In many modern applications, large-scale sensor networks are used to perform statistical inference tasks. In this paper, we propose Bayesian methods for multiple change-point detection using a sensor network in which a fusion center (FC) can receive a data stream from each sensor. Due to communication limitations, the FC monitors only a subset of the sensors at each time slot. Since the number of change points can be high, we adopt the false discovery rate (FDR) criterion for controlling the rate of false alarms, while minimizing the average detection delay (ADD). We propose two Bayesian detection procedures that handle the communication limitations by monitoring the subset of the sensors with the highest posterior probabilities of change points having occurred. This monitoring policy aims to minimize the delay between the occurrence of each change point and its declaration using the corresponding posterior probabilities. One of the proposed procedures is more conservative than the second one in terms of having lower FDR at the expense of higher ADD. It is analytically shown that both procedures control the FDR under a specified tolerated level and are also scalable in the sense that they attain an ADD that does not increase asymptotically with the number of sensors. In addition, it is demonstrated that the proposed detection procedures are useful for trading off between reduced ADD and reduced average number of observations drawn until discovery. Numerical simulations are conducted for validating the analytical results and for demonstrating the properties of the proposed procedures.
Bayesian Inverse Contextual Reasoning for Heterogeneous Semantics-Native Communication
Kwangwoon University（光云大学）；Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院（KAIST））；University of Oulu（奥卢大学）；Seoul National University (SNU)（首尔国立大学（SNU））
Low-Complexity Channel Estimation for Internet of Vehicles AFDM Communications With Sparse Bayesian Learning
Northwestern Polytechnical University（西北工业大学）；Shaanxi University of Science and Technology（陕西科技大学）；Nanyang Technological University（南洋理工大学）；South China University of Technology（华南理工大学）；Xidian University（西安电子科技大学）
Online Learning of Modular Bayesian Deep Receivers: Single-Step Adaptation with Streaming Data
School of ECE, Ben-Gurion University of the Negev（内盖夫本-古里安大学电气与计算机工程学院）；Institute for Intelligent Networked Systems, Northeastern University London（伦敦东北大学智能网络系统研究所）
