Incremental RBF-based cross-tier interference mitigation for resource-constrained dense IoT networks in 5G communication system

The deployment of resource-constrained and densely distributed Internet of Things (IoT) devices poses significant challenges for 5G communication systems due to the increased likelihood of inter-tier interference. This interference can degrade network performance and hinder the transmission of data in a reliable and efficient manner. Using an incremental Radial Basis Function (RBF) technique, this paper proposes a novel approach for cross-tier interference mitigation in 5G communication among resource-constrained dense IoT networks. Utilizing the incremental RBF method to model and optimize interference patterns in resource-constrained dense IoT networks is the primary innovation of our approach. In contrast to conventional interference mitigation techniques, which view interference as a static phenomenon, our method adapts to the dynamic nature of IoT networks by incrementally updating the RBF model. This enables precise modeling of the various interference scenarios and real-time modification of interference mitigation parameters. Utilizing the spatial distribution of IoT devices, this approach improves interference mitigation. The proposed method intelligently allocates resources and optimizes interference mitigation parameters based on the location and density of IoT devices. This adaptive resource allocation improves network capacity, reliability, and overall system performance by maximizing the utilization of available resources while minimizing interference. We demonstrate the effectiveness of the incremental RBF-based approach in mitigating cross-tier interference in resource-constrained dense IoT networks within the 5G ecosystem through extensive experiments and simulations. Our findings indicate substantial improvements in communication performance, including increased throughput, decreased packet loss, and decreased latency.