Energy minimized joint resource allocation and task offloading with user association for URLLC and eMBB 5G multi-RAT systems

In this paper, a multi-agent deep reinforcement learning-based joint resource allocation and task offloading control technique is proposed to minimize the energy consumption of user equipment (UE) supporting 5G ultra reliability low latency communications (URLLC) and enhanced mobile broadband (eMBB) services while meeting strict quality of service (QoS) requirements in 5G multi-radio access technology (RAT) networks. An optimization problem involving transmission power, channel resource, user association, offloading rate, and central processing unit (CPU) frequency is formulated using a queueing system-based mathematical design to support services with different characteristics while minimizing the energy consumption. It is proven in this paper that this problem is nondeterministic polynomial (NP) hard, in which multi-agent deep reinforcement learning (DRL) is used to solve the problem. To increase the learning efficiency and stability of deep reinforcement learning, prioritized experience replay (PER) and delayed target network and policy updates are applied. Simulation results show that the proposed scheme provides an improved energy consumption performance compared to the benchmarked schemes.