Channel Fingerprint Construction for Massive MIMO: A Deep Conditional Generative Approach

作者：Zhenzhou Jin, Li You, Xudong Li, Zhen Gao, Yuanwei Liu, Xiang‐Gen Xia, Xiqi Gao · 发表于：IEEE Transactions on Wireless Communications · 年份：2025 · DOI：10.1109/twc.2025.3623623 · 被引用次数：6 · 研究领域：Wireless Signal Modulation Classification、Advanced MIMO Systems Optimization、Millimeter-Wave Propagation and Modeling

Accurate channel state information (CSI) acquisition for massive multiple-input multiple-output (MIMO) systems is essential for future mobile communication networks. Channel fingerprint (CF), also referred to as channel knowledge map, is a key enabler for intelligent environment-aware communication and can facilitate CSI acquisition. However, due to the cost limitations of practical sensing nodes and test vehicles, the resulting CF is typically coarse-grained, making it insufficient for wireless transceiver design. In this work, we introduce the concept of CF twins and design aconditionalgenerative diffusion model (CGDM) with strong implicit prior learning capabilities as the computational core of the CF twin to establish the connection between coarse- and fine-grained CFs. Specifically, we employ a variational inference technique to derive the evidence lower bound (ELBO) for the log-marginal distribution of the observed fine-grained CFconditionedon the coarse-grained CF, enabling the CGDM to learn the complicated distribution of the target data. During the denoising neural network optimization, the coarse-grained CF is introduced asside informationto accurately guide the conditioned generation of the CGDM. To make the proposed CGDM lightweight, we further leverage the additivity of output distortion and introduce a one-shot pruning approach along with a multi-objective knowledge distillation technique. Experimental results show that the proposed approach exhibits significant...