Energy-Efficient Cell Association and Load Balancing for Low Battery Users in Heterogeneous Cellular Networks

The future Heterogeneous Cellular Network (HCN) is expected to fulfill the demand for the Internet of Things, Industry 4.0, extended reality, connected vehicles and mobile gadgets. In HCN, the deployment of low power nodes inside the coverage area of a high power node yields the benefit of providing improved data rates to users by offloading a portion of users from a highly loaded high power node to relatively lightly loaded low power nodes. However, the offloading of users through conventional cell association schemes may not guarantee improved load balancing along with the energy efficiency (EE) of critical users such as user equipment (UE) with low battery power. This paper proposes improved cell association schemes based on the battery levels of UE and Deep Q-learning (DQL) to achieve load balancing and to decrease the power consumption of Low Battery Users (LBUs). The proposed approach combines UE battery levels with three conventional association schemes for improvement: minimum distance based cell association, maximum biased received power (MBRP), and maximum biased SINR (MBSINR). Results on cell association, load balancing, and EE of LBUs have been compared with conventional association schemes. The proposed battery based cell association models perform well in favor of LBUs and clearly show the significance of the proposed approach.