High-dimensional identification systems consisting of two groups of users in the presence of statistical uncertainties are considered in this work. The task is to design enrollment mappings to compress users’ information and an identification mapping that combines the stored information in the database and an observation to estimate the underlying user index. The compression-identification trade-off regions are established for the compound, extended compound, general and mixture settings. It is shown that several settings admit the same compression-identification trade-offs. We then study a connection between the Wyner-Ahlswede-Körner network and the identification setting. It indicates that a strong converse for the WAK network is equivalent to a strong converse for the identification setting. Finally, we present strong converse arguments for the discrete identification setting that are extensible to the Gaussian scenario.

中文翻译：

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识别系统的不确定性

在这项工作中考虑了由两组用户组成的高维识别系统，存在统计不确定性。任务是设计注册映射以压缩用户信息，以及标识映射，该映射将数据库中存储的信息与观察值结合起来以估计基础用户索引。为化合物，扩展化合物，常规和混合物设置建立了压缩识别权衡区域。可以看出，几个设置都接受了相同的压缩标识折衷。然后，我们研究Wyner-Ahlswede-Körner网络与标识设置之间的连接。它表示WAK网络的强逆等效于标识设置的强逆。最后，