标题： 下一代人工智能原生无线通信：基于MCMC的接收器架构用于统一处理 显示英文标题

标题： Next-Generation AI-Native Wireless Communications: MCMC-Based Receiver Architectures for Unified Processing

摘要： 多输入多输出（MIMO）接收机处理是当前和下一代无线通信的关键技术。 然而，随着天线数量的增加，它在复杂性和可扩展性方面面临重大挑战。 人工智能（AI）是下一代无线网络的核心，为解决这些挑战提供了巨大的潜力。 本文提出了一种基于马尔可夫链蒙特卡洛（MCMC）技术的AI驱动的通用MIMO接收机架构。 与现有基于AI的方法将接收机处理视为黑盒不同，我们的基于MCMC的方法作为一种通用的贝叶斯计算引擎，适用于各种处理任务，包括信道估计、符号检测和信道解码。 这种方法提高了接收机在不同场景下的可解释性、可扩展性和灵活性。 此外，所提出的方法将这些任务整合到一个统一的概率框架中，从而实现整体性能优化。 这个统一框架还可以无缝地与数据驱动的学习方法结合，以促进完全智能通信接收机的发展。 显示英文摘要

摘要： The multiple-input multiple-output (MIMO) receiver processing is a key technology for current and next-generation wireless communications. However, it faces significant challenges related to complexity and scalability as the number of antennas increases. Artificial intelligence (AI), a cornerstone of next-generation wireless networks, offers considerable potential for addressing these challenges. This paper proposes an AI-driven, universal MIMO receiver architecture based on Markov chain Monte Carlo (MCMC) techniques. Unlike existing AI-based methods that treat receiver processing as a black box, our MCMC-based approach functions as a generic Bayesian computing engine applicable to various processing tasks, including channel estimation, symbol detection, and channel decoding. This method enhances the interpretability, scalability, and flexibility of receivers in diverse scenarios. Furthermore, the proposed approach integrates these tasks into a unified probabilistic framework, thereby enabling overall performance optimization. This unified framework can also be seamlessly combined with data-driven learning methods to facilitate the development of fully intelligent communication receivers.