Artificial Intelligence-Based Resource Allocation in Ultradense Networks: Applying Event-Triggered Q-Learning Algorithms

Ultradense networks (UDNs) have emerged as a promising architecture that can support the extremely high demand for data traffic in the future. Through the dense deployment of massive small base stations (SBSs), the system can be well promoted in terms of network capacity and spectrum efficiency, but this deployment scheme will also bring huge challenges to wireless resource management. Artificial intelligence (AI) can be applied to UDN scenarios, enabling intelligent communication devices to learn and complete resource allocation. This article discusses resource allocation schemes based on AI algorithms in UDNs and proposes an event-triggered, reinforcement-learning-based subchannel and power allocation algorithm.