发表机构
College of Information Science and Electronic Engineering, Zhejiang University; Zhejiang Key Laboratory of Multi-modal Commu. Netw. & Intell. Info. Proc.（浙江大学信息与电子工程学院; 浙江省多模通信网络与智能信息处理重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出Diff-Rx，一种基于增量条件扩散的MIMO-OFDM AI接收机，通过软判决逐步丰富条件输入并实现单步生成，在低导频密度下显著提升信道估计与数据检测性能。
AI 中文摘要
传统的迭代接收机通常先利用稀疏导频观测进行信道估计，然后基于信道估计进行数据检测，接着利用判决反馈更新信道估计，如此循环。在基于人工智能（AI）的接收机设计中，利用这种逐步丰富的数据观测来增强生成式信道估计也至关重要。然而，数据判决的统计特性和可靠性总是随着信道估计过程而演变，这给整体学习框架和算法的设计带来了巨大挑战。本文提出Diff-Rx，一种用于多输入多输出正交频分复用（MIMO-OFDM）系统的、数据检测与信道估计协同设计的增量条件扩散接收机。具体而言，我们开发了一种条件自适应后训练方法，使生成式信道估计器能够适应由软数据判决逐步丰富的条件输入，并隐式对齐导频和数据诱导的信道特征空间以减轻潜在估计误差。无阈值软判决提供平滑的条件更新，无需针对特定条件进行可靠性调优。我们进一步开发了一种条件扩散变换器，能够在噪声观测和不同导频模式下进行鲁棒信道估计，同时将传统的多步扩散生成简化为单步。在统计信道和特定站点射线追踪信道上的仿真表明，Diff-Rx在不同噪声水平、导频密度和调制方案下均展现出一致的增益，在低至1/32的导频密度下仍能良好工作，并在信道估计和数据检测性能上取得显著提升。
英文摘要
Conventional iterative receiver usually begins with channel estimation using sparse pilot observations and follows with data detection based on the channel estimates, and then updates channel estimation using decision feedback, and so on. In Artificial Intelligence (AI)-based receiver design, it is also crucial to make use of such progressively enriched data observations to enhance the generative channel estimation. However, the statistical characteristics and reliability of the data decisions always evolve with the channel estimation processes, which brings great challenges to the design of the overall learning framework and algorithms. In this paper, we propose \textit{Diff-Rx}, an incrementally conditioned diffusion-based receiver with co-designed data detection and channel estimation, for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Specifically, we develop a condition-adaptive post-training method which enables the generative channel estimator to adapt to the conditioning inputs that are progressively enriched by soft data decisions, and also to implicitly align the pilot- and data-induced channel feature spaces to mitigate potential estimation errors. The threshold-free soft decisions provide smooth condition updates without condition-specific reliability tuning. We further develop a conditional diffusion transformer that is capable of performing robust channel estimation under noisy observations and various pilot patterns while reducing the conventional multi-step diffusion generation to one single step. Simulations on both statistical and site-specific ray-tracing channels show that, Diff-Rx exhibits consistent gains across different noise levels, pilot densities and modulation schemes, and works well at a pilot density as low as 1/32 while achieving significant improvement in channel estimation and data detection performances.
