# 6G Cognitive Information Theory: A Mailbox Perspective
## 4 Distributed Intelligent Network
### Fusion of Edge Caching, Communication and Computing
In the 3C edge fusion method based on unsupervised learning [36], a label of input data is unknown, and the goal is to determine features and structures hidden in the data to achieve prediction and reasoning functions. One of the most widely used unsupervised learning algorithms is the K-means algorithm, which attempts to divide data of unknown classes into several disjoint clusters. These methods are relatively simple and suitable for practical applications, but their performances are dependent on training data. For instance, mobile users can form clusters based on location, service requirements, available resources, and other functions. The MEC server selection and offloading decisions are made by clusters rather than individuals, which can significantly reduce the number of participants more effectively. (3) 3C _edge fusion and offloading based on deep learning:_ Deep learning is a representation (or feature) learning method based on a multi-layer neural network that allows computing models to automatically extract features required for prediction or classification from a large amount of raw data. By using multilayer concatenation for feature extraction and transformation, deep learning can discover complex structures and learn hidden features from a large amount of the original data. However, deep learning is regarded as a black box, so certain training skills and experience are needed in the practical model training because there is no complete theoretical guidance for model training. It is necessary to use computing offloading of an edge cloud in a 3C fusion scheme based on deep learning because a large amount of data is necessary for training with complicated computing, and a MEC server can provide available computing resources and raw data [37]. (4) 3C _edge fusion and offloading based on reinforcement learning:_ Reinforcement learning focuses on how to learn from own experience and chooses the best behavior through continuous interactions with the system environment [38]. The Markov decision-making process is a simple reinforced learning method that can be used for offloading decisions in a stochastic dynamic environment, such as the decision on whether to offload and which MEC servers to select. When considering mobile users, their energy can be saved, and their computing power can be enhanced by using offloading. However, the additional overhead caused by the allocated workload transmission and computing resource consumption should also be considered. Therefore, the first query should be whether to offload. If the decision is yes, then an appropriate MEC server should be selected, and the workload for offloading should be determined [39]. (5) 3C _edge fusion and offloading based on deep reinforcement learning:_ Traditional reinforcement learning requires handcrafted features to learn about optimal decision making, as well as low dimensional state spaces. However, with the implementation of deep learning, useful features can be directly extracted by learning high-dimensional raw data. Thus, reinforced deep learning can be used to make optimal decisions in the context of real-world complexity. The Deep Q network was proposed as a typical form of reinforced deep learning [40]. By using end-to-end reinforced learning and deep learning, the deep Q network can directly learn about the optimal decisions from high-dimensional raw data. Thus, reinforced deep learning can be used to learn successful strategies directly from higher-dimensional raw data generated from edge networks without handcrafting features. It can acquire real-time transmission data for training within the edge network. This computing-intensive learning method can be executed in an edge-cloud server to expedite the training process.