用主成分分析压缩下行信道状态信息，效果不输深度神经网络。
Study on Downlink CSI compression: Are Neural Networks the Only Solution?
- 采用主成分分析法压缩用户设备上报的信道状态信息。
- 在多天线系统中，压缩重建性能与深度神经网络相当。
- 适合关注低复杂度、跨厂商兼容性的通信系统设计者。
大规模多输入多输出（Massive MIMO）系统通过窄波束形成实现下行链路（DL）的空间复用，从而提升数据速率。这一过程依赖于基站获取准确的下行信道状态信息（CSI）。在频分双工（FDD）系统中，用户设备（UE）需将下行CSI反馈给基站（gNB），该反馈带来显著开销，且随发射天线数量和CSI粒度增加而增长。为缓解此问题，已有研究采用基于自编码器的AI/ML方法，即在UE端使用编码器网络压缩CSI，基站端用解码器网络重构。然而，此类方法面临模型复杂度高、跨信道场景泛化能力差及不同厂商间模型兼容性差等挑战。本文研究一种传统降维方法——主成分分析（PCA），无需训练，避免上述问题。仿真结果表明，基于PCA的压缩方案在重建性能上可媲美主流的深度神经网络模型。
原文摘要 · Abstract (English)
Massive Multi Input Multi Output (MIMO) systems enable higher data rates in the downlink (DL) with spatial multiplexing achieved by forming narrow beams. The higher DL data rates are achieved by effective implementation of spatial multiplexing and beamforming which is subject to availability of DL channel state information (CSI) at the base station. For Frequency Division Duplexing (FDD) systems, the DL CSI has to be transmitted by User Equipment (UE) to the gNB and it constitutes a significant overhead which scales with the number of transmitter antennas and the granularity of the CSI. To address the overhead issue, AI/ML methods using auto-encoders have been investigated, where an encoder neural network model at the UE compresses the CSI and a decoder neural network model at the gNB reconstructs it. However, the use of AI/ML methods has a number of challenges related to (1) model complexity, (2) model generalization across channel scenarios and (3) inter-vendor compatibility of the two sides of the model. In this work, we investigate a more traditional dimensionality reduction method that uses Principal Component Analysis (PCA) and therefore does not suffer from the above challenges. Simulation results show that PCA based CSI compression actually achieves comparable reconstruction performance to commonly used deep neural networks based models.
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