AoI, Timely-Throughput, and Beyond: A Theory of Second-Order Wireless Network Optimization

TL;DR

This paper develops a second-order theoretical framework for wireless network optimization, capturing mean and variance to better model fading channels and metrics like AoI and timely-throughput, leading to improved scheduling policies.

Contribution

It introduces a novel second-order framework for wireless network optimization that incorporates temporal variance, enabling more accurate modeling and optimization of complex performance metrics.

Findings

Characterizes the second-order capacity region of wireless networks.

Proposes a simple scheduling policy achieving all interior points of the capacity region.

Demonstrates improved performance over existing methods in AoI and timely-throughput optimization.

Abstract

This paper introduces a new theoretical framework for optimizing second-order behaviors of wireless networks. Unlike existing techniques for network utility maximization, which only consider first-order statistics, this framework models every random process by its mean and temporal variance. The inclusion of temporal variance makes this framework well-suited for modeling Markovian fading wireless channels and emerging network performance metrics such as age-of-information (AoI) and timely-throughput. Using this framework, we sharply characterize the second-order capacity region of wireless access networks. We also propose a simple scheduling policy and prove that it can achieve every interior point in the second-order capacity region. To demonstrate the utility of this framework, we apply it to an unsolved network optimization problem where some clients wish to minimize AoI while others wish to maximize timely-throughput. We show that this framework accurately characterizes AoI and timely-throughput. Moreover, it leads to a tractable scheduling policy that outperforms other existing work.