Ultra-Reliable and Low-Latency Vehicular Transmission: An Extreme Value Theory Approach

TL;DR

This paper applies extreme value theory and Lyapunov stochastic optimization to develop power allocation strategies that enhance reliability and reduce latency in vehicular networks, focusing on controlling the maximal data queue length.

Contribution

It introduces a novel application of extreme value theory to characterize extreme queue events and proposes two queue-aware power control methods for vehicular networks.

Findings

Lower mean and variance of maximal queue length compared to baseline

Effective characterization of extreme events using extreme value theory

Enhanced reliability and latency performance in vehicular transmission

Abstract

Considering a Manhattan mobility model in vehicle-to-vehicle networks, this work studies a power minimization problem subject to second-order statistical constraints on latency and reliability, captured by a network-wide maximal data queue length. We invoke results in extreme value theory to characterize statistics of extreme events in terms of the maximal queue length. Subsequently, leveraging Lyapunov stochastic optimization to deal with network dynamics, we propose two queue-aware power allocation solutions. In contrast with the baseline, our approaches achieve lower mean and variance of the maximal queue length.