# 6G Cognitive Information Theory: A Mailbox Perspective
## 4 Distributed Intelligent Network
### Communication, Computing and Caching Integration  
The main question that arises is how to understand the mailbox theory. According to the aforementioned discussions, the mailbox theory should be applied to the entire communication system. To simplify this explanation, consider the mailbox theory from the aspect of terminals and communication, computing, and caching (3C) integration, as shown in Figure 3. First, for terminal 3C integration, algorithms shall be embedded into terminals and terminal business data. To be specific, for intelligent terminal services, key performance indexes include capacity, latency, reliability, link count, and cost-effectiveness. The user service quality refers to the user's satisfaction at the network level. Such satisfaction is associated with the realization status of the user's expectation on a business, the user's personal preference and environment, and the business itself. Specifically, a model between the user's experience (service quality) and the network KPIs can be expressed as follows: \(QoE=f(KPI_{1},\cdots,KPI_{n})\), where \(f\) denotes a non-linear function that can be solved using AI algorithms. The heterogeneity of services should also need to be considered, which means that different services require different storage capacities and computing resources. For instance, for simple push notifications, it is only needed to consider the communicating and computing abilities of a user's device. However, in virtual reality (VR) games, the communicating, computing, and storing abilities of devices are all critical. Thus, a model between service KPIs and communicating, computing, and caching resources should be built. For services, such as augmented reality (AR) games, the demands of services (i.e., the demand of the service for 3C resources) can be expressed as \(Q=(\omega,s,o)\), where \(\omega\) denotes the required computation amount, \(s\) denotes the required transmission amount, and \(o\) represents the caching amount. For instance, earlier caching can be made for scene rendering of VR games. Thus, modeling between service KPIs and 3C can be expressed as \(KPI=f(Q)\), hence the relation between the KPI and 3C can be obtained. However, the  
Figure 3: Distributed intelligent network based on communication, computing, and caching integration. theories for explaining the integration of communicating, computing, and caching are yet to be determined. The information theory should explain the integration of terminal 3C businesses and limits of communicating, computing, and caching resources. In the field of wireless-communication systems, network optimization problems have been extensively studied using an appropriate network configuration or a method of maximizing system performance. The network-optimization problem involves a wide range of wireless network-related research. Typical applications include resource allocation and management, system reconfiguration, task scheduling, and user QoS optimization. As shown in Figure 4, a typical network-optimization process in a wireless-communication system includes the five following steps:  
* _Network-resource perception_. The transmission data fused in the network-optimization process includes: communication resources, such as channel state information, interference, noise, user or sensor position, spectrum, and time slice occupancy information; QoS information, such as delay and energy consumption rate and mobile state; computing resources; and storage resources at the node, edge cloud, remote cloud, and so on;
* _Network-status analysis_. The optimization objective and model to be adopted can be determined by analyzing the perceived network-resource data. Currently, the most widely used analytical methods include human analysis and intervention, and automatic analysis and prediction based on AI. However, artificial analysis by domain experts is both costly and inefficient.