Modelling of reconfigurable intelligent surfaces with adaptive power and thermal energy harvesting

Reconfigurable intelligent surfaces (RIS) have emerged as transformative elements in the evolution of wireless communication systems, particularly within cognitive radio networks (CRNs). These surfaces enable dynamic control over the wireless environment, allowing for enhanced signal propagation, interference mitigation, and spectral efficiency. In this work, we explore the integration of RIS with adaptive transmit power control and thermal energy harvesting, where energy is harvested from ambient temperature gradients. This approach enables the RIS to operate sustainably by converting environmental thermal differences - such as those found in industrial or natural settings - into usable electrical energy. We investigate the performance of RIS-enabled CRNs under various system configurations through comprehensive analytical modelling and simulation, we demonstrate that the proposed RIS-assisted CRN with thermal energy harvesting significantly outperforms conventional systems, offering notable improvements in throughput. These results underscore the potential of RIS, powered by ambient thermal gradients.