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 design for multi-user multi-task system based on orthogonal decomposition of subspaces. It is proved to be optimal in some special cases, and for general cases, numerical results also show it can achieve near-optimal performance. 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：本文研究了面向多任务导向的通信系统，通过探讨面向多个估计任务的模拟编码方法。其基本思路是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设计，适用于多用户多任务系统。该设计在某些特殊情况下被证明是最优的，而在一般情况下，数值结果也表明其能够实现接近最优的性能。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，并发现该方法在能效方面也有所提升。