We consider a cell-free massive multiple-input multiple-output (CF-MaMIMO) communication system in the uplink transmission and propose a novel algorithm for blind or semi-blind joint channel estimation and data detection (JCD). We formulate the problem in the framework of bilinear inference and develop a solution based on the expectation propagation (EP) method for both channel estimation and data detection. We propose a new approximation of the joint a posteriori distribution of the channel and data whose representation as a factor graph enables the application of the EP approach using the message-passing technique, local low-complexity computations at the nodes, and an effective modeling of channel-data interplay. The derived algorithm, called bilinear-EP JCD, allows for a distributed implementation among access points (APs) and the central processing unit (CPU) and has polynomial complexity. Our simulation results show that it outperforms other EP-based state-of-the-art polynomial time algorithms.

翻译：我们考虑上行链路传输中的无小区大规模多输入多输出（CF-MaMIMO）通信系统，并提出一种用于盲或半盲联合信道估计与数据检测（JCD）的新型算法。我们将问题构建于双线性推理框架下，并基于期望传播（EP）方法开发了针对信道估计与数据检测的解决方案。我们提出一种新的信道与数据联合后验分布近似方法，其因子图表示使得EP方法能够通过消息传递技术、节点处的低复杂度局部计算以及对信道-数据交互的有效建模得以应用。所推导的算法称为双线性EP JCD，支持接入点（AP）与中央处理单元（CPU）之间的分布式实现，并具有多项式复杂度。仿真结果表明，该算法优于其他基于EP的最新多项式时间算法。