发表机构
Zhejiang University; Inha University; Singapore University of Technology and Design（浙江大学; 仁荷大学; 新加坡科技设计大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对大规模MIMO CSI反馈开销大且现有方法未利用低秩结构的问题，提出LRP框架及DCRNetV2，通过可学习秩一感知与合成实现高效压缩重建，在精度与复杂度间取得更优权衡。
AI 中文摘要
下行信道状态信息（CSI）反馈对于频分双工大规模MIMO至关重要，然而反馈开销随天线和子载波数量的增加而迅速增长。为降低该开销，大多数深度学习方法将CSI视为通用图像进行压缩，未利用其低秩多径结构。相比之下，模型驱动替代方案在迭代恢复中显式嵌入该结构，但以高计算复杂度和延迟为代价。为克服这些局限，我们提出一种低秩先验引导（LRP）框架，在用户设备端执行可学习的秩一感知，将CSI压缩为低维码字，并在基站端通过直接秩一合成重建CSI。压缩与重建联合优化，并充分利用CSI的低秩结构。我们进一步开发了DCRNetV2，其以LRP为骨干，并使用门控膨胀卷积残差路径来补偿有限秩误差。实验结果表明，LRP以较低复杂度优于迭代模型驱动方法，且DCRNetV2在精度-复杂度权衡上优于现有基于学习的方法。
英文摘要
Downlink channel state information (CSI) feedback is essential for frequency-division duplex massive MIMO, yet the feedback overhead grows rapidly with the numbers of antennas and subcarriers. To reduce this overhead, most deep learning approaches compress CSI by treating it as a generic image, leaving its low-rank multipath structure unexploited. In contrast, model-driven alternatives explicitly embed this structure in iterative recovery, but at the cost of high computational complexity and latency. To overcome these limitations, we propose a low-rank prior-guided (LRP) framework that performs learnable rank-one sensing at the user equipment to compress CSI into low-dimensional codewords, and reconstructs CSI by direct rank-one synthesis at the base station. Both compression and reconstruction are jointly optimized, and fully exploit the low-rank structure of CSI. We further develop DCRNetV2, which preserves LRP as its backbone and uses gated dilated-convolutional residual paths to compensate for finite-rank errors. Experimental results show that LRP outperforms iterative model-based methods with lower complexity, and DCRNetV2 achieves a better accuracy-complexity tradeoff than existing learning-based methods.
