论文标题
多用户CSI反馈的多任务学习
Multi-Task Learning for Multi-User CSI Feedback
论文作者
论文摘要
近年来，基于深度学习的大规模MIMO CSI反馈受到了很多关注。现在，存在大量的CSI反馈模型，主要是基于自动编码器（AE）体系结构，该体系结构在用户设备（UE）的编码器网络（UE）和GNB（基地站）的解码器网络。但是，这些模型是在单渠道方案中为单个用户培训的，使它们在多用户方案中无效，并且在整个用户中具有不同的频道和不同的编码模型。在这项工作中，我们通过在大量MIMO CSI反馈的背景下利用多任务学习（MTL）的技术来解决这个问题。特别是，我们提出的方法是在多用户设置中共同训练现有模型的同时提高某些组成模型的性能。例如，通过我们提出的方法，在与STNET一起培训时，CSINET的性能提高了39美元，同时将系统的总和增加了0.07bps/hz $。
Deep learning-based massive MIMO CSI feedback has received a lot of attention in recent years. Now, there exists a plethora of CSI feedback models mostly based on auto-encoders (AE) architecture with an encoder network at the user equipment (UE) and a decoder network at the gNB (base station). However, these models are trained for a single user in a single-channel scenario, making them ineffective in multi-user scenarios with varying channels and varying encoder models across the users. In this work, we address this problem by exploiting the techniques of multi-task learning (MTL) in the context of massive MIMO CSI feedback. In particular, we propose methods to jointly train the existing models in a multi-user setting while increasing the performance of some of the constituent models. For example, through our proposed methods, CSINet when trained along with STNet has seen a $39\%$ increase in performance while increasing the sum rate of the system by $0.07bps/Hz$.
