Hybrid AI model-driven dynamic spectrum sharing for 6G wireless IoT networks

Abstract

The immense scale of the Internet of Things growth in 6G is utterly inconceivable to address utilizing conventional static spectrum allocations. A paradigm shift towards dynamic spectrum sharing is necessitated. In this article, a hybrid artificial intelligence model that combines deep reinforcement learning and a blockchain-based distributed consensus engine has been presented. Intelligent, secure, and efficient spectrum sharing may be accomplished using our model. The proposed methodology employs multi-agent reinforcement learning for efficient decentralized decision-making and IoT-enabled spectrum utilization. Specifically, IoT devices can use MARL to dynamically determine their power budget or spectrum resources to avoid inducing or experiencing interference while delivering acceptable quality of service. Using a blockchain engine to record and validate spectrum transactions enables transparent security in spectrum access. Our proposed hybrid AI model may be used to improve spectrum efficiency by 35%-40% while lowering energy usage by around 30% via intelligent sleep-wake lexicography methodologies and decision predication relative to traditional 5G. We thoroughly covered the spectrum management topic in 6G-IoT, demonstrating the feasibility of AI-based solutions.