D3QN-Based Multi-Priority Computation Offloading for Time-Sensitive and Interference-Limited Industrial Wireless Networks

Industrial wireless networks (IWNs) are generally time-sensitive and interference-limited to guarantee real-time and reliability for critical industrial tasks. However, the high-concurrent access of heterogeneous industrial tasks poses great challenges for IWNs which are generally resource-limited. By employing multi-access edge computing (MEC) to enhance the computing capability, this paper proposes a multi-priority computation offloading scheme to realize end-edge orchestrated computing for time-sensitive and interference-limited IWNs based on deep reinforcement learning. Specifically, we study a general scenario that multiple industrial end devices offload tasks to multiple MEC-enhanced industrial base stations to cooperatively accomplish a complex industrial work. By fully considering different task deadlines, edge computing capabilities, maximum transmit power and peak co-channel interference power, we formulate an overall task delay minimization problem with respect to computing decisions, offloading ratios and transmit powers. Due to the non-convexity of the problem, we reformulate it by Markov decision process and design a priority-driven reward, where multiple priorities are assigned according to different deadline requirements. To approximate the optimum solution in the explosive state space, we employ the double and dueling architectures on the basis of deep Q-network (namely D3QN), and propose the D3QN-based multi-priority computation offloading scheme (D3QN-MPCOS). Extensive experiments are performed to validate the suitability and superiority of D3QN-MPCOS for IWNs, where eight benchmark schemes are compared. The results show that D3QN-MPCOS can converge with a higher reward and a smaller overall task delay than other schemes, and satisfy the deadline requirements of heterogeneous industrial tasks under different interference constraints.