AI-Assisted Low Information Latency Wireless Networking

TL;DR

This paper introduces SMART, an AI-driven multi-agent reinforcement learning framework that optimizes information latency in wireless networks, significantly improving autonomous driving applications over traditional uRLLC systems.

Contribution

It proposes a novel AI-assisted approach focusing on information latency rather than conventional latency, enhancing wireless network control for autonomous driving.

Findings

SMART effectively reduces information latency in wireless networks.

Information latency-optimized systems outperform uRLLC in autonomous driving tasks.

The approach demonstrates significant improvements in traffic efficiency.

Abstract

The 5G Phase-2 and beyond wireless systems will focus more on vertical applications such as autonomous driving and industrial Internet-of-things, many of which are categorized as ultra-Reliable Low-Latency Communications (uRLLC). In this article, an alternative view on uRLLC is presented, that information latency, which measures the distortion of information resulted from time lag of its acquisition process, is more relevant than conventional communication latency of uRLLC in wireless networked control systems. An AI-assisted Situationally-aware Multi-Agent Reinforcement learning framework for wireless neTworks (SMART) is presented to address the information latency optimization challenge. Case studies of typical applications in Autonomous Driving (AD) are demonstrated, i.e., dense platooning and intersection management, which show that SMART can effectively optimize information latency, and more importantly, information latency-optimized systems outperform conventional uRLLC-oriented systems significantly in terms of AD performance such as traffic efficiency, thus pointing out a new research and system design paradigm.