Ultra-Reliable and Low-Latency Vehicular Communication: An Active Learning Approach

TL;DR

This paper introduces an active learning method using Gaussian process regression for resource allocation in vehicular networks, significantly reducing AoI violations by adaptively learning network dynamics.

Contribution

It proposes a novel active learning approach for resource allocation in vehicular networks that accounts for network dynamics and AoI, improving reliability and latency.

Findings

At least 50% reduction in AoI violation probability.

Effective online decentralized learning of network dynamics.

Improved resource allocation performance over baselines.

Abstract

In this letter, an age of information (AoI)-aware transmission power and resource block (RB) allocation technique for vehicular communication networks is proposed. Due to the highly dynamic nature of vehicular networks, gaining a prior knowledge about the network dynamics, i.e., wireless channels and interference, in order to allocate resources, is challenging. Therefore, to effectively allocate power and RBs, the proposed approach allows the network to actively learn its dynamics by balancing a tradeoff between minimizing the probability that the vehicles' AoI exceeds a predefined threshold and maximizing the knowledge about the network dynamics. In this regard, using a Gaussian process regression (GPR) approach, an online decentralized strategy is proposed to actively learn the network dynamics, estimate the vehicles' future AoI, and proactively allocate resources. Simulation results show a significant improvement in terms of AoI violation probability, compared to several baselines, with a reduction of at least 50%.