This paper proposes an incremental Radial Basis Function (RBF) based approach for cross-tier interference mitigation in resource-constrained dense IoT networks within 5G communication systems. The primary innovation of this approach is the use of an incremental RBF method to model and optimize interference patterns in resource-constrained dense IoT networks. Unlike conventional interference mitigation techniques that view interference as a static phenomenon, this method adapts to the dynamic nature of IoT networks by incrementally updating the RBF model. This enables precise modeling of various interference scenarios and real-time modification of interference mitigation parameters. The approach also considers the spatial distribution of IoT devices, allowing for intelligent resource allocation and optimization of interference mitigation parameters based on device density and location data. This adaptive resource allocation improves network capacity, reliability, and overall system performance by maximizing the utilization of available resources while minimizing interference. The effectiveness of the incremental RBF-based approach is demonstrated through extensive experiments and simulations, showing substantial improvements in communication performance, including increased throughput, decreased packet loss, and decreased latency. The proposed method addresses the specific challenges of interference between tiers in resource-constrained dense IoT networks within 5G communication systems. It mitigates interference by leveraging the incremental RBF technique and considering the spatial distribution of IoT devices, thereby enhancing network capacity, reliability, and overall system performance. The research also closes a gap in the literature by proposing a dynamic and adaptive interference mitigation technique tailored to the characteristics of resource-constrained dense IoT networks. The proposed method advances the state of the art in 5G communication systems by enhancing the efficient transmission of data in such networks and facilitating the reliable operation of IoT devices in environments with limited resources. The paper also discusses the system model, network model, and the incremental RBF modeling approach for cross-tier interference mitigation in resource-constrained dense IoT networks. The proposed model employs incremental RBF to model and optimize interference patterns in resource-constrained dense IoT networks, offering a dynamic and adaptable method for handling cross-tier interference. The incremental RBF method allows for continuous updating of the interference model in response to changing interference scenarios, enhancing the precision of interference modeling and optimization. The interference coefficients play a crucial role in the proposed network model for modeling and optimizing interference patterns in resource-constrained dense IoT networks. These coefficients quantify the influence of interference between two IoT devices and are determined based on observed interference measurements or channel state information. The problem formulation involves modeling and mitigating cross-tier interference in resource-constrained dense IoT networks to ensure efficient and dependable network communication. The objective function aims to optimize the system's overall performance by balancing the need to maximize system capacity while minimizing interference. The proposed model includes components such as interference management, resource allocation, energy efficiency, quality of service, network coverage, scalability, network stability, and network congestion. The initialization phase sets default interference mitigation parameters to define how interference is estimated and controlled in the network. The incremental update process adjusts interference mitigation parameters based on observed interference patterns, refining initial values to achieve accurate interference modelingThis paper proposes an incremental Radial Basis Function (RBF) based approach for cross-tier interference mitigation in resource-constrained dense IoT networks within 5G communication systems. The primary innovation of this approach is the use of an incremental RBF method to model and optimize interference patterns in resource-constrained dense IoT networks. Unlike conventional interference mitigation techniques that view interference as a static phenomenon, this method adapts to the dynamic nature of IoT networks by incrementally updating the RBF model. This enables precise modeling of various interference scenarios and real-time modification of interference mitigation parameters. The approach also considers the spatial distribution of IoT devices, allowing for intelligent resource allocation and optimization of interference mitigation parameters based on device density and location data. This adaptive resource allocation improves network capacity, reliability, and overall system performance by maximizing the utilization of available resources while minimizing interference. The effectiveness of the incremental RBF-based approach is demonstrated through extensive experiments and simulations, showing substantial improvements in communication performance, including increased throughput, decreased packet loss, and decreased latency. The proposed method addresses the specific challenges of interference between tiers in resource-constrained dense IoT networks within 5G communication systems. It mitigates interference by leveraging the incremental RBF technique and considering the spatial distribution of IoT devices, thereby enhancing network capacity, reliability, and overall system performance. The research also closes a gap in the literature by proposing a dynamic and adaptive interference mitigation technique tailored to the characteristics of resource-constrained dense IoT networks. The proposed method advances the state of the art in 5G communication systems by enhancing the efficient transmission of data in such networks and facilitating the reliable operation of IoT devices in environments with limited resources. The paper also discusses the system model, network model, and the incremental RBF modeling approach for cross-tier interference mitigation in resource-constrained dense IoT networks. The proposed model employs incremental RBF to model and optimize interference patterns in resource-constrained dense IoT networks, offering a dynamic and adaptable method for handling cross-tier interference. The incremental RBF method allows for continuous updating of the interference model in response to changing interference scenarios, enhancing the precision of interference modeling and optimization. The interference coefficients play a crucial role in the proposed network model for modeling and optimizing interference patterns in resource-constrained dense IoT networks. These coefficients quantify the influence of interference between two IoT devices and are determined based on observed interference measurements or channel state information. The problem formulation involves modeling and mitigating cross-tier interference in resource-constrained dense IoT networks to ensure efficient and dependable network communication. The objective function aims to optimize the system's overall performance by balancing the need to maximize system capacity while minimizing interference. The proposed model includes components such as interference management, resource allocation, energy efficiency, quality of service, network coverage, scalability, network stability, and network congestion. The initialization phase sets default interference mitigation parameters to define how interference is estimated and controlled in the network. The incremental update process adjusts interference mitigation parameters based on observed interference patterns, refining initial values to achieve accurate interference modeling