Ultra-reliable and low-latency communication (URLLC) is a pivotal technique for enabling the wireless control over industrial Internet-of-Things (IIoT) devices. By deploying distributed access points (APs), cell-free massive multiple-input and multiple-output (CF mMIMO) has great potential to provide URLLC services for IIoT devices. In this paper, we investigate CF mMIMO-enabled URLLC in a smart factory. Lower bounds (LBs) of downlink ergodic data rate under finite channel blocklength (FCBL) with imperfect channel state information (CSI) are derived for maximum-ratio transmission (MRT), full-pilot zero-forcing (FZF), and local zero-forcing (LZF) precoding schemes. Meanwhile, the weighted sum rate is maximized by jointly optimizing the pilot power and transmission power based on the derived LBs. Specifically, we first provide the globally optimal solution of the pilot power, and then introduce some approximations to transform the original problems into a series of subproblems, which can be expressed in a geometric programming (GP) form that can be readily solved. Finally, an iterative algorithm is proposed to optimize the power allocation based on various precoding schemes. Simulation results demonstrate that the proposed algorithm is superior to the existing algorithms, and that the quality of URLLC services will benefit by deploying more APs, except for the FZF precoding scheme.

翻译：超可靠低时延通信（URLLC）是支撑工业物联网（IIoT）设备无线控制的关键技术。通过部署分布式接入点（AP），无蜂窝大规模多输入多输出（CF mMIMO）技术在为IIoT设备提供URLLC服务方面展现出巨大潜力。本文针对智能工厂场景，研究了基于CF mMIMO的URLLC技术。在信道状态信息（CSI）不完美且有限信道块长度（FCBL）条件下，推导了采用最大比传输（MRT）、全导频迫零（FZF）和局部迫零（LZF）预编码方案时下行遍历数据速率的下界（LB）。同时，基于所推导的下界，通过联合优化导频功率与传输功率实现了加权和速率最大化。具体而言，我们首先给出导频功率的全局最优解，随后引入若干近似变换将原问题转化为一系列子问题，这些子问题可表示为易于求解的几何规划（GP）形式。最终提出一种迭代算法，基于不同预编码方案优化功率分配。仿真结果表明，所提算法优于现有算法，且除FZF预编码方案外，通过部署更多AP可提升URLLC服务质量。