In this paper, we propose a novel fully Bayesian approach for the massive multiple-input multiple-output (MIMO) massive unsourced random access (URA). The payload of each user device is coded by the sparse regression codes (SPARCs) without redundant parity bits. A Bayesian model is established to capture the probabilistic characteristics of the overall system. Particularly, we adopt the core idea of the model-based learning approach to establish a flexible Bayesian channel model to adapt the complex environments. Different from the traditional divide-and-conquer or pilot-based massive MIMO URA strategies, we propose a three-layer message passing (TLMP) algorithm to jointly decode all the information blocks, as well as acquire the massive MIMO channel, which adopts the core idea of the variational message passing and approximate message passing. We verify that our proposed TLMP significantly enhances the spectral efficiency compared with the state-of-the-arts baselines, and is more robust to the possible codeword collisions.

翻译：本文提出了一种新颖的完全贝叶斯方法，用于大规模多输入多输出系统（Massive MIMO）中的大规模无源随机接入（URA）。每个用户设备的有效载荷由稀疏回归码（SPARCs）编码，无需冗余校验位。我们建立了一个贝叶斯模型来捕捉整个系统的概率特性。特别地，我们采用基于模型的学习方法的核心思想，构建了一个灵活的贝叶斯信道模型，以适应复杂的环境。与传统的分而治之或基于导频的大规模MIMO URA策略不同，我们提出了一种三层消息传递（TLMP）算法，该算法采用变分消息传递和近似消息传递的核心思想，联合解码所有信息块，并获取大规模MIMO信道。我们验证了所提出的TLMP相较于现有最先进基准方法显著提升了频谱效率，并且对可能的码字碰撞具有更强的鲁棒性。