Massive random access is an important technology for achieving ultra-massive connectivity in next-generation wireless communication systems. It aims to address key challenges during the initial access phase, including active user detection (AUD), channel estimation (CE), and data detection (DD). This paper examines massive access in massive multiple-input multiple-output (MIMO) systems, where deep learning is used to tackle the challenging AUD, CE, and DD functions. First, we introduce a Transformer-AUD scheme tailored for variable pilot-length access. This approach integrates pilot length information and a spatial correlation module into a Transformer-based detector, enabling a single model to generalize across various pilot lengths and antenna numbers. Next, we propose a generative diffusion model (GDM)-driven iterative CE and DD framework. The GDM employs a score function to capture the posterior distributions of massive MIMO channels and data symbols. Part of the score function is learned from the channel dataset via neural networks, while the remaining score component is derived in a closed form by applying the symbol prior constellation distribution and known transmission model. Utilizing these posterior scores, we design an asynchronous alternating CE and DD framework that employs a predictor-corrector sampling technique to iteratively generate channel estimation and data detection results during the reverse diffusion process. Simulation results demonstrate that our proposed approaches significantly outperform baseline methods with respect to AUD, CE, and DD.

翻译：海量随机接入是实现下一代无线通信系统超大规模连接的关键技术，旨在解决初始接入阶段的核心挑战，包括活跃用户检测（AUD）、信道估计（CE）和数据检测（DD）。本文研究大规模多输入多输出（MIMO）系统中的海量接入问题，利用深度学习处理复杂的AUD、CE和DD功能。首先，我们提出一种针对可变导频长度接入定制的Transformer-AUD方案。该方法将导频长度信息和空间相关性模块集成到基于Transformer的检测器中，使单一模型能够泛化适用于不同导频长度和天线数量。其次，我们提出一个生成扩散模型（GDM）驱动的迭代CE和DD框架。GDM采用评分函数来捕捉大规模MIMO信道和数据符号的后验分布。评分函数的一部分通过神经网络从信道数据集中学习，其余评分分量则通过应用符号先验星座分布和已知传输模型以闭合形式推导得出。利用这些后验评分，我们设计了一个异步交替的CE和DD框架，采用预测器-校正器采样技术，在反向扩散过程中迭代生成信道估计和数据检测结果。仿真结果表明，我们提出的方法在AUD、CE和DD方面显著优于基线方法。