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、信道估计与数据检测。本文研究大规模多输入多输出系统中的海量接入问题，利用深度学习技术应对复杂的AUD、CE与DD功能。首先，我们提出一种专为可变导频长度接入设计的Transformer-AUD方案。该方法将导频长度信息与空间相关性模块集成至基于Transformer的检测器中，使单一模型能够泛化至不同导频长度与天线数量场景。其次，我们构建了生成式扩散模型驱动的迭代式CE与DD框架。该GDM采用评分函数捕获大规模MIMO信道与数据符号的后验分布：部分评分函数通过神经网络从信道数据集中学习获得，其余评分分量则通过应用符号先验星座分布与已知传输模型以闭式推导得出。基于这些后验评分，我们设计了异步交替式CE与DD框架，该框架在反向扩散过程中采用预测器-校正器采样技术，迭代生成信道估计与数据检测结果。仿真结果表明，所提方法在AUD、CE与DD性能上显著优于基线方案。