# The Framework of 6G Self-Evolving Networks and the Decision-Making Scheme for Massive IoT
## 1 Introduction
Various standardization organizations play extremely important roles in the promotion of 5G network intelligence. 3 GPP SA2 established the research project \"Study of enablers for Network Automation for 5G (eNA)\" and enhanced the network data analytic functions (NWDAF) of the 5G core network in May 2017 [8]. In February 2017, the European Telecommunications Standards Institute (ETSI) also formally approved the establishment of a new industry standard group, \"Experiential Networked Intelligence (ENI)\" [9]. ITU-T established the \"Machine Learning for Future Networks including 5G (FG-ML5G)\" working group during the SG13 meeting held in November 2017. Moreover, there are many studies about the integration of AI and 5G in academia, which focus on the intelligent resource allocation [10; 11; 12; 13; 14; 15; 16], intelligent network slicing [17; 18; 19; 20; 21; 22], intelligent network operation and management [23; 24; 25; 26; 27; 28], etc. From the above survey, we \begin{table}
\begin{tabular}{l l l} \hline \hline
**KPIs** & **5G** & **6G** \\ \hline Peak Rate & 20 Gbps & \(\geq\)1 Tbps \\ User-Experience Rate & 0.1 Gbps & \(\geq\)1 Gbps \\ Peak Spectrum Efficiency & 30 bit/s/Hz & \(\geq\)60 bit/s/Hz \\ User-Experience Spectrum Efficiency & 0.3 bit/s/Hz & \(\geq\)3 bit/s/Hz \\ Connectivity Density & 10\({}^{6}\) devices/km\({}^{2}\) & \(\geq\)10\({}^{6}\) devices/km\({}^{2}\) \\ Air-Interface Latency & 1 ms & \(\leq\)0.1 ms \\ Mobility & 500 km/h & \(\geq\)1000 km/h \\ Energy Efficiency & 1\(\times\) & 10–1000\(\times\) \\ Computing Power & - & to be defined \\ Reliability Level & - & to be defined \\ Intelligence Level & - & to be defined \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative comparison of the KPIs between 5G and 6G. can find that the integration of AI and 5G just focuses on applying AI techniques in the 5G system to enhance some network functions and only achieve partial network intelligence to a certain extent, since the wireless network environment is constantly changing, and the deployed AI components need to be continuously trained and updated, which will hinder the intelligentization of the network. It is expected that AI will be embedded in future networks. In a word, the 6G system will undergo unprecedented innovations compared to the 5G system. AI abilities are expected to be endogenous in the 6G system. Table 2 gives the differences of AI abilities in 5G and 6G. The 6G network with the endogenous intelligence can realize autonomous sensing, autonomous decision-making, and autonomous control, which can better satisfy the service requirements of the massive IoT scenarios. In terms of intelligent networking for 6G, much research has also been conducted. For example, Huawei proposed the intent-driven network, which was expected to adjust the network configuration based on the prediction of the user intent [29]. [PERSON] et al. [30] proposed a distributed and autonomous network architecture for 6G, which was enabled by pervasive distributed intelligence. [PERSON] et al. [31] proposed an extreme-intelligent and extreme-concise system architecture of radio access networks to fulfill the requirements of ultra-high data rates and ultra-low latency in 6G networks. [PERSON] et al. [32] further introduced the concept of 6G endogenous intelligence networks which was driven by artificial intelligence and discussed the characteristics and the key technologies of the endogenous intelligence networks. [PERSON] et al. [33] presented an AI-enabled intelligent architecture for 6G, which consisted of the following four layers: sensing layer, data mining and analytics layer, control layer, and application layer. Furthermore, the proposed architecture can realize smart resource management, automatic network adjustment and intelligent service provisioning with a high level of intelligence. Considering the typical scenarios in 6G, [PERSON] et al. [34] proposed the deep-learning-based path selection to optimize the performance of Space-Air-Ground integrated networks. [PERSON] et al.