发表机构
University of Texas at Austin（德克萨斯大学奥斯汀分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对带边信息的部分可观测线性系统，以LQG装置与高斯-马尔可夫源为对象，证明线性策略可优化条件定向信息下界，对应标量情况的优化问题为凸，结果推广了过往相关研究，数值仿真验证了边信息的作用。
AI 中文摘要
本文研究带有边信息的部分可观测线性系统所需的最小速率，考虑线性二次高斯（Linear Quadratic Gaussian, LQG）装置与高斯-马尔可夫源。我们证明一类线性策略足以优化条件定向信息下界；还证明对于标量情况，时变和时不变系统的优化问题均为凸问题。我们的结果推广了仅考虑全观测或部分观测，以及全观测与边信息结合的过往研究。数值仿真用于说明边信息对部分可观测系统的影响。
英文摘要
This paper studies the minimum rate required for a partially observable linear system with side information. The Linear Quadratic Gaussian(LQG) plant and the Gaussian-Markov source are considered. We show that a class of linear policies is sufficient for optimizing the conditional directed information lower bound. We also show that the resulting optimization problem is convex for the scalar case in both time-varying and time-invariant systems. Our results generalize the past works that consider the case with full or partial observation only, and the case with full observation and side information. Numerical simulations are presented to illustrate the effect of side information for partially observable systems.
Commentsaccepted for CDC 2026, full version, modified a few typos, polished more
