Intelligent Resource Management Using Multiagent Double Deep Q-Networks to Guarantee Strict Reliability and Low Latency in IoT Network

With the rapid adoption of the Internet of Things, it is necessary to go beyond fifth-generation applications and apply stringent high reliability and low latency requirements, closely related to strict delay demands. These requirements support massive network connectivity for multiple Internet of Things devices. Hence, in this paper, we optimize energy efficiency and achieve quality-of-service requirements by mitigating co-channel interference, performing efficient power control of transmitters, and harvesting energy using time-slot exchanges. Due to a nonconvex optimization problem, we propose an iterative algorithm for power allocation and time slot interchange to reduce the computational complexity. To achieve a high degree of ultra-reliability and low latency with quality-of-service-aware instantaneous reward under massive connectivity, we efficiently employ multiagent reinforcement learning by addressing the intelligent resource management problem via a novel Double Deep Q Network. The network prioritizes experience replay to exploit the best policy and maximize accumulative rewards. It also learns the optimal policy and enhances learning efficiency by maximizing its reward function to make decisions with high intelligence and guarantee strict ultra-reliability and low latency. The simulation result shows that the Double Deep Q Network with prioritized experience replay can guarantee stringent ultra-reliability and low latency. As a result, the co-channel interference between transmission links and the high-power consumption density associated with the massive connectivity of the Internet of Things devices are mitigated.

Automated summary

In this paper, the authors propose a solution to meet the high reliability and low latency requirements of Internet of Things devices using energy efficiency optimization, co-channel interference mitigation, efficient power control, and time-slot exchanges. They employ a Double Deep Q Network and multiagent reinforcement learning to achieve intelligent resource management and prioritize experience replay for maximum rewards.