LADEA: A Near Real-Time Transmission Power Adaptation Algorithm for Increased Energy Efficiency in Dense Indoor Campus Networks

To increase the local network capacity and reliability in 5G and Beyond 5G domains, network densification, through the addition of more 5G base stations or radio heads has become a prevailing practice. However, the lack of dynamic control in this approach resulted in an undesirable increase in total transmission power consumption. To increase the dynamicity, we introduce a new near-real-time power adaptation algorithm, named Location And Density Aware Energy Adaptation (LADEA), that intelligently adjusts the transmission power levels based on the number and the positioning of the active user devices while maintaining reliable connectivity for all active users. Through extensive simulations emulating a real indoor factory setting, we have validated the efficacy of our LADEA algorithm. Unlike conventional strategies such as ON-OFF switching and non-real-time Self Organized Networks, our pioneering approach dynamically optimizes transmission power in real-time, considering the momentary proximity of connected users to base stations, fitting the overall power consumption closer to the theoretical optimal. Our experimental results showcase the superiority of the proposed approach, achieving a remarkable 50 percent reduction in power consumption compared to traditional cellular networks, while simultaneously upholding dependable connectivity.