# Scheduling of Industrial Control Traffic for Dynamic RAN Slicing with Distributed Massive MIMO +
## 2 Related Work
In our case, we consider massive MIMO and LIS systems where spatial multiplexing allows time and frequency resources to be shared between users. In [25], the authors used deep reinforcement learning to allocate radio resources between slices in a massive MIMO RAN, aiming to optimize the QoS of the slices, including an ultra-reliable low latency (URLLC) slice, into which category our industrial communications use case fits. However, this work was focused on allocation between slices, and divides the time and frequency resource blocks disjointly, without borrowing between slices. We instead consider the scheduling of devices within the industrial control slice, devices share resource blocks but are instead separated via spatial multiplexing with the help of pilot signals, and devices in one slice can borrow unused resources from other slices. Machine learning was also applied to RAN slicing in [26], in this case, with a cascaded convolutional neural network-long short-term memory network but for broadcasting services rather than URLLC traffic. Slicing for low latency services was performed in [27] with the help of an SDN-based framework, but here, the targeted system was the backhaul networks rather than the RAN. RAN slicing between massive IoT and URLLC use cases is performed in [28] for a cooperative multipoint system; however, they studied bursty URLLC, whereas we consider periodic industrial control traffic. The traffic model in this case significantly impacts how resource allocation can be performed. The work in [29] considers the three canonical 5G use cases: URLLC, enhanced mobile broadband (eMBB), and massive machine-type communication (mMTC), and allocates resource blocks as well as other resources, such as virtual network functions, to different slices within each use case. Mixed-integer programming for throughput optimization was used for RAN slicing in [30], and both the RAN and network routing between the remote radio heads and the baseband unit were considered. However, the traffic model used downlink traffic only, characterized by a required data rate for each user. For our use case of industrial control traffic, it is important to consider periodic transmissions in the traffic model. There is as yet not much existing work on RAN slicing taking into account the particular spatial multiplexing capabilities of massive MIMO and allocating resources based on pilot signals. Distributed massive MIMO with non-orthogonal slicing was investigated in [31], using mixed-integer programming optimization as we also do in our work. However, in that work, the objective to be optimized was energy efficiency, which we do not consider here, instead aiming for minimal resource usage. Massive MIMO is also the radio technology used in [32], with both pilot contamination and pilot duration taken into account. The objective in that work was, however, to maximize the total system data rate, and each slice was characterized by a required minimum rate. In this paper, we draw together the threads of dynamic RAN slicing, massive MIMO resource allocation using pilot signals, modeling of industrial control traffic, and scheduling. We continue the work we began in [16] and extend it to the dynamic RAN slicing case. We develop solutions for the scheduling of industrial control traffic that facilitate efficient resource borrowing between slices by minimizing the pilot rate demanded by the industrial control slice, while still meeting its stringent latency and reliability requirements. A summary of this paper's contributions with respect to the works discussed above is given in Table 1.