Low-Complexity Distributed Radio Access Network Slicing: Algorithms and Experimental Results

TL;DR

This paper presents low-complexity distributed algorithms for RAN slicing in 5G networks, modeling the problem as a congestion game, and demonstrates their effectiveness through simulations and real-world experiments.

Contribution

It introduces a novel congestion game model for RAN slicing, along with distributed algorithms that converge to a unique Nash equilibrium without privacy leaks.

Findings

Algorithms converge rapidly to the NE

Achieve near-optimal network performance

Pricing mechanism enhances network owner's profit

Abstract

Radio access network (RAN) slicing is an effective methodology to dynamically allocate networking resources in 5G networks. One of the main challenges of RAN slicing is that it is provably an NP-Hard problem. For this reason, we design near-optimal low-complexity distributed RAN slicing algorithms. First, we model the slicing problem as a congestion game, and demonstrate that such game admits a unique Nash equilibrium (NE). Then, we evaluate the Price of Anarchy (PoA) of the NE, i.e., the efficiency of the NE as compared to the social optimum, and demonstrate that the PoA is upper-bounded by 3/2. Next, we propose two fully-distributed algorithms that provably converge to the unique NE without revealing privacy-sensitive parameters from the slice tenants. Moreover, we introduce an adaptive pricing mechanism of the wireless resources to improve the network owner's profit. We evaluate the performance of our algorithms through simulations and an experimental testbed deployed on the Amazon EC2 cloud, both based on a real-world dataset of base stations from the OpenCellID project. Results conclude that our algorithms converge to the NE rapidly and achieve near-optimal performance, while our pricing mechanism effectively improves the profit of the network owner.