论文标题
基于深度学习的CSI反馈的可扩展框架
Scalable Framework For Deep Learning based CSI Feedback
论文作者
论文摘要
多输入多输出（MIMO）系统中基于深度学习（DL）的渠道状态信息（CSI）反馈最近引起了学术界和工业的广泛关注。从实际的观点来看，训练，转移和部署基本站（BS）每个参数配置的DL模型是巨大的负担。在本文中，我们为基于DL的CSI反馈提出了一个可扩展且灵活的框架，称为可扩展CSINET（SCSINET），以适应一个配置参数的家族，例如反馈有效载荷，MIMO通道等级，天线数量。为了降低模型大小和训练的复杂性，在不同的参数配置中重复使用了带有预处理和后处理的核心块，这与与面向配置的设计完全不同。预处理和后处理是引入可训练的神经网络层，用于匹配输入/输出维度和概率分布。在系统级模拟中，通过平方广义余弦相似性（SGC）和用户吞吐量（UPT）的指标评估所提出的SCSINET。与现有方案（面向配置的DL方案和3GPP REL-16基于II代码书的方案）相比，该计划的方案可以显着降低模式大小，并在所有参数配置方面实现2％-10％的UPT改进。
Deep learning (DL) based channel state information (CSI) feedback in multiple-input multiple-output (MIMO) systems recently has attracted lots of attention from both academia and industrial. From a practical point of views, it is huge burden to train, transfer and deploy a DL model for each parameter configuration of the base station (BS). In this paper, we propose a scalable and flexible framework for DL based CSI feedback referred as scalable CsiNet (SCsiNet) to adapt a family of configured parameters such as feedback payloads, MIMO channel ranks, antenna numbers. To reduce model size and training complexity, the core block with pre-processing and post-processing in SCsiNet is reused among different parameter configurations as much as possible which is totally different from configuration-orienting design. The preprocessing and post-processing are trainable neural network layers introduced for matching input/output dimensions and probability distributions. The proposed SCsiNet is evaluated by metrics of squared generalized cosine similarity (SGCS) and user throughput (UPT) in system level simulations. Compared to existing schemes (configuration-orienting DL schemes and 3GPP Rel-16 Type-II codebook based schemes), the proposed scheme can significantly reduce mode size and achieve 2%-10% UPT improvement for all parameter configurations.
