用扩散模型生成用户专属高维无线信道，解决实测数据难获取问题。
Generating High Dimensional User-Specific Wireless Channels using Diffusion Models
- 以用户位置为条件，用扩散模型生成高保真信道样本。
- 信道压缩任务中NMSE降低1-2dB，波束成形增益提升11dB。
- 适合需要大量真实感信道数据的通信系统研究者。
深度神经网络在未来的无线通信系统物理层和媒体接入层中日益重要，尤其适用于大规模多天线信道建模。然而，训练此类模型通常需要大量高维信道测量数据，而这些数据极难且昂贵地获取。本文提出一种基于扩散模型的新型方法，生成反映真实无线环境的用户特定信道数据。该方法采用条件去噪扩散隐式模型（cDDIM），有效捕捉用户位置与多天线信道特性的关系。通过将用户位置作为条件输入，生成高质量合成信道样本，构建更大的增强数据集以缓解测量稀缺性问题。实验表明，该方法在信道压缩和波束对准等下游任务中表现优异：相比噪声添加或生成对抗网络（GAN）等传统方法，信道压缩的归一化均方误差（NMSE）降低1-2 dB，波束成形的信噪比（SNR）提升达11 dB。
原文摘要 · Abstract (English)
Deep neural network (DNN)-based algorithms are emerging as an important tool for many physical and MAC layer functions in future wireless communication systems, including for large multi-antenna channels. However, training such models typically requires a large dataset of high-dimensional channel measurements, which are very difficult and expensive to obtain. This paper introduces a novel method for generating synthetic wireless channel data using diffusion-based models to produce user-specific channels that accurately reflect real-world wireless environments. Our approach employs a conditional denoising diffusion implicit model (cDDIM) framework, effectively capturing the relationship between user location and multi-antenna channel characteristics. We generate synthetic high fidelity channel samples using user positions as conditional inputs, creating larger augmented datasets to overcome measurement scarcity. The utility of this method is demonstrated through its efficacy in training various downstream tasks such as channel compression and beam alignment. Our diffusion-based augmentation approach achieves over a 1-2 dB gain in NMSE for channel compression, and an 11dB SNR boost in beamforming compared to prior methods, such as noise addition or the use of generative adversarial networks (GANs).
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