AI- Driven Adaptive Resource Allocation For Next- Generation IoT Communication Networks

Abstract

The emergence of Internet of Things (IoT) devices in next-generation communication networks has brought about new and complex challenges related to resource management, which include massive connectivity, heterogeneous traffic loads, and strict energy considerations. Static and heuristic resource allocation algorithms have been shown to be incapable of addressing the highly dynamic, non-stationary characteristics of the 6G enabled IoT network ecosystem. This paper examines a number of AI-based adaptive resource allocation techniques utilizing deep reinforcement learning (DRL), federated learning (FL) as well as digital twin (DT) in order to allocate spectrum, power, and computational resources dynamically and efficiently. A hybrid design based on multi-agent DRL as well as hierarchical optimization for the joint optimization of transmit power, sub-channel allocation, and task offloading is presented. It is shown experimentally that the proposed scheme achieves significant reductions in terms of energy consumption and latency, while providing a high QoS satisfaction level compared to traditional heuristic algorithms.