Understanding the Fundamental Trade-Off Between Age of Information and Throughput in Unreliable Wireless Networks

TL;DR

This paper introduces a theoretical framework and practical scheduling policy to optimize the trade-off between throughput and Age of Information in unreliable wireless networks, validated through extensive simulations.

Contribution

It develops the throughput-AoI capacity region with bounds and proposes a low complexity policy that achieves optimal trade-offs, advancing joint throughput and freshness optimization.

Findings

The proposed policy outperforms conventional methods in simulations.

The framework accurately characterizes the feasible throughput-AoI pairs.

The policy achieves all interior points of the capacity region.

Abstract

This paper characterizes the fundamental trade-off between throughput and Age of Information (AoI) in wireless networks where multiple devices transmit status updates to a central base station over unreliable channels. To address the complexity introduced by stochastic transmission successes, we propose the throughput-AoI capacity region, which defines all feasible throughput-AoI pairs achievable under any scheduling policy. Using a second-order approximation that incorporates both mean and temporal variance, we derive an outer bound and a tight inner bound for the throughput-AoI capacity region. Furthermore, we propose a simple and low complexity scheduling policy and prove that it achieves every interior point within the tight inner bound. This establishes a systematic and theoretically grounded framework for the joint optimization of throughput and information freshness in practical wireless communication scenarios. To validate our theoretical framework and demonstrate the utility of the throughput-AoI capacity region, extensive simulations are implemented. Simulation results demonstrate that our proposed policy significantly outperforms conventional methods across various practical network optimization scenarios. The findings highlight our approach's effectiveness in optimizing both throughput and AoI, underscoring its applicability and robustness in practical wireless networks.