# 6G Cognitive Information Theory: A Mailbox Perspective
## 4 Distributed Intelligent Network
### Fusion of Edge Caching, Communication and Computing  
In recent years, edge-cloud computing has provided users with short-delay and high-performance computing services by deploying computing nodes or servers on the network edge to meet the computing requirements of delay-sensitive tasks. There are two main advantages of using the edge cloud. First, compared to local computing, the limited computing capacity of mobile devices can be overcome using edge-cloud computing. Second, compared to offloading computing on a remote cloud, in edge cloud computing, excessive delays caused by offloading task content on the remote cloud can be avoided. As a result, edge cloud computing commonly achieves a better trade-off for delay sensitive and computing-intensive tasks. In a MEC system, an increase in the number of user devices and diversity of applications lead to the exponential growth of mobile services. Therefore, the ability to effectively offload large amounts of raw data in a communication system is essential. However, the computing, storage, and communication capabilities of MEC nodes are limited, and thus the fusion and configuration of 3C edge resources should be considered to achieve an optimal offloading strategy. Currently, the main types of 3C edge fusion methods are; (1) Traditional pattern-recognition methods. In these methods, description and modeling are conducted based on 3C resources on the edge cloud and terminal devices, and optimization is conducted to perform the computing-offloading. (2) 3C edge fusion based on AI. Since AI can provide analysis, training, and learning abilities for making network-transmission decisions, machine learning (ML) can be applied to 3C fusion at an intelligent edge. In the following, several mainstream 3C edge fusion and offloading methods are briefly introduced. _(1) Traditional 3C edge fusion and optimization methods_: At present, existing studies on 3C edge-cloud fusion using the traditional modeling method focus on the four following aspects. (1) Content unloading or edge caching. Various caching strategies have been proposed to reduce delay and energy costs when a user obtains the request [29; 30]. (2) Computing offloading. The main design problem is to decide when and how to offload the user's tasks from the user's device to the edge cloud and which tasks should be offloaded from the user's device to the edge cloud to save energy and reduce computing delay [31]. For instance, in [32], the authors propose a task-scheduling scheme for edge-cloud computing when a user is mobile. (3) Mobile edge computing and offloading. The main concern is the deployment of edge clouds near base-stations [33] and design of optimal solutions to reduce energy costs and delays while simultaneously considering the communication and computing resources [34]. (4) 3C edge fusion and optimization. In [28], a new concept of computing task caching was proposed, where joint optimization of edge-cloud computing, caching, and communication resources was achieved, and an innovative caching scheme and an offloading scheme for computing tasks were developed. _(2) 3C edge fusion and offloading based on machine learning:_ To obtain an effective inference function from a labeled 3C resource data for training, statistical rules can be used in a 3C edge fusion method based on supervised learning [35]. Its purpose is to develop an analysis method that can predict output results based on the input 3C resource data. Supervised learning consists of two steps: learning and prediction. First, an analysis method is established by supervised learning, and then classifier parameters are optimized to obtain the optimal global prediction solution. The support vector machine (SVM) and support vector regression (SVR) are typical representatives of supervised learning algorithms, which are commonly used for discrete value classification and continuous value regression, respectively.