# The Framework of 6G Self-Evolving Networks and the Decision-Making Scheme for Massive IoT
## 3 The Distributed Task Offloading Scheme for Massive IoT
### System Model
#### 3.1.4 Problem Formulation
Because of the limited computing power of our personal computer, the number of the users is set as much smaller than the the actual massive IoT scenarios, but we can obtain the same conclusion as above because the performance of the total QoE utility is not affected by the number of users. Moreover, in the actual execution of the proposed decision-making scheme, the centralized training is completed in the control center, and the terminal devices can realize the real-time decision-making once the training is complete. ## 4 Use Cases

The proposed edge-based self-evolution framework and the autonomous decision-making algorithm can be well applied in many massive IoT scenarios. In this section, we will illustrate the application in the super-smart vehicle as an example. Super-smart vehicle is one of the typical application scenarios of massive IoT in the 6G era, which is the upgrade of the current autonomous vehicles. Compared to the self-driving cars in the 5G era, super-smart vehicles will be more intelligent, which is reflected in the following aspects: (1) more diverse means of transportation will be used; (2) it is expected to realize point-to-point smart travel. On the other hand, the devices are always moving and the network environment is constantly changing, so it is necessary to embed the AI functions in 6G networks to realize the whole network intelligence. Since the devices in super-smart vehicles have the features of high mobility and flexibility and the requirements for super-smart vehicles are constantly changing, it is necessary for the network to offer a flexible framework to support super-smart vehicles. By deploying AI components on the terminal devices and in the network, the super-smart vehicle systems have the abilities to autonomously sense the network environment (the states of cars, roads, people, etc. ); to collect and process the traffic information, vehicle information and environmental information; and to analyze and predict traffic conditions, so that they can autonomously and quickly make decisions and control the transportation, which are fully supported by the proposed 6G self-evolving network. According to [48], the current network intelligence level is at L2-L3, and in future work, we will continue to discuss the application of the proposed 6G self-evolving network in super-smart vehicles

Figure 3: The comparison of QoE performance among different algorithms. and research the key technologies to realize the network intelligence level at L3-L4, and we expect to give the corresponding simulation results to prove it. ## 5 Conclusions and Future Work

In order to realize the future intelligent wireless network to well satisfy the service requirements of the massive IoT application scenarios, it is necessary to embed the essential capabilities of AI into the wireless system. In this paper, we first propose the edge-computing-based self-evolution framework for 6G massive IoT, which is expected to realize autonomous sensing, autonomous decision-making, and autonomous configuration. Autonomous decision-making is also one of the crucial parts in the proposed 6G self-evolution framework, so we introduce the decision-making mechanism and analyze the pros and cons of the centralized decision-making schemes and the distributed decision-making schemes. Then, we consider the task offloading problem and propose the distributed D3 QN-based algorithm. The simulation results show that the proposed D3 QN-based scheme can converge faster than the DQN- and DDQN-based algorithm and can acquire the better performance in terms of QoE of users compared with Q-learning and DQN algorithms. Finally, there is still much research to be done in the future. For example, this paper concentrated on centralized training and distributed decision-making. As the network and devices for massive IoT are becoming increasingly dense, the complexity of centralized training will get higher and higher.