论文标题
在智能工厂中启用了无上行链路的大型MIMO的资源分配
Resource Allocation for Uplink Cell-Free Massive MIMO enabled URLLC in a Smart Factory
论文作者
论文摘要
智能工厂需要支持具有超可靠性和低延迟通信（URLLC）的多个工业互联网（IIOT）设备的同时通信。同时，与传统的人与人之间通信相比，用于IIOT应用的短数据包传输会损失性能。另一方面，通过部署分布式访问点（APS），无单元的大量多输入和多输出（CF MMIMO）技术可以为所有设备提供统一的服务。在本文中，我们采用CF MMIMO来支持智能工厂的URLLC。具体而言，我们首先在有限的区块长度（FBL）下使用不完善的通道状态信息（CSI）来得出可实现的上行链路数据速率的下限（LB），以用于最大比率组合（MRC）和全杆零型（FZF）解码器。 \ textColor {black} {基于MRC情况的衍生LB速率具有与Ergodic速率相同的趋势，而使用FZF解码器使用FZF解码器的LB速率紧密匹配ergodic速率}，这意味着可以根据LB数据速率执行资源分配，而不是fbl下的Ergodic数据率。然后，使用\ textColor {black} {log-function方法}和连续的凸近似（SCA）来近似将非凸的加权总和问题问题转换为一系列几何程序（GP）问题，并提出了一系列迭代算法，以共同优化飞行员和有效载荷功率分配。模拟结果表明，与集中式MMIMO相比，CF MMIMO显着提高了平均加权总和（AWSR）。一个有趣的观察结果是，增加设备的数量可改善CF MMIMO的AWSR，而AWSR对于集中式MMIMO仍然相对恒定。
Smart factories need to support the simultaneous communication of multiple industrial Internet-of-Things (IIoT) devices with ultra-reliability and low-latency communication (URLLC). Meanwhile, short packet transmission for IIoT applications incurs performance loss compared to traditional long packet transmission for human-to-human communications. On the other hand, cell-free massive multiple-input and multiple-output (CF mMIMO) technology can provide uniform services for all devices by deploying distributed access points (APs). In this paper, we adopt CF mMIMO to support URLLC in a smart factory. Specifically, we first derive the lower bound (LB) on achievable uplink data rate under the finite blocklength (FBL) with imperfect channel state information (CSI) for both maximum-ratio combining (MRC) and full-pilot zero-forcing (FZF) decoders. \textcolor{black}{The derived LB rates based on the MRC case have the same trends as the ergodic rate, while LB rates using the FZF decoder tightly match the ergodic rates}, which means that resource allocation can be performed based on the LB data rate rather the exact ergodic data rate under FBL. The \textcolor{black}{log-function method} and successive convex approximation (SCA) are then used to approximately transform the non-convex weighted sum rate problem into a series of geometric program (GP) problems, and an iterative algorithm is proposed to jointly optimize the pilot and payload power allocation. Simulation results demonstrate that CF mMIMO significantly improves the average weighted sum rate (AWSR) compared to centralized mMIMO. An interesting observation is that increasing the number of devices improves the AWSR for CF mMIMO whilst the AWSR remains relatively constant for centralized mMIMO.
