AI 中文总结
本文针对多信道无线网络中吞吐量与信息年龄联合优化难题，提出TSDM两阶段亏空匹配调度框架，经理论证明与仿真验证，其性能显著优于现有调度策略。
AI 中文摘要
针对依赖多信道无线网络的遥感应用，同时优化低信息年龄（AoI）与高吞吐量至关重要，但在异构且不可靠信道的系统中，这一联合优化问题极具分析挑战性。为解决该问题，本文提出TSDM（两阶段亏空匹配调度框架），该框架基于二阶方法，通过均值与时序方差表征各数据流性能。第一阶段将高层效用最大化目标转化为各节点-信道对传输的目标均值与时序方差统计量集合；第二阶段采用低复杂度加权匹配亏空（WMD）规则执行实时信道分配。本文从理论上证明TSDM可实现各流所需的均值与时序方差，还针对两个公开的吞吐量-AoI联合优化问题开展大量仿真，结果显示TSDM在两种场景下均显著优于现有调度策略。
英文摘要
Optimizing for both low Age of Information (AoI) and high throughput is critical for remote sensing applications that rely on multichannel wireless networks. However, jointly optimizing these two metrics is an analytically challenging problem, particularly in systems with heterogeneous and unreliable channels. To address this challenge, we propose TSDM, a Two-Stage Deficit Matching scheduling framework. TSDM is based on a second-order approach that characterizes the performance of each data flow by its mean and temporal variance. In the first stage, TSDM translates the high-level utility maximization objective into a concrete set of target mean and temporal variance statistics for transmissions over each node-channel pair. In the second stage, a low-complexity Weighted Matching Deficit (WMD) rule performs real-time channel assignment. We theoretically prove that TSDM achieves the desired mean and temporal variance for each flow. Furthermore, we conduct extensive simulations on two open joint throughput-AoI optimization problems. In both cases, TSDM significantly outperforms existing scheduling policies.
