发表机构
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对与两个闲聊式接收端通信的发射器，构建MDP模型优化VAoI、采样与传输成本，通过RVI得出最优策略结构，验证其性能优于基线方法。
AI 中文摘要
本文研究发射器与两个可相互共享信息的闲聊式接收端通信时的最优联合采样与传输策略，目标是在版本信息年龄（VAoI）度量下跟踪源节点。发射器可观测源版本变化，但需支付采样成本获取当前源信息内容；同样，发射器与接收端通信需支付传输成本。闲聊机制支持本地信息交换，可减少高成本的直接传输。在通信链路不完善的情况下，本文构建无限时域平均成本马尔可夫决策过程（MDP），以联合最小化接收端VAoI、采样成本与传输成本。本文使用相对值迭代（RVI）评估最优策略，并确定其结构的若干性质：证明发射器VAoI的采样具有阈值结构；在直接传输中，为较旧的接收端服务是最优的；进一步通过接收端VAoI差值刻画传输或空闲决策。分析表明，链路可靠性与接收端VAoI失衡对最优策略结构有显著影响。数值结果验证了上述结构性质，并证明最优策略相较于多个基线方法的性能增益。
英文摘要
We study the optimal joint sampling and transmission policy for a transmitter communicating with two gossiping receivers that share information with each other, with the objective of tracking a source under the Version Age of Information (VAoI) metric. The transmitter can observe source-version changes, but it has to pay a sampling cost to get the current source information content. Similarly, it can communicate with a receiver by paying a transmission cost. Gossiping enables local information exchange and is able to reduce costly direct transmissions. With imperfect communication links, we formulate an infinite-horizon average-cost Markov Decision Process (MDP) to jointly minimize receiver VAoI, sampling cost, and transmission cost. Using Relative Value Iteration (RVI), we evaluate the optimal policy and establish several properties of its structure. We prove that sampling has a threshold structure in the transmitter VAoI. Among direct transmissions, it is optimal to serve the older receiver. We further characterize the transmit or idle decision through the receiver VAoI difference. Our analysis shows that link reliability and receiver VAoI imbalance have a significant effect on the optimal policy structure. Numerical results verify the structural properties and demonstrate the performance gains of the optimal policy over multiple baselines.
CommentsTo appear in IEEE ITW 2026
