论文标题
免费的免费轻量级网络免费：代码字模拟学习，用于大规模MIMO CSI反馈
Better Lightweight Network for Free: Codeword Mimic Learning for Massive MIMO CSI feedback
论文作者
论文摘要
频道状态信息（CSI）需要从用户设备（UE）回馈到频划分双工（FDD）多输入多输出（MIMO）系统中的基站（BS）。最近，神经网络被广泛应用于CSI压缩反馈，因为原始开销对于庞大的MIMO系统太大。值得注意的是，轻巧的反馈网络由于部署的实用性而引起了特别的关注。但是，反馈精度可能会受到网络压缩的损害。在本文中，提出了一种名为CodeWord Mimic（CM）的免费成本蒸馏技术，以使用实用的轻量级编码来训练更好的反馈网络。具有特殊蒸馏计划器的模拟探索培训策略旨在增强CM学习。实验表明，所提出的CM学习的表现优于先前的最新反馈蒸馏方法，从而提高了轻量级反馈网络的性能而没有任何额外的推理成本。
The channel state information (CSI) needs to be fed back from the user equipment (UE) to the base station (BS) in frequency division duplexing (FDD) multiple-input multiple-output (MIMO) system. Recently, neural networks are widely applied to CSI compressed feedback since the original overhead is too large for the massive MIMO system. Notably, lightweight feedback networks attract special attention due to their practicality of deployment. However, the feedback accuracy is likely to be harmed by the network compression. In this paper, a cost free distillation technique named codeword mimic (CM) is proposed to train better feedback networks with the practical lightweight encoder. A mimic-explore training strategy with a special distillation scheduler is designed to enhance the CM learning. Experiments show that the proposed CM learning outperforms the previous state-of-the-art feedback distillation method, boosting the performance of the lightweight feedback network without any extra inference cost.
