论文标题
增强大量MIMO CSI反馈的深度学习表现
Enhancing Deep Learning Performance of Massive MIMO CSI Feedback
论文作者
论文摘要
CSI反馈是大规模多输入多输出（MIMO）技术的重要问题，因为反馈开销与亚渠道的数量和天线数量成正比，这两种数量均与大型MIMO系统的大小相规。基于深度学习的CSI反馈方法由于其出色的性能而被广泛采用。尽管取得了成功，但当前的方法并未完全利用CSI数据的特征与深度学习框架之间的关系。在本文中，我们提出了一种拼图拼图帮助培训策略（JPTS），以通过最大程度地提高原始CSI和压缩CSI之间的相互信息来增强基于深度学习的大型MIMO CSI反馈方法。我们将JPT应用于现有的最新方法。实验结果表明，通过采用这种训练策略，在室内和室外环境中，精度平均可以提高12.07％和7.01％。提出的方法准备采用大量MIMO CSI反馈的现有深度学习框架。 JPT的代码可在GitHub上使用。
CSI feedback is an important problem of Massive multiple-input multiple-output (MIMO) technology because the feedback overhead is proportional to the number of sub-channels and the number of antennas, both of which scale with the size of the Massive MIMO system. Deep learning-based CSI feedback methods have been widely adopted recently owing to their superior performance. Despite the success, current approaches have not fully exploited the relationship between the characteristics of CSI data and the deep learning framework. In this paper, we propose a jigsaw puzzles aided training strategy (JPTS) to enhance the deep learning-based Massive MIMO CSI feedback approaches by maximizing mutual information between the original CSI and the compressed CSI. We apply JPTS on top of existing state-of-the-art methods. Experimental results show that by adopting this training strategy, the accuracy can be boosted by 12.07% and 7.01% on average in indoor and outdoor environments, respectively. The proposed method is ready to adopt to existing deep learning frameworks of Massive MIMO CSI feedback. Codes of JPTS are available on GitHub for use.
