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.