Deep reinforcement learning based flexible preamble allocation for RAN slicing in 5G networks

Abstract

One of the most difficult challenges in radio access network slicing occurs in the connection establishment phase where multiple devices use a common random access channel in order to gain access to the network. It is now very well known that random access channel congestion is a serious issue in case of sporadic arrival of machine-to-machine nodes and may result in a significant delay for all nodes. Hence, random access channel resources are also needed to be allocated to different services to enable random access network slicing. In the random access channel procedure, the nodes transmit a selected preamble from a predefined set of preambles. If multiple nodes transmit the same preamble at the same random access channel opportunity, a collision occurs at the eNodeB. To isolate the one service class from others during this phase, one approach is to allocate different preamble subsets to different service classes. This research proposes an adaptive preamble subset allocation method using deep reinforcement learning. The proposed method can distribute preambles to different service classes according to their priority providing virtual isolation for service classes. The results indicate that the proposed mechanism can quickly adapt the preamble allocation according to the changing traffic demands of service classes.