# Deep Reinforcement Learning-Based Joint Scheduling of 5G and TSN in Industrial Networks
## 2 Related Work
### Related Research on 5G-TSN
In [20], the authors present a system-level simulator integrated with 5G and TSN, and then investigate the impacts of GCL setting in TSN (consecutive transmission or uniformly distributed transmission) and radio channel quality of 5G on end-to-end latency of time-triggered applications and system throughput, but this work does not present any dynamic joint scheduling schemes. In [21], the authors propose a 5G-TSN joint scheduling mechanism based on radio channel information; the TSN scheduling considers the impacts of channel quality in 5G, and the flows bearing on radio channels with worse quality could be processed as priority. A transmission time budget in 5 GSs is proposed, considering the maximum retransmission times in the correspondence channel quality, and this time budget in 5 GSs is set as the constraints for scheduling. However, this work still does not research dynamic 5G scheduling considering TSN shaping state. Reference [22] investigates how to achieve low-latency communication in a 5G-TSN integration network by considering non-real-time communication services with high-throughput requirements, and a two-step approach is proposed to solve the resource allocation problem and develop priority metrics for the TSN and eMBB streams according to their characteristics, respectively. However, this work also only considers 5G scheduling with latency constraints of time-triggered applications, and does not consider the impacts of TSN scheduling on 5G resource allocations. Reference [23] makes use of the network function virtualization (NFV) technique to implement QoS-aware mapping and scheduling in 5G-TSN networks; it first designs an incremental greedy algorithm to map VNFs to resources of 5G and TSN, and develops a preemption-based 5G resource scheduling scheme to offer no-wait transmission for higher priority applications. This work mainly focuses on the dynamic priority configuration, which aims to improve the resource preemption capability for time-critical applications in both 5G and TSN domains. However, this work does not pay attention to the collaboration of 5G and TSN scheduling schemes, and the 5G scheduling scheme is related in too simple a manner. The above related research status on 5G dynamical scheduling and 5G-TSN resource allocation indicates that there is limited literature taking both 5G and TSN resource scheduling states into consideration. Therefore, a dynamic 5G-TSN joint scheduling algorithm is studied in this article; the proposed algorithm takes GCL setting status, features of multiple applications, available resources and radio channel quality in 5G into consideration, and the object of the optimization is to satisfy the latency requirements for time-critical application across 5G-TSN as well as to guarantee throughput requirements for other applications in 5 GSs. Considering the optimization problem of 5G-TSN joint scheduling is more complex and cannot be solved with the convex optimization method directly; a reinforcement learning model is proposed to learn the optimal resource allocation policy from complex network environments.