Multi IRS-Aided Low-Carbon Power Management for Green Communication in 6G Smart Agriculture Using Deep Game Theory

Abstract

Power consumption management is vital in achieving sustainable and low-carbon green communication goals in 6G smart agriculture. This research aims to provide a low-power consumption measurement framework designed specifically for critical data handling in smart agriculture application networks. Deep Q-learning combined with game theory is proposed to allow network entities such as Internet of Things (IoT) devices, Intelligent Reflecting Surfaces (IRSs), and Base Stations (BS) to make intelligent decisions for optimal resource allocation and energy and power consumption. The learning capabilities of DQL with strategic reasoning of game theory, a hybrid framework, have been developed to realize an adaptive routing plan that emphasizes energy-conscious communication protocols and underestimates the environment. It further enables the investigation of multi-IRS performance through several key metrics assessments, such as reflected power consumption, energy efficiency, and Signal-to-Noise Ratio (SNR) improvement.