复旦大学数字信号处理与传输实验室的谢煊同学的论文Sampling Policy Design for Tracking Time-Varying Graph Signals with Adaptive Budget Allocation，被Asia-Pacific Signal and Information Processing AssociationAnnual Summit and Conference 2020 (APSIPA ASC 2020) 录用

论文摘要：There have been many works that focus on the sampling set design for a static graph signal, but few for time-varying graph signals (GS). In this paper, we concentrate on how to select vertices to sample and how to allocate the sampling budget for a time-varying GS to achieve a minimal tracking error for the long-term. In the Kalman Filter (KF) framework, the problem of sampling policy design and budget allocation is formulated as an infinite horizon sequential decision process, in which the optimal sampling policy is obtained by Dynamic Programming (DP). Since the optimal policy is intractable, an approximate algorithm is proposed by truncating the infinite horizon. By introducing a new tool for analyzing the convexity or concavity of composite functions, we prove that the truncated problem is convex. Finally, we demonstrate the performance of the proposed approach through numerical experiments.

论文作者：Xuan Xie, Hui Feng, Bo Hu