论文标题
位基于深度学习的大量MIMO CSI反馈的量化适配器
Quantization Adaptor for Bit-Level Deep Learning-Based Massive MIMO CSI Feedback
论文作者
论文摘要
在大量的多输入多输出（MIMO）系统中，用户设备（UE）需要将通道状态信息（CSI）送回基站（BS）以进行以下光束成形。但是，大型MIMO系统中的大型天线会导致大量的反馈开销。基于深度学习（DL）的方法可以压缩UE处的CSI并在BS上恢复它，从而大大降低了反馈成本。但是，必须将压缩的CSI量化为位流以进行传输。在本文中，我们提出了基于位基于DL的CSI反馈的适应器辅助量化策略。首先，我们设计了一个网络辅助适配器和高级培训方案，以适应性地提高量化和重建精度。此外，为了简单地就业，我们介绍了数据分配的专家知识，并提出了一种可插入和无成本的适配器方案。实验表明，与最先进的反馈量化方法相比，这种适应器辅助的量化策略可以实现更好的量化精度和重建性能，而较少或没有额外的成本。开源代码可在https://github.com/zhang-xd18/qcrnet上找到。
In massive multiple-input multiple-output (MIMO) systems, the user equipment (UE) needs to feed the channel state information (CSI) back to the base station (BS) for the following beamforming. But the large scale of antennas in massive MIMO systems causes huge feedback overhead. Deep learning (DL) based methods can compress the CSI at the UE and recover it at the BS, which reduces the feedback cost significantly. But the compressed CSI must be quantized into bit streams for transmission. In this paper, we propose an adaptor-assisted quantization strategy for bit-level DL-based CSI feedback. First, we design a network-aided adaptor and an advanced training scheme to adaptively improve the quantization and reconstruction accuracy. Moreover, for easy practical employment, we introduce the expert knowledge of data distribution and propose a pluggable and cost-free adaptor scheme. Experiments show that compared with the state-of-the-art feedback quantization method, this adaptor-aided quantization strategy can achieve better quantization accuracy and reconstruction performance with less or no additional cost. The open-source codes are available at https://github.com/zhang-xd18/QCRNet.
