Artificial Intelligence Powered Mobile Networks: From Cognition to Decision

TL;DR

This paper explores how artificial intelligence can enhance mobile networks by enabling better understanding and decision-making, addressing complexity challenges, and demonstrating a deep learning approach that improves quality of service based on real-world data.

Contribution

It proposes an AI-powered architecture for mobile networks, discusses associated challenges, and introduces a deep learning method to directly map network states to QoS, improving decision-making.

Findings

Deep learning approach effectively maps network state to QoS.

Proposed method improves decision-making for network operators.

Validated on real-world dataset with 31,261 users over 77 stations.

Abstract

Mobile networks (MN) are anticipated to provide unprecedented opportunities to enable a new world of connected experiences and radically shift the way people interact with everything. MN are becoming more and more complex, driven by ever-increasingly complicated configuration issues and blossoming new service requirements. This complexity poses significant challenges in deployment, management, operation, optimization, and maintenance, since they require a complete understanding and cognition of MN. Artificial intelligence (AI), which deals with the simulation of intelligent behavior in computers, has demonstrated enormous success in many application domains, suggesting its potential in cognizing the state of MN and making intelligent decisions. In this paper, we first propose an AI-powered mobile network architecture and discuss challenges in terms of cognition complexity, decisions with high-dimensional action space, and self-adaption to system dynamics. Then, potential solutions that are associated with AI are discussed. Finally, we propose a deep learning approach that directly maps the state of MN to perceived QoS, integrating cognition with the decision. Our proposed approach helps operators in making more intelligent decisions to guarantee QoS. Meanwhile, the effectiveness and advantages of our proposed approach are demonstrated on a real-world dataset, involving $31261$ users over $77$ stations within $5$ days.