发表机构
School of Software Technology, Zhejiang University; State Key Laboratory of Blockchain and Data Security, Zhejiang University Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（浙江大学软件学院; 浙江大学杭州高新技术产业开发区（滨江）区块链与数据安全国家重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对CSI模型在不同场景适应性差的问题，提出CCPG方法，通过组件冻结实验识别瓶颈，生成轻量级LoRA权重，经多种技术处理实现快速跨场景自适应，实验表明该方法能高效部署CSI模型用于6G通信。
AI 中文摘要
深度学习在大规模多输入多输出（Massive MIMO）物理层任务中显示出强大潜力，但环境异质性会严重降低CSI模型在未知场景中的性能，传统自适应需要目标域数据和大量计算。本文提出信道条件参数生成（CCPG），一种在动态无线环境中快速部署CSI模型的端到端管道。CCPG通过组件冻结实验识别场景敏感的自适应瓶颈，仅生成轻量级LoRA权重而非完整模型参数。它使用级联奇异值分解和感知器重采样器将高维信道特征压缩为紧凑的潜在条件。基于能量的规范化机制减轻LoRA权重中的排列和符号模糊性，基于扩散的生成器结合结构信息和不对称大小感知损失进行拓扑感知参数生成。在用于CSI反馈和信道估计的DeepMIMO和WAIR-D上的实验表明，CCPG在单次前向传播中约3秒就能适应新场景，无需目标场景训练或微调，并实现与昂贵的在线自适应相当的跨域恢复性能。这些结果表明，CCPG能够在大规模动态无线场景中高效部署CSI模型，用于智能6G通信。
英文摘要
Deep learning has shown strong potential for massive multiple-input multiple-output (Massive MIMO) physical-layer tasks, including channel state information (CSI) feedback and channel estimation. However, environmental heterogeneity can severely degrade CSI models in unseen scenarios, while conventional adaptation requires target-domain data and substantial computation. This paper proposes Channel Conditional Parameter Generation (CCPG), an end-to-end pipeline for rapid deployment of CSI models in dynamic wireless environments. CCPG identifies scene-sensitive adaptation bottlenecks through component-freezing experiments and generates only lightweight LoRA weights instead of full model parameters. It compresses high-dimensional channel features into compact latent conditions using cascaded SVD and a Perceiver Resampler. An energy-based canonicalization mechanism mitigates permutation and sign ambiguities in LoRA weights, while a diffusion-based generator incorporates structural information and an asymmetric size-aware loss for topology-aware parameter generation. Experiments on DeepMIMO and WAIR-D for CSI feedback and channel estimation show that CCPG adapts to new scenarios in about 3 seconds with a single forward pass, without target-scenario training or fine-tuning, and achieves cross-domain recovery performance comparable to costly online adaptation. These results demonstrate that CCPG enables efficient deployment of CSI models in large-scale dynamic wireless scenarios for intelligent 6G communications.
