properties and thus similar unknown vectors. In this work, we study the distributed sparse multitask recursive least squares (RLS) problem over networks. We first propose a decentralized online alternating direction method of multipliers (ADMM) algorithm for the formulated RLS problem. The algorithm is simplified for easy implementation with closed-form computations in each iteration and low storage requirements. Moreover, to further reduce the complexity, we present a decentralized online subgradient method with low computational overhead. We theoretically analyze the convergence behavior of the proposed subgradient method and derive an error bound related to the network topology and algorithm parameters. The effectiveness of the proposed algorithms is corroborated by numerical simulations and an accuracy-complexity tradeoff between the proposed two algorithms is highlighted.

math.OC↗

arXiv2014

Data-Driven Stochastic Models and Policies for Energy Harvesting Sensor Communications

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

cs.NI↗

arXiv2013

Evolutionary Dynamics of Information Diffusion over Social Networks

Current social networks are of extremely large-scale generating tremendous information flows at every moment. How information diffuse over social networks has attracted much attention from both industry and academics. Most of the existing works on information diffusion analysis are based on machine learning methods focusing on social network structure analysis and empirical data mining. However, the dynamics of information diffusion, which are heavily influenced by network users' decisions, actions and their socio-economic interactions, is generally ignored by most of existing works. In this paper, we propose an evolutionary game theoretic framework to model the dynamic information diffusion process in social networks. Specifically, we derive the information diffusion dynamics in complete networks, uniform degree and non-uniform degree networks, with the highlight of two special networks, Erdős-Rényi random network and the Barabási-Albert scale-free network. We find that the dynamics of information diffusion over these three kinds of networks are scale-free and the same with each other when the network scale is sufficiently large. To verify our theoretical analysis, we perform simulations for the information diffusion over synthetic networks and real-world Facebook networks. Moreover, we also conduct experiment on Twitter hashtags dataset, which shows that the proposed game theoretic model can well fit and predict the information diffusion over real social networks.

cs.SI↗

arXiv2013

Indian Buffet Game with Negative Network Externality and Non-Bayesian Social Learning

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

cs.GT↗

arXiv2013

Distributed Adaptive Networks: A Graphical Evolutionary Game-Theoretic View

Distributed adaptive filtering has been considered as an effective approach for data processing and estimation over distributed networks. Most existing distributed adaptive filtering algorithms focus on designing different information diffusion rules, regardless of the nature evolutionary characteristic of a distributed network. In this paper, we study the adaptive network from the game theoretic perspective and formulate the distributed adaptive filtering problem as a graphical evolutionary game. With the proposed formulation, the nodes in the network are regarded as players and the local combiner of estimation information from different neighbors is regarded as different strategies selection. We show that this graphical evolutionary game framework is very general and can unify the existing adaptive network algorithms. Based on this framework, as examples, we further propose two error-aware adaptive filtering algorithms. Moreover, we use graphical evolutionary game theory to analyze the information diffusion process over the adaptive networks and evolutionarily stable strategy of the system. Finally, simulation results are shown to verify the effectiveness of our analysis and proposed methods.

cs.GT↗