Heart rate variability (HRV) is an essential indicator of cardiovascular and nervous system function, with wide applications in health monitoring and disease management. Traditional contact-based methods like electrocardiograms (ECG) and photoplethysmography (PPG), while effective, face significant limitations in user experience, such as discomfort during prolonged use, and challenges in long-term, continuous monitoring. Meanwhile, contactless wireless sensing based on mmWave radar offers a promising alternative but is hindered by issues of directional sensing and noise interference. In this paper, we propose mmCG (mmWave Cardiac Gram), a contactless HRV monitoring system. Specifically, it integrates a heartbeat spatial localization method for directional sensing, which significantly improves the SNR, and a dynamic peak search algorithm that leverages heartbeat temporal correlations to effectively mitigate the impact of artifacts. Experimental results show that mmCG achieves advanced performance, reducing the IBI error to 9.44ms, a 51.29% improvement over existing methods. With its lightweight design and enhanced accuracy, mmCG offers a practical solution for daily HRV monitoring, with potential applications in stress management, personalized healthcare, and cardiovascular disease monitoring.

Space-air-ground integrated network (SAGIN) is a promising network architecture for next-generation wireless networks, which combines satellite networks, aerial networks, and terrestrial networks to enable ubiquitous global network services to ground users and improve connectivity for wide deployment wireless applications. Also, free-space optical (FSO) communication with the advantages of low deployment cost, energy efficiency, and extremely high-speed data-delivering capability has attracted more attention recently. However, data transmission efficiency in SAGIN is still limited by the dynamic time-varying network topology and data transmission link connection. In this paper, we construct an FSO/radio frequency (RF) space-air-ground integrated network to enable large-scale and high-speed data transmission as well as degrade the burden of terrestrial networks. In addition, a deep-Q network-based reinforcement learning with an experience replay memory mechanism is proposed to execute dynamic hybrid routing by evaluated rewards. The simulation results show that the proposal achieves significant network performance compared with baseline methods.

In the context of the 5G/6G mobile network, high levels of requirements such as ultra-high data transmission rate, support for the high mobility node and seamless connection need to be handled. Additionally, ensuring user quality of service (QoS) in high-density and high-traffic mobile networks presents a significant challenge. Unmanned aerial vehicles (UAVs) have emerged as key components in providing flexible assistance in aerial spaces. To further enhance the network performance in dynamic and heterogeneous environments, an intelligent resource allocation strategy with low communication overhead is essential. In this paper, we construct a UAV-assisted mobile network to provide efficient communication for all mobile users in high-density and high-traffic environments, at the same time, a digital twin-empowered dynamic resource allocation strategy based on online training with low communication overhead is proposed. Our proposal employs digital twin-empowered multi-task learning to meet various resource allocation requirements for different node types. Moreover, we propose a deep-Q network-based reinforcement learning mechanism with experience replay memory to execute resource allocation decisions based on evaluated rewards. The simulation results show that the proposal achieves significant network performance compared with baseline algorithms.

The sixth generation (6G) is conceived to address the expected high level of requirements (such as ultra-high-data-transmission rate, support for the highest moving speed and seamless connection, etc.) in the next decade and beyond. In the context of 6G, a large number of Industrial Internet of Things (IoT) (IIoT) devices may access the network, and thanks to the rapid development of artificial intelligence make smart manufacturing has the opportunity to be realized. However, a large number of IoT devices, the tremendous volume of data, the heterogeneous nature of devices, and the increasing concerns of privacy challenge the efficient management and quality of services in IIoT. To address these problems, in this article, a device-to-device (D2D) communication-aided digital twin edge network is proposed, where edge computing is introduced to bring computing and storage resources near to the end devices, and digital twin is utilized to fill the gap between physical and virtual space and D2D communication is applied to assist resource limited IoT devices to achieve normal communication. Moreover, digital twin-empowered federated reinforcement learning is leveraged to provide privacy awareness and decentralized resource allocation strategy training on D2D communication links to further improve network performance. The simulation results show that the proposal achieves significant network performance compared with baseline algorithms.