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.

翻译：CSI反馈是大规模多输入多输出（MIMO）技术中的一个重要问题，因为反馈开销与子信道数量和天线数量成正比，而这两者均随大规模MIMO系统规模扩大而增长。基于深度学习的CSI反馈方法因其优越性能近年来被广泛采用。尽管取得了成功，现有方法尚未充分利用CSI数据特征与深度学习框架之间的关系。本文提出了一种拼图辅助训练策略（JPTS），通过最大化原始CSI与压缩CSI之间的互信息来增强基于深度学习的大规模MIMO CSI反馈方法。我们将JPTS应用于现有最先进方法之上。实验结果表明，采用该训练策略后，室内和室外场景的平均准确率分别提升了12.07%和7.01%。所提方法可直接适配于现有大规模MIMO CSI反馈的深度学习框架。JPTS的代码已在GitHub上开源供使用。