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

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%.

arXiv commentAccepted for publication in IEEE Communication Letters with 4 pages and 4 figures

References in corpus (4)

• Gaussian Processes for Nonlinear Signal Processing
• Ultra-Reliable and Low-Latency Vehicular Transmission: An Extreme Value Theory Approach
• Ultra-Reliable Low-Latency Vehicular Networks: Taming the Age of Information Tail
• Joint Information Freshness and Completion Time Optimization for Vehicular Networks

Cited by in corpus (4)

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• Intelligent Radio Signal Processing: A Survey