Knowledge Transfer based Radio and Computation Resource Allocation for 5G RAN Slicing

Applications of Machine Learning in Resource Management for RAN-Slicing in 5G and Beyond Networks: A Survey

“…Possible Solution: The use of parallel agents like A3C (e.g., [40], [120]) and the prior and acquired knowledge of other agents such as TRL (e.g., [141], [149], [150]) and federated learning (e.g., [144], [155]) can increase the speed of convergence of ML algorithms and the discovery of the environment. • Dynamic Slice Creation and Management: The reviewed proposals have not considered dynamic creation and management of slices in resource management.…”

“…So, these methods are not practically feasible in real-time applications with regard to the new architectures of RAN. In some proposals such as [40], [120], [136], [151], [133], [141], [147], [143], [153], [144], [145], [146], [149], [150], [155], [142], [154], [118], [152], [157] computational and time complexity have been considered as an important issue, but in other reviewed proposals this issue has not been accounted for RAN slicing. Therefore, given the changes in the network and responding to users' requests at an acceptable time, this is a vital challenge that must be considered.…”

“…In addition to radio and power resources, computational resources are also allocated in [149]. The authors of [149] have proposed a multi-agent method called knowledge transferbased resource allocation (KTRA) to allocate PRB and computational resources for two slices, uRLLC and eMBB, to minimize packet latency and task processing time. In this method, each BS acts as a DQN agent and exchanges its knowledge with other agents to increase the convergence speed and fast discovery of the environment.…”

“…Compared to [149], radio, power, and cache memory are allocated in [150]. In [150], a hierarchical and multi-agent method based on transfer RL (TRL) is presented for allocating the resources in multiple BSs of a RAN.…”

“…To increase the convergence speed and decrease the computational and time complexity in resource allocation methods in RAN slicing, the methods in V-D are presented. The exchange of acquired knowledge between different entities in the system in transfer learning increases the speed of convergence of resource allocation algorithms in RAN [141], [149], [150]. Also, the use of federated learning distributes the resource allocation algorithm between different entities in the RAN and reduces the computational and time complexity of the algorithm [144], [155].…”