6G技术中的空中接入网:进展与展望

空中接入网(aerial access network, AAN)具有抗毁性强、覆盖范围广、视距传播概率高的特点,与地面网络相结合能够缓解流量负载压力,提供全时空的连接与服务,可有效助力第六代移动通信网络(6th generation mobile networks, 6G)“万物智联”愿景的实现.因此,在分析AAN中的卫星通信和高空平台(high altitude platform station, HAPS)技术研究进展的基础上,研究了卫星通信在数据接入方面的应用以及HAPS在一体化网络架构中的作用,探究了卫星与HAPS两种技术对未来网络的影响以及进一步优化二者的潜在方法.最后,对卫星接入的数据碰撞问题和HAPS的缓存问题进行讨论分析,并从上述问题出发展望了卫星通信和HAPS技术的未来发展方向.

Excimer laser 6^(th)generation:state of the art and refractive surgical outcomes

After nearly three decades of innovation in excimer laser,today we are presented with a state of the art generation targeting minimally invasive refractive surgery with high speed laser,faster trackers,pupil monitoring systems and better customization profiles.These systems are capable of delivering better treatments with less induced postoperative high order aberrations.The results reported by many authors had confirmed the superiority in efficiency and safety profiles of this generation compared to previous generations.Still,current technology is facing major challenges in the correction of high hyperopic errors and in presbyopic treatments,with upgrades in ablation centration and thermal control needed,which will ensure better biomechanical results,as a step closer to perfection in refractive surgery.

Intellicise communication system: model-driven semantic communications

As one of the critical technologies for the 6 th generation mobile communication system(6 G) mobile communication systems, artificial intelligence(AI) technology will provide complete automation for connecting the virtual and physical worlds. In order to construct the future ubiquitous intelligent network, people are beginning to rethink how mobile communication systems transmit and exploit intelligent information. This paper proposes a new communication paradigm, called the Intellicise communication system: model-driven semantic communication. Intellicise communication system is built on top of the traditional communication system and innovatively adds a new feature dimension on top of the traditional source coding, which enables the communication system to evolve from the traditional transmission of bit to the transmission of "model". Like the semantic base(Seb) for semantic communication, the model is considered as the new feature obtained from the joint source-channel coding. The sink node can re-construct the original signal based on the received model and the encoded sequence. In addition, the performance evaluation metrics and the implementation details of the Intellicise communication system are discussed in this paper. Finally, preliminary results of model-driven image transmission in the Intellicise communication system are presented.

Native intelligence for 6G mobile network: technical challenges,architecture and key features

The application of the artificial intelligence(AI) technology in the 5 th generation mobile communication system(5 G) networks promotes the development of the mobile communication network and its application in vertical industries, however, the application models of "patching" and "plug-in" have hindered the effect of AI applications. Meanwhile, the application of AI in all walks of life puts forward requirements for new capabilities of the future network, such as distributed training, real-time collaborative inference, local data processing, etc., which require "native intelligence design" in future networks. This paper discusses the requirements of native intelligence in the 6 th generation mobile communication system(6 G) networks from the perspectives of 5 G intelligent network challenges and the "ubiquitous intelligence" vision of 6 G, and analyzes the technical challenges of the AI workflows in its lifecycle and the AI as a service(AIaaS) in cloud network. The progress and deficiencies of the current research on AI functional architecture in various industry organizations are summarized. The end-to-end functional architecture for native AI for 6 G network and its three key technical characteristics are proposed: quality of AI services(QoAIS) based AI service orchestration for its full lifecycle, deep integration of native AI computing and communication, and integration of native AI and digital twin network. The directions of future research are also prospected.

Aerial edge computing for 6G

In the 6 th generation mobile communication system(6 G) era, a large number of delay-sensitive and computation-intensive applications impose great pressure on resource-constrained Internet of things(IoT) devices. Aerial edge computing is envisioned as a promising and cost-effective solution, especially in hostile environments without terrestrial infrastructures. Therefore, this paper focuses on integrating aerial edge computing into 6 G for providing ubiquitous computing services for IoT devices. This paper first presents the layered network architecture of aerial edge computing for 6 G. The benefits, potential applications, and design challenges are also discussed in detail. Next, several key techniques like unmanned aerial vehicle(UAV) deployment, operation mode, offloading mode, caching policy, and resource management are highlighted to present how to integrated aerial edge computing into 6 G. Then, the joint UAV deployment optimization and computation offloading method is designed to minimize the computing delay for a typical aerial edge computing network. Numerical results reveal the significant delay reduction of the proposed method compared with the other benchmark methods. Finally, several open issues for aerial edge computing in 6 G are elaborated to provide some guidance for future research.