AoI-Aware Resource Allocation for Smart Multi-QoS Provisioning
Jingqing Wang, Wenchi Cheng, and Wei Zhang

TL;DR
This paper introduces a DRL-based resource allocation framework that optimizes Age of Information (AoI) in ultra-reliable low-latency communication systems, considering multi-QoS constraints and finite blocklength effects.
Contribution
It presents a novel DRL approach integrating AoI metrics with finite blocklength modeling for real-time resource allocation in mURLLC scenarios.
Findings
DRL algorithms effectively minimize AoI violation probabilities.
The framework ensures delay and error-rate constraints are met.
Simulation results validate the approach's efficiency.
Abstract
The Age of Information (AoI) has recently gained recognition as a critical quality-of-service (QoS) metric for quantifying the freshness of status updates, playing a crucial role in supporting massive ultra-reliable and low-latency communications (mURLLC) services. In mURLLC scenarios, due to the inherent system dynamics and varying environmental conditions, optimizing AoI under such multi-QoS constraints considering both delay and reliability often results in non-convex and computationally intractable problems. Motivated by the demonstrated efficacy of deep reinforcement learning (DRL) in addressing large-scale networking challenges, this work aims to apply DRL techniques to derive optimal resource allocation solutions in real time. Despite its potential, the effective integration of FBC in DRL-based AoI optimization remains underexplored, especially in addressing the challenge of…
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Taxonomy
TopicsIoT and Edge/Fog Computing
