# Scheduling of Industrial Control Traffic for Dynamic RAN Slicing with Distributed Massive MIMO +
## 1 Introduction  
The rise of Industry 4.0 brings new challenges in communications. Key ideas in Industry 4.0 include that manufacturing should become more flexible and dynamic, with greater customization and optimization of production, automation, and adaptation, with real-time capability, decentralization, and modularity as important principles for operation [1, 2]. In order to realize this vision, there is an ambition to move more and more to wireless communication on the factory floor. Compared with traditional wired deployments, wireless communication allows for easier reconfiguration of the factory floor, adjustments of scheduling and resource allocation, as well as reduced costs since there is no need to install cables for communication. As such, smart factories are a major application domain for new and emerging use cases envisaged to be served by 6G systems [3]. Providing wireless communication in a domain where applications have stringent requirements on reliability and latency is no easy task; however, massive multiple-input-multiple-output (MIMO) [4] is far more promising in this regard than previous wireless technologies. With massive MIMO, the large number of antennas provide a high degree of diversity, improving reliability for industrial applications [5], while an effect known aschannel hardening_ smooths out small-scale variations in the radio channel and provides a much more predictable performance [6]. These effects will be enhanced with the advent of large intelligent surfaces (LIS) [7; 8], also known as cell-free distributed massive MIMO, slated to be the next evolution of massive MIMO and a key technology for 6G [3]. In LIS, the number of antennas will increase once again, and they will be deployed in multiple arrays over a wider area, further enhancing spatial diversity and increasing the chances that a user will have antennas located close by and within line-of-sight, providing good channel quality. While there are many other promising technologies in the development of 6G, both for communication [9; 10] as well as localization and sensing [11], we focus here specifically on LIS and how it affects scheduling and resource allocation. To provide a high quality of service (QoS) communication that meets the demands of industrial applications requires not only the underlying physical layer technology, but also appropriate scheduling and resource allocation methods at the link layer. With the concept of networking slicing [12; 13], the network is divided into multiple logically separated virtual networks, called slices, each tailored to a particular application or group of users. Each slice can implement policies that optimize quality of service for the particular application it serves. Radio access network (RAN) slicing [14; 15] occurs when the network slicing concept is applied to the RAN, with radio resources divided between the slices in such a way as to meet their application requirements. Slicing can be performed either statically, where each slice receives a fixed allocation of resources, or dynamically, where each slice may receive some minimum allocation, but also opportunistically borrow resources from other slices when they are not needed. In large-scale antenna array systems, such as massive MIMO and LIS, the primary resource to be allocated in the RAN is orthogonal pilot signals. In time division duplex (TDD) massive MIMO systems, during each coherence block--a time-frequency region in which the channel is effectively constant--a user device must transmit a pilot signal in order for the base station to collect its channel state information (CSI) and calculate an appropriate precoding matrix (on the downlink) and combining matrix (on the uplink). Having done so, the device may both transmit and receive in the coherence block, and a large number of devices may transmit and receive simultaneously.