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.

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.

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.

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

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.

Chinese Restaurant Game - Part I: Theory of Learning with Negative Network Externality

In a social network, agents are intelligent and have the capability to make decisions to maximize their utilities. They can either make wise decisions by taking advantages of other agents' experiences through learning, or make decisions earlier to avoid competitions from huge crowds. Both these two effects, social learning and negative network externality, play important roles in the decision process of an agent. While there are existing works on either social learning or negative network externality, a general study on considering both these two contradictory effects is still limited. We find that the Chinese restaurant process, a popular random process, provides a well-defined structure to model the decision process of an agent under these two effects. By introducing the strategic behavior into the non-strategic Chinese restaurant process, in Part I of this two-part paper, we propose a new game, called Chinese Restaurant Game, to formulate the social learning problem with negative network externality. Through analyzing the proposed Chinese restaurant game, we derive the optimal strategy of each agent and provide a recursive method to achieve the optimal strategy. How social learning and negative network externality influence each other under various settings is also studied through simulations.

Chinese Restaurant Game - Part II: Applications to Wireless Networking, Cloud Computing, and Online Social Networking

In Part I of this two-part paper [1], we proposed a new game, called Chinese restaurant game, to analyze the social learning problem with negative network externality. The best responses of agents in the Chinese restaurant game with imperfect signals are constructed through a recursive method, and the influence of both learning and network externality on the utilities of agents is studied. In Part II of this two-part paper, we illustrate three applications of Chinese restaurant game in wireless networking, cloud computing, and online social networking. For each application, we formulate the corresponding problem as a Chinese restaurant game and analyze how agents learn and make strategic decisions in the problem. The proposed method is compared with four common-sense methods in terms of agents' utilities and the overall system performance through simulations. We find that the proposed Chinese restaurant game theoretic approach indeed helps agents make better decisions and improves the overall system performance. Furthermore, agents with different decision orders have different advantages in terms of their utilities, which also verifies the conclusions drawn in Part I of this two-part paper.