RAN Slicing in Multi-MVNO Environment under Dynamic Channel Conditions

TL;DR

This paper addresses the challenge of resource allocation in RAN slicing for multi-MVNO environments with dynamic channel conditions, proposing a greedy algorithm solution and analyzing its performance bounds.

Contribution

It introduces a MCS-aware RAN slicing model considering dynamic channels, formulates it as an NP-hard problem, and develops a greedy algorithm with performance bounds.

Findings

The greedy algorithm performs close to the upper bound in simulations.

Dynamic channel conditions significantly impact resource allocation efficiency.

The problem is NP-hard, indicating the complexity of optimal solutions.

Abstract

With the increasing diversity in the requirement of wireless services with guaranteed quality of service(QoS), radio access network(RAN) slicing becomes an important aspect in implementation of next generation wireless systems(5G). RAN slicing involves division of network resources into many logical segments where each segment has specific QoS and can serve users of mobile virtual network operator(MVNO) with these requirements. This allows the Network Operator(NO) to provide service to multiple MVNOs each with different service requirements. Efficient allocation of the available resources to slices becomes vital in determining number of users and therefore, number of MVNOs that a NO can support. In this work, we study the problem of Modulation and Coding Scheme(MCS) aware RAN slicing(MaRS) in the context of a wireless system having MVNOs which have users with minimum data rate requirement. Channel Quality Indicator(CQI) report sent from each user in the network determines the MCS selected, which in turn determines the achievable data rate. But the channel conditions might not remain the same for the entire duration of user being served. For this reason, we consider the channel conditions to be dynamic where the choice of MCS level varies at each time instant. We model the MaRS problem as a Non-Linear Programming problem and show that it is NP-Hard. Next, we propose a solution based on greedy algorithm paradigm. We then develop an upper performance bound for this problem and finally evaluate the performance of proposed solution by comparing against the upper bound under various channel and network configurations.