Rapid advancements in the development of smart terminals and infrastructure, coupled with a wide range of applications with complex requirements, are creating traffic demands that current networks may not be able to fully handle. Accordingly, the study of 6G networks deserves attention from both industry and academia. Artificial intelligence (AI) has emerged for application in the optimization and design process of new 6G networks. The developmental trend of 6G is towards effective resource management, along with the architectural improvement of the current network and hardware specifications. Cloud RAN (CRAN) is considered one of the major concepts in sixth- and fifth-generation wireless networks, being able to improve latency, capacity, and connectivity to huge numbers of devices. Besides bettering the current set-up in terms of setting the carriers’ network architecture and hardware specifications, among other potential enablers, the developmental trend of 6G also means that there must be effective resource management. As a result, this study covers a thorough analysis of resource management plans in CRAN, optimization, and AI taxonomy, and how AI integration might enhance existing resource management.

The efficiency of the current cellular network is limited due to the imbalance between resource availability and traffic demand. To overcome these limitations, baseband units (BBUs) are deployed on virtual machines (VMs) to form a virtual pool of BBUs. This setup enables the pooling of hardware resources, reducing the costs associated with building base stations (BSs) and simplifying both management and control. However, extreme levels of server resource use within the pool can increase physical maintenance costs and impact virtual BBU performance. This study introduces an adaptive, threshold-based dynamic migration strategy for virtual BBUs within the iCanCloud framework by setting upper and lower limits on the servers’ resource usage in the pool. The proposed method determines whether to initiate a migration by evaluating resource usage on each compute node and identifies the target node for migration if required. This aims to balance server load and cut energy consumption, and also to avoid unnecessary migration because of too high or too low server load, and effectively determine the time to trigger migration and not depend only on a certain instantaneous peak of server resource utilization. This paper used a deep Q-network learning method to predict resource utilization and make an accurate migration decision based on a history dataset. Experimental results show that as compared with Kalman filter prediction and other traditional methods, this model can effectively lower the cost of VM migration by decreasing the migration time and occurrence of it to enhance overall performance while reducing energy consumption.