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

2025-05-12

AI Summary

In large-scale MIMO systems, hardware cost constraints limit channel fingerprint (CF) resolution, hindering fine-grained wireless transceiver design. To address this, we propose “Channel Fingerprint Twinning,” a novel paradigm that employs a conditional generative diffusion model based on variational inference to reconstruct high-fidelity fine-grained CFs from coarse-grained inputs. Our contributions include: (i) the first CF twinning architecture; (ii) an evidence lower bound (ELBO)-optimized, side-information-guided denoising mechanism; and (iii) a lightweight, one-shot network-layer additive pruning strategy coupled with multi-objective knowledge distillation. Experiments demonstrate that our method significantly outperforms baselines in reconstruction accuracy and enables zero-shot cross-scale reconstruction—validating its strong generalization capability and scalability for practical deployment.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

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 a conditional generative 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 CF conditioned on 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 as side information to accurately guide the conditioned generation of the CGDM. To make the proposed CGDM lightweight, we further leverage the additivity of network layers and introduce a one-shot pruning approach along with a multi-objective knowledge distillation technique. Experimental results show that the proposed approach exhibits significant improvement in reconstruction performance compared to the baselines. Additionally, zero-shot testing on reconstruction tasks with different magnification factors further demonstrates the scalability and generalization ability of the proposed approach.

Problem

• Improving coarse-grained channel fingerprint accuracy for MIMO
• Designing a generative model to refine channel state information
• Enhancing scalability of channel fingerprint reconstruction methods

Innovation

• Deep conditional generative model for CF twins
• Variational inference for fine-grained CF learning
• Lightweight CGDM with pruning and distillation