Inter-slice resource management for 5G radio access network using markov decision process

The vision of the 5G network is to provide wireless connectivity to different market verticals with a diverse quality of service requirements. To meet the requirements of these verticals, network resources at each layer (core, transmission, and radio access) of 5G architecture need efficient resource management. Network slicing is one of the key features of 5G networks where network resources form virtual sub-networks to handle diverse resource requirements from verticals. In this paper, we propose a framework using multi-objective Markov decision process that models radio resource management (RRM) for 5G radio access network slices. In particular, we present a multi-objective scheduler for 5G radio that allocates inter-slice radio resources efficiently for enhanced mobile broadband (eMBB) and ultra-reliable low latency communication (uRLLC) slices. Probabilistic model checking is used to analyze the performance of the scheduler and to perform quantitative verification. The proposed scheduler takes into account key design parameters such as mmWave radio channel condition and network load condition to optimize the performance of bandwidth greedy eMBB and latency sensitive uRLLC slices through appropriate joint resource allocation. Results show that the proposed scheduler provides optimal strategy synthesis for joint resource management of shared radio bandwidth in eMBB and uRLLC slices .