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.

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.

Reconfigurable intelligent surfaces (RISs) hold significant potential for enhancing coverage and data rates in 6G wireless communication systems. While most research has concentrated on reflective RIS applications, studies on transmissive RIS (TRIS) have been largely limited to simulations or laboratory prototypes. To evaluate the real-world performance of TRIS, we developed a 256 -unit cell, 1-bit TRIS prototype operating at the 5.8 GHz frequency band. The design employs an antisymmetric configuration of two PIN diodes, achieving nearly uniform transmission amplitude with inverse phase states over a wide 20 % bandwidth. The fabricated TRIS, composed of$16 \times 16$unit cells, demonstrates effective 1-bit phase tuning with minimal insertion loss and a 3 dB transmission bandwidth exceeding 1.2 GHz. By dynamically modulating the quantized code distributions, we achieved scanning beams of$\pm 60^{\circ}$. Subsequently, we integrated the TRIS into a Wi-Fi wireless communication system and assessed its performance in a real-world wall-penetration scenario. The TRIS improved the average Signal-to-Noise Ratio (SNR) by 8 dB, with a maximum gain of 15 dB, across a 29$\mathbf{m}^{2}$indoor area. Download speeds also increased, showing a maximum improvement of 10.57 Mbps and an average gain of 8.01 Mbps. These results highlight the effectiveness of TRIS in reducing path loss, enhancing SNR, and improving WiFi performance in challenging wall-penetration environments, ultimately leading to a better user experience.

Human activity recognition (HAR) using radio frequency (RF) signals has attracted increasing interest due to its non-intrusive and privacy-preserving nature. However, traditional systems often suffer from multipath fading, environmental noise, and limited spatial diversity, particularly in through-the-wall scenarios. In this paper, we propose TRIS-HAR, a novel HAR system that integrates a transmissive reconfigurable intelligent surface (TRIS) with an advanced dual-stream state space model, Human intelligence Mamba (HiMamba). The TRIS actively reshapes the propagation environment by constructing deterministic quasi-line-of-sight (QLoS) paths across obstacles, significantly improving channel state information (CSI) quality. Complementing this, HiMamba leverages a lightweight structured state space architecture to jointly model temporal and spectral dynamics, enabling robust activity recognition under non-line-of-sight conditions. Extensive experiments on both public and real-world datasets demonstrate that TRIS-HAR improves recognition accuracy from 85.00% to 98.06% and maintains strong generalizability across environments. The model is also deployed on a CPU-based edge device, achieving real-time inference at 108 FPS with minimal memory cost. This work establishes a co-designed hardware-algorithm framework for RF-based HAR, offering a scalable and deployable solution for smart homes, healthcare, and next-generation pervasive sensing applications.

Reconfigurable intelligent surfaces (RISs) present significant potential for enhancing coverage and data rates in 6G wireless communication systems. While most research has focused on reflective RIS applications, studies on transmissive RIS (TRIS) have largely been limited to simulations or laboratory-scale prototypes. To evaluate the real-world performance of TRIS, we develop a 256-unit, 1-bit TRIS prototype operating at the 5.8 GHz frequency band. The design uses an antisymmetric configuration of two PIN diodes, achieving nearly uniform transmission amplitude with inverse phase states over a wide 20% bandwidth. A TRIS composed of 16 × 16 units is fabricated and validated through measurements, showing effective 1-bit phase tuning with minimal insertion loss and a 3 dB transmission bandwidth exceeding 1.2 GHz at the central frequency of 5.8 GHz. By dynamically modulating the quantized code distributions, ±90° scanning beams are achieved. We then integrate the TRIS into a wireless communication system and evaluate its performance in a real-world wall-penetration scenario. With directional antennas that are connected to the Universal Software Radio Peripheral (USRP) modules and are placed on either side of a 240 mm concrete wall, the TRIS provides a signal power gain of 19-23 dB within a ±90° beamforming range, significantly reducing path loss. Additionally, we assess its impact on a commercial Wi-Fi system, where the TRIS improves the average Signal-to-Noise Ratio (SNR) by 8 dB, with a maximum gain of 15 dB, across a 29 m2indoor area. Downlink rates also increase, with a maximum improvement of 10.57 Mbps and an average gain of 8.01 Mbps. These results highlight TRIS’s effectiveness in reducing path loss, enhancing SNR, and improving Wi-Fi performance in challenging wall-penetration environments, leading to better user experiences.

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.

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.

Human activity recognition (HAR) is crucial in smart homes, security, and healthcare. Existing systems are limited by insufficient spatial diversity due to the constrained number of antennas. Additionally, challenges in noise reduction and feature extraction from sensing data, particularly channel state information (CSI), affect recognition performance. This study introduces a reconfigurable intelligent surface (RIS)-assisted passive HAR (RISAR) method compatible with commercial Wi-Fi devices. RISAR leverages RIS to enhance the spatial diversity of Wi-Fi signals, capturing a broader range of spatial information. A novel high-dimensional factor model based on random matrix theory is proposed to improve noise reduction and feature extraction in the temporal domain. Furthermore, a dual-stream spatiotemporal attention network model is developed to assign variable weights to different characteristics and sequences, mimicking human cognitive processes in prioritizing essential information. Experimental results demonstrate that RISAR significantly outperforms existing HAR methods in both accuracy and efficiency, achieving an average accuracy of 97.26%. These findings highlight RISAR’s adaptability and potential as a robust activity recognition solution in real-world environments.