# Scheduling of Industrial Control Traffic for Dynamic RAN Slicing with Distributed Massive MIMO +
## 3 Targeted Scenario  
We target a smart factory scenario as in Figure 1. On the factory floor, there are machines and other devices that need to communicate. Some of these require real-time control with low latency and high reliability, for example, robots and other industrial equipment, while other devices do not have strict requirements, for example, Internet of Things (IoT) sensors, smartphones, and other devices used by factory staff. We consider that RAN slicing is applied, with each device category occupying a slice to serve its traffic. In the following, we focus on the real-time control devices, which occupy the highest-priority slice. In the typical closed-loop control case, the plant (machine or other equipment to be controlled) needs to transmit frequent updates to the controller, and receive control values in return. In our scenario, the controller may be located either on the factory floor or in the local cloud; however, even in the case where the controller is on the factory floor, it may communicate with the plant wirelessly. As such, this communication needs to go via a base station (in the centralized massive MIMO case), or a federation of cooperating contact \begin{table}
\begin{tabular}{c c} \hline \hline
**Traffic Model** & \\ \hline
[19; 20; 21; 22] & grant-free and mMTC traffic \\
[23] & alarm traffic \\
[28] & bursty URLLC traffic \\
[30] & downlink rate-based traffic \\
**This work** & scheduled periodic traffic for industrial control, \\  & both uplink and downlink \\ \hline \hline \end{tabular}  
Slicing and resource allocation
[25; 29] & URLLC with allocation between slices slicing for broadcasting services scheduling within industrial control slice, with resource borrowing between slices \\ \hline \hline \end{tabular}  
Optimization objective
[31] & energy efficiency
[32] & maximization of total system rate minimal resource usage  
\end{table} 
Table 1: Summary of the contributions of this work in relation to existing work. Figure 1: Our targeted smart factory scenario. Machines and other devices on the factory floor communicate with each other and with cloud backends via massive multiple-input-multiple-output (MIMO) base stations. service points (CSPs) (in the LIS case) [8]. In the following we will focus on the LIS case and refer to the _serving CSPs_, but our work is equally applicable to cellular massive MIMO systems. The key requirement is that the devices should be served by antennas that are operated coherently, and share the same set of pilot signals. To facilitate communication, end devices send pilot signals to the service CSPs, where there are a limited number of pilot signals in each time slot (coherence interval). In this work, we do not model the radio channel in detail, but we assume similar characteristics to the Lund massive MIMO testbed LuMaMi [33] for the massive MIMO case, and to the Lund LIS testbed (still under construction) for the LIS case. Both of these use 3.7 GHz as the operating frequency, and uniform rectangular arrays as the antenna configuration. For LIS, the distances between the CSPs can vary, so long as they are operated coherently and thus share the same set of pilot signals and serve the same set of users. However, the maximum possible number of CSPs to serve a group of users and the distance between them is still a matter of active research, especially in low-latency cases where communication of channel state information between the CSPs, needed for coherent operation, can impose a bottleneck. We assume the end devices to be single-antenna devices. Our problem is then to allocate pilot signals to end devices in time slots such that the communication requirements of the devices are fulfilled while minimizing the resources--pilots--used by the industrial control slice as a whole. Each device should receive sufficiently many pilots to satisfy its traffic demands, both on the uplink and downlink, and should receive a pilot sufficiently frequently enough to ensure stable and effective control.