arXiv subjects

Junshuo Liu

Publications and source records attributed to Junshuo Liu.

12 recordsLinked to original sources

arXiv2025

Bayesian Optimization for Non-Cooperative Game-Based Radio Resource Management

Radio resource management in modern cellular networks often calls for the optimization of complex utility functions that are potentially conflicting between different base stations (BSs). Coordinating the resource allocation strategies efficiently across BSs to ensure stable network service poses significant challenges, especially when each utility is accessible only via costly, black-box evaluations. This paper considers formulating the resource allocation among spectrum sharing BSs as a non-cooperative game, with the goal of aligning their allocation incentives toward a stable outcome. To address this challenge, we propose PPR-UCB, a novel Bayesian optimization (BO) strategy that learns from sequential decision-evaluation pairs to approximate pure Nash equilibrium (PNE) solutions. PPR-UCB applies martingale techniques to Gaussian process (GP) surrogates and constructs high probability confidence bounds for utilities uncertainty quantification. Experiments on downlink transmission power allocation in a multi-cell multi-antenna system demonstrate the efficiency of PPR-UCB in identifying effective equilibrium solutions within a few data samples.

eess.SP↗

arXiv2025

DGAR: A Unified Domain Generalization Framework for RF-Based Human Activity Recognition

Radio-frequency (RF)-based human activity recognition (HAR) provides a contactless and privacy-preserving solution for monitoring human behavior in applications such as astronaut extravehicular activity monitoring, human-autonomy collaborative cockpit, and unmanned aerial vehicle surveillance. However, real-world deployments usually face the challenge of domain knowledge shifts arising from inter-subject variability, heterogeneous physical environments, and unseen activity patterns, resulting in significant performance degradation. To address this issue, we propose DGAR, a domain-generalized activity recognition framework that learns transferable representations without collecting data from the target domain. DGAR integrates instance-adaptive feature modulation with cross-domain distribution alignment to enhance both personalization and generalization. Specifically, it incorporates a squeeze-and-excitation (SE) block to extract salient spatiotemporal features and employs correlation alignment to mitigate inter-domain discrepancies. Extensive experiments on public RF-based datasets -- HUST-HAR, Lab-LFM, and Office-LFM -- demonstrate that DGAR consistently outperforms state-of-the-art baselines, achieving up to a 5.81% improvement in weighted F1-score. The empirical results substantiate the generalization capability of DGAR in real-time RF sensing across dynamic scenarios.

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arXiv2024

TRIS-HAR: Transmissive Reconfigurable Intelligent Surfaces-assisted Cognitive Wireless Human Activity Recognition Using State Space Models

Human activity recognition (HAR) using radio frequency (RF) signals has garnered considerable attention for its applications in smart environments. However, traditional systems often struggle with limited independent channels between transmitters and receivers, multipath fading, and environmental noise, which particularly degrades performance in through-the-wall scenarios. In this paper, we present a transmissive reconfigurable intelligent surface (TRIS)-assisted through-the-wall human activity recognition (TRIS-HAR) system. The system employs TRIS technology to actively reshape wireless signal propagation, creating multiple independent paths to enhance signal clarity and improve recognition accuracy in complex indoor settings. Additionally, we propose the Human intelligence Mamba (HiMamba), an advanced state space model that captures temporal and frequency-based information for precise activity recognition. HiMamba achieves state-of-the-art performance on two public datasets, demonstrating superior accuracy. Extensive experiments indicate that the TRIS-HAR system improves recognition performance from 85.00% to 98.06% in laboratory conditions and maintains high performance across various environments. This approach offers a robust solution for enhancing RF-based HAR, with promising applications in smart home and elderly care systems.

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arXiv2024

PoseMamba: Monocular 3D Human Pose Estimation with Bidirectional Global-Local Spatio-Temporal State Space Model

Transformers have significantly advanced the field of 3D human pose estimation (HPE). However, existing transformer-based methods primarily use self-attention mechanisms for spatio-temporal modeling, leading to a quadratic complexity, unidirectional modeling of spatio-temporal relationships, and insufficient learning of spatial-temporal correlations. Recently, the Mamba architecture, utilizing the state space model (SSM), has exhibited superior long-range modeling capabilities in a variety of vision tasks with linear complexity. In this paper, we propose PoseMamba, a novel purely SSM-based approach with linear complexity for 3D human pose estimation in monocular video. Specifically, we propose a bidirectional global-local spatio-temporal SSM block that comprehensively models human joint relations within individual frames as well as temporal correlations across frames. Within this bidirectional global-local spatio-temporal SSM block, we introduce a reordering strategy to enhance the local modeling capability of the SSM. This strategy provides a more logical geometric scanning order and integrates it with the global SSM, resulting in a combined global-local spatial scan. We have quantitatively and qualitatively evaluated our approach using two benchmark datasets: Human3.6M and MPI-INF-3DHP. Extensive experiments demonstrate that PoseMamba achieves state-of-the-art performance on both datasets while maintaining a smaller model size and reducing computational costs. The code and models will be released.

cs.CV↗

arXiv2024

TRGR: Transmissive RIS-aided Gait Recognition Through Walls

Gait recognition with radio frequency (RF) signals enables many potential applications requiring accurate identification. However, current systems require individuals to be within a line-of-sight (LOS) environment and struggle with low signal-to-noise ratio (SNR) when signals traverse concrete and thick walls. To address these challenges, we present TRGR, a novel transmissive reconfigurable intelligent surface (RIS)-aided gait recognition system. TRGR can recognize human identities through walls using only the magnitude measurements of channel state information (CSI) from a pair of transceivers. Specifically, by leveraging transmissive RIS alongside a configuration alternating optimization algorithm, TRGR enhances wall penetration and signal quality, enabling accurate gait recognition. Furthermore, a residual convolution network (RCNN) is proposed as the backbone network to learn robust human information. Experimental results confirm the efficacy of transmissive RIS, highlighting the significant potential of transmissive RIS in enhancing RF-based gait recognition systems. Extensive experiment results show that TRGR achieves an average accuracy of 97.88\% in identifying persons when signals traverse concrete walls, demonstrating the effectiveness and robustness of TRGR.

cs.AI↗

arXiv2024

RISAR: RIS-assisted Human Activity Recognition with Commercial Wi-Fi Devices

Human activity recognition (HAR) holds significant importance in smart homes, security, and healthcare. Existing systems face limitations because of the insufficient spatial diversity provided by a limited number of antennas. Furthermore, inefficiencies in noise reduction and feature extraction from sensing data pose challenges to recognition performance. This study presents a reconfigurable intelligent surface (RIS)-assisted passive human activity recognition (RISAR) method, compatible with commercial Wi-Fi devices. RISAR leverages a RIS to enhance the spatial diversity of Wi-Fi signals, effectively capturing a wider range of information distributed across the spatial domain. A novel high-dimensional factor model based on random matrix theory is proposed to address noise reduction and feature extraction in the temporal domain. A dual-stream spatial-temporal attention network model is developed to assign variable weights to different characteristics and sequences, mimicking human cognitive processes in prioritizing essential information. Experimental analysis shows that RISAR significantly outperforms existing HAR methods in accuracy and efficiency, achieving an average accuracy of 97.26%. These findings underscore RISAR's adaptability and potential as a robust activity recognition solution in real environments.

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arXiv2024

Design and Prototyping of Transmissive RIS-Aided Wireless Communication

Reconfigurable Intelligent Surfaces (RISs) exhibit promising enhancements in coverage and data rates for wireless communication systems, particularly in the context of 5G and beyond. This paper introduces a novel approach by focusing on the design and prototyping of a transmissive RIS, contrasting with existing research predominantly centered on reflective RIS. The achievement of 1-bit transmissive RIS through the antisymmetry configuration of the two PIN diodes, nearly uniform transmission magnitudes but inversed phase states in a wide band can be obtained. A transmissive RIS prototype consisting of 16 $\times$ 16 elements is meticulously designed, fabricated, and subjected to measurement to validate the proposed design. The results demonstrate that the proposed RIS unit cell achieves effective 1-bit phase tuning with minimal insertion loss and a transmission bandwidth of 3 dB exceeding $20\%$ at 5.8GHz. By dynamically modulating the quantized code distributions on the RIS, it becomes possible to construct scanning beams. The experimental outcomes of the RIS-assisted communication system validate that, in comparison to scenarios without RIS, the signal receiving power experiences an increase of approximately 7dB when RIS is deployed to overcome obstacles. This underscores the potential applicability of mobile RIS in practical communication.

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arXiv2024

TRTAR: Transmissive RIS-assisted Through-the-wall Human Activity Recognition

Device-free human activity recognition plays a pivotal role in wireless sensing. However, current systems often fail to accommodate signal transmission through walls or necessitate dedicated noise removal algorithms. To overcome these limitations, we introduce TRTAR: a device-free passive human activity recognition system integrated with a transmissive reconfigurable intelligent surface (RIS). TRTAR eliminates the necessity for dedicated devices or noise removal algorithms, while specifically addressing signal propagation through walls. Unlike existing approaches, TRTAR solely employs a transmissive RIS at the transmitter or receiver without modifying the inherent hardware structure. Experimental results demonstrate that TRTAR attains an average accuracy of 98.13% when signals traverse concrete walls.

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arXiv2023

A Wi-Fi Signal-Based Human Activity Recognition Using High-Dimensional Factor Models

Passive sensing techniques based on Wi-Fi signals have emerged as a promising technology in advanced wireless communication systems due to their widespread application and cost-effectiveness. However, the proliferation of low-cost Internet of Things (IoT) devices has led to dense network deployments, resulting in increased levels of noise and interference in Wi-Fi environments. This, in turn, leads to noisy and redundant Channel State Information (CSI) data. As a consequence, the accuracy of human activity recognition based on Wi-Fi signals is compromised. To address this issue, we propose a novel CSI data signal extraction method. We established a human activity recognition system based on the Intel 5300 network interface cards (NICs) and collected a dataset containing six categories of human activities. Using our approach, signals extracted from the CSI data serve as inputs to machine learning (ML) classification algorithms to evaluate classification performance. In comparison to ML methods based on Principal Component Analysis (PCA), our proposed High-Dimensional Factor Model (HDFM) method improves recognition accuracy by 6.8%.

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arXiv2023

RIS-aided Real-time Beam Tracking for a Mobile User via Bayesian Optimization

The conventional beam management procedure mandates that the user equipment (UE) periodically measure the received signal reference power (RSRP) and transmit these measurements to the base station (BS). The challenge lies in balancing the number of beams used: it should be large enough to identify high-RSRP beams but small enough to minimize reporting overhead. This paper investigates this essential performance-versus-overhead trade-off using Bayesian optimization. The proposed approach represents the first application of real-time beam tracking via Bayesian optimization in RIS-assisted communication systems. Simulation results validate the effectiveness of this scheme.

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arXiv2023

Design of Reconfigurable Intelligent Surfaces for Wireless Communication: A Review

This paper addresses the hardware structure of Reconfigurable Intelligent Surfaces (RIS) and presents a comprehensive overview of RIS design, considering both unit design and prototype systems. It commences by tracing the evolutionary trajectory of RIS, originating from static cell-structured hypersurfaces. The article conducts a meticulous examination from the standpoint of adaptability, elucidating the diverse array of unit structures and design philosophies that underlie existing RIS frameworks. Following this, the study systematically categorizes and synthesizes channel modeling research for RIS-facilitated wireless communication, leveraging both physical insights and statistical data. Additionally, the article provides a detailed exposition of current RIS experimental setups and their corresponding empirical findings, delving into the attributes of prototype design and system functionalities. Moreover, this work introduces an in-house developed RIS prototype. The prototype undergoes rigorous empirical evaluation, encompassing multi-hop RIS signal amplification, image reconstruction, and real-world indoor signal coverage experiments. The empirical results robustly affirm the efficacy of RIS in effectively mitigating signal coverage blind spots and enabling radio wave imaging. With RIS-enhanced augmentation, the average indoor signal gain surpasses 8 dB.

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arXiv2023

Multi-RIS-aided Wireless Communications in Real-world: Prototyping and Field Trials

The performance of multiple reconfigurable intelligent surfaces (RISs) receives limited attention in previous studies. This article fills this research gap by investigating the capabilities of multiple RISs in real-world networks. We propose a simplified yet highly scalable sandwich architecture for implementing one-bit unit cells, with the flexibility to accommodate multi-bit unit cells. To effectively control multiple RISs, we present a cost-effective remote-controlling scheme and develop a cloud-based RIS management system. Through a series of four field trials, we demonstrate the effectiveness of multi-hop routing schemes in establishing reliable links. Our experiments reveal significant improvements in signal strength and data transmission in multi-RIS-aided Wi-Fi and commercial 5G networks. Furthermore, we investigate the power scaling law of RIS-aided beamforming and provide insights into the roles of the later nodes in multi-hop relay chains.

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