Grant-free cell-free massive multiple-input multiple-output (GF-CF-MaMIMO) systems are anticipated to be a key enabling technology for next-generation Internet-of-Things (IoT) networks, as they support massive connectivity without explicit scheduling. However, the large amount of connected devices prevents the use of orthogonal pilot sequences, resulting in severe pilot contamination (PC) that degrades channel estimation and data detection performance. Furthermore, scalable GF-CF-MaMIMO networks inherently rely on distributed signal processing. In this work, we consider the uplink of a GF-CF-MaMIMO system and propose two novel distributed algorithms for joint activity detection, channel estimation, and data detection (JACD) based on expectation propagation (EP). The first algorithm, denoted as JACD-EP, uses Gaussian approximations for the channel variables, whereas the second, referred to as JACD-EP-BG, models them as Bernoulli-Gaussian (BG) random variables. To integrate the BG distribution into the EP framework, we derive its exponential family representation and develop the two algorithms as efficient message passing over a factor graph constructed from the a posteriori probability (APP) distribution. The proposed framework is inherently scalable with respect to both the number of access points (APs) and user equipments (UEs). Simulation results show the efficient mitigation of PC by the proposed distributed algorithms and their superior detection accuracy compared to (genie-aided) centralized linear detectors.

翻译：免授权无蜂窝大规模多输入多输出系统因其无需显式调度即可支持海量连接，有望成为下一代物联网网络的关键使能技术。然而，大量连接设备阻碍了正交导频序列的使用，导致严重的导频污染，从而降低信道估计与数据检测性能。此外，可扩展的免授权无蜂窝大规模MIMO网络本质上依赖于分布式信号处理。本文考虑免授权无蜂窝大规模MIMO系统的上行链路，基于期望传播提出了两种新颖的分布式算法，用于联合活动性检测、信道估计与数据检测。第一种算法记为JACD-EP，对信道变量采用高斯近似；第二种算法称为JACD-EP-BG，将其建模为伯努利-高斯随机变量。为将伯努利-高斯分布融入期望传播框架，我们推导了其指数族表示，并通过基于后验概率分布构建的因子图开发了两种高效的消息传递算法。所提框架在接入点数量和用户设备数量方面均具有内在可扩展性。仿真结果表明，所提分布式算法能有效抑制导频污染，且相较于（理想辅助的）集中式线性检测器具有更优的检测精度。