# 6G Cognitive Information Theory: A Mailbox Perspective
## 4 Distributed Intelligent Network
### Fusion of Terminal Caching, Communication and Computing
Specifically, to achieve a collaborative optimization between the terminal and edge cloud, the goal of the single terminal 3C offloading is to minimize the user's delay. This delay consists of three parts: wireless-transmission delay, computing delay, and delay in the optical transmission. Then, the energy consumption at the terminal can be obtained, including the energy consumption of data transmission and energy consumption of local computing. Using a combination with the corresponding offloading and caching strategies, the constraint conditions are the same as those in an optimal 3C fusion and configuration. For this optimization problem, a stable caching, communication, and computing resource-allocation scheme for a single terminal is given. _(2) 3C fusion for multi-terminal._ In a multi-terminal scenario, if collaboration between terminals is not initiated, a model similar to the single-terminal model should be considered; otherwise, a mobile device can share the communication, computing, and storage resources with another device through collaboration between their terminals, such as a D2D connection between devices, which is conducive to an increase in the overall system throughput and reduction in delay. Specifically, research on multi-terminal 3C fusion has several main directions, which are as follows. (1) Multi-user modeling and collaborative optimization of 3C resources. In a multi-terminal collaboration scenario, the offloading of resources to other terminals via D2D communication increases the delay in data transmission. Therefore, it is necessary to study a trade-off between transmission, computing, and caching, i.e., the trade-off between the collaboration gain and overhead. (2) Adaptive collaborative optimization of 3C resources based on AI. For realizing the collaboration of caching, computing, and communication resources in a multi-terminal scenario, not only should the resources of a terminal be considered, but also the resources of other devices that are connected to the user. If the mobility of a terminal is high, then its connection with other terminals can be broken, resulting in dynamic changes in the caching, computing, and communication resources, which makes the configuration of resources more difficult. However, this problem can be solved using AI technology to predict terminal mobility from two perspectives, the spatial information and terminal context information, and then applying reallocation to 3C resources. (3) 3C collaborative optimization with a combination of multi-dimensional perception and AI. More data can be perceived by decentralizing the caching, computing, and communication to a mobile terminal. Specifically, two types of data, the business data and terminal data, can be perceived. The business data includes the data content, such as AR game or HD video, and type and size of the business data. The terminal data includes the communication ability, computing, and storage ability of a terminal device, as well as the context data of an end-user, including age, occupation, and social information. Considering that the optimization problem of the user's context information is generally non-convex and non-linear, using the traditional optimization scheme to solve it will result in high complexity and time consumption. Therefore, a method of reinforced deep learning can be adopted, and an offline deep neural network and on-line reinforced learning can be combined to obtain an optimal 3C resource allocation. (4) 3C offloading for multiple terminals. Through cooperation between terminals, the caching, computing, and communication resources of the terminals can be reasonably allocated to achieve a better service quality at the terminals. Since centralized algorithms consume more 3C resources, a distributed algorithm can be used to achieve optimal collaboration between terminals. However, a scheme for 3C fusion of a single terminal can be adopted in a distributed single-terminal computing offloading algorithm.