Information Flow Optimization in Inference Networks
Information Flow Optimization in Inference Networks
复制标题
推理网络中的信息流优化
DOI：
10.1109/icassp40776.2020.9053417
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Gunjan Verma
中科院分区：
文献类型：
--
作者：
Aditya Deshmukh;Jing Liu;V. Veeravalli;Gunjan Verma
最大限度地提高信息流通过传感器网络的任务与推理目标在融合中心的问题被认为是。传感器节点采集观测数据，压缩后通过中继网络发送到融合中心。网络对每个连接中的传输速率和流量守恒约束施加容量约束。结果表明，这种速率约束的推理问题，可以铸造作为一个网络效用最大化问题，通过适当地定义每个传感器的效用函数，并可以使用现有的技术来解决。两个实际的设置进行了分析：多终端参数估计和二元假设检验。通过仿真验证，使用所提出的配方给出了更好的推理性能比最大流的解决方案，简单地最大化的总比特率的融合中心。
The problem of maximizing the information flow through a sensor network tasked with an inference objective at the fusion center is considered. The sensor nodes take observations, compress and send them to the fusion center through a network of relays. The network imposes capacity constraints on the rate of transmission in each connection and flow conservation constraints. It is shown that this rate-constrained inference problem can be cast as a Network Utility Maximization problem by suitably defining the utility functions for each sensor, and can be solved using existing techniques. Two practical settings are analyzed: multi-terminal parameter estimation and binary hypothesis testing. It is verified via simulations that using the proposed formulation gives better inference performance than the Max-Flow solution that simply maximizes the total bit-rate to the fusion center.