# 6G Cognitive Information Theory: A Mailbox Perspective
## 4 Distributed Intelligent Network  
In this section, we introduce the distributed intelligent network with a fusion of the cloud-edge-terminal. With the rapid development of AI chips, embedding intelligent algorithms in network infrastructures (access networks, edge infrastructures, core networks,and cloud infrastructures) is becoming a new reality. In a 6G network, the intelligence of networks can be realized by making full use of the sensing, communication, computing, and storage capabilities of network nodes. Unlike the traditional network, with separate transmission and computing, 6G will be more complex and highly distributed. It will be based on the integration of transmission, computing, caching, and intelligence. In this network, the distributed communication, computing, and caching resources perceive and transform into each other where cloud, edge cloud, and users are effectively collaborating with each other intelligently. Thus, in this way, not only the utilization of network communication, computing, and caching resources can be improved, but fast scheduling of services and resources is also realized to satisfy users experience. Specifically, the architecture can be divided into resource cognition engine, service cognition engine, and network orchestration management layer. The resource cognition engine is responsible for perception and forwarding of heterogeneous communication, computing, and storage resources. The service cognition engine is responsible for the decomposition and scheduling of general and intelligence services. Based on the resource cognition engine and service cognition engine, the distributed intelligent control [27], layout, and scheduling of the network are realized in a network orchestration layer. This aims to meet the demand of users and also make use of the complicated network environment. For example, the overall perception and measurement can be conducted by existing multidimensional communication, computing, and caching resources in a 6G network according to the service demand of users, so as to reasonably allocate and schedule services. Next, we introduce the realization of transmission and computing integration with the fusion of communication, computing, and caching resources through the cloud-edge-terminal collaboration. As a supplement and extension of the limited terminal and spectrum resources, the aim is to reduce the cloud service access and transmission delay. Under these conditions, edge caching represents an efficient technology in the trade-off optimization scheme for cost and storage resources. The edge caching focuses on the three following questions: where to cache, how to cache, and what to cache. Specifically, to provide end-users with more accurate real-time processing and high-quality services, the personalized intelligent caching policy should be designed according to a user's data as perceived and acquired by an edge network. This includes personal information, business requirements, location, mobility, epidemicity, and historical information. To achieve this, the most appropriate content and optimal caching placement node (edge cloud) should be selected, where caching content should be transmitted using the distributed data-transmission technology and placed on the computing nodes of an optimal edge cloud. Therefore, this section focuses mainly on the mainstream edge-caching strategies, which are as follows. (1) Content caching and distribution: where content that can be cached and shared is determined based on the popularity of the content requested, user mobility, and type of content. (2) Task caching [28]: in the current era hardware devices, AI, and 5G communication technologies are becoming increasingly popular, and computing tasks frequently appear in the request results and service process of terminals. If a user requests a computing task that is not cached by an edge cloud, then it will be offloaded to the edge cloud to be completed before returning to the user.