单比特和降维扩散策略在分布式网络中的应用
Single Bit and Reduced Dimension Diffusion Strategies Over Distributed Networks
AI总结：
本文提出了一种基于扩散的自适应估计策略，通过降低通信负载实现与全信息交换相媲美的性能。
摘要：
本文提出了一种基于扩散的自适应估计策略，通过降低通信负载实现与全信息交换相媲美的性能。在每个节点生成所需数据的局部估计后，使用特定的随机投影生成单比特信息或降维数据向量。该新生成的数据被扩散并在相邻节点中用于恢复原始完整信息。我们提供了算法的完整状态空间描述和均值稳定性分析。
英文摘要：
We introduce novel diffusion based adaptive estimation strategies for distributed networks that have significantly less communication load and achieve comparable performance to the full information exchange configurations. After local estimates of the desired data is produced in each node, a single bit of information (or a reduced dimensional data vector) is generated using certain random projections of the local estimates. This newly generated data is diffused and then used in neighboring nodes to recover the original full information. We provide the complete state-space description and the mean stability analysis of our algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
