Learning-based delay sensitive and reliable traffic adaptation for DC-PLC and 5G integrated multi-mode heterogeneous networks

Low-carbon smart parks achieve self balanced carbon emission and absorption through the cooperative scheduling of direct current (DC)-based distributed photovoltaic, energy storage units, and loads. Direct current power line communication (DC-PLC) enables real-time data transmission on DC power lines. With traffic adaptation, DC-PLC can be integrated with other complementary media such as 5G to reduce transmission delay and improve reliability. However, traffic adaptation for DC-PLC and 5G integration still faces the challenges such as coupling between traffic admission control and traffic partition, dimensionality curse, and the ignorance of extreme event occurrence. To address these challenges, we propose a deep reinforcement learning (DRL)-based delay sensitive and reliable traffic adaptation algorithm (DSRTA) to minimize the total queuing delay under the constraints of traffic admission control, queuing delay, and extreme events occurrence probability. DSRTA jointly optimizes traffic admission control and traffic partition, and enables learning-based intelligent traffic adaptation. The long-term constraints are incorporated into both state and bound of drift-plus-penalty to achieve delay awareness and enforce reliability guarantee. Simulation results show that DSRTA has lower queuing delay and more reliable quality of service (QoS) guarantee than other state-of-the-art algorithms.