In this paper we study multi-task oriented communication system via studying analog encoding method for multiple estimation tasks. The basic idea is to utilize the correlation among interested information required by different tasks and the feature of broadcast channel. For linear estimation tasks, we provide a low complexity algorithm for multi-user multi-task system based on orthogonal decomposition of subspaces. It is proved to be the optimal solution in some special cases, and for general cases, numerical results also show significant improvements over baseline methods. Further, we make a trial to migrate above method to neural networks based non-linear estimation tasks, and it also shows improvement in energy efficiency.

翻译：本文研究面向多任务导向通信系统，通过探索适用于多项参数估计任务的模拟编码方法。其核心思想在于利用不同任务所需感兴趣信息之间的相关性，以及广播信道的特性。针对线性估计任务，我们提出了一种基于子空间正交分解的低复杂度算法，适用于多用户多任务系统。该算法在某些特殊情况下被证明是最优解，而在一般情形下，数值结果也表明其相较于基准方法有显著改进。此外，我们尝试将上述方法迁移至基于神经网络的非线性估计任务，结果显示该方法在能效方面亦有提升。