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

Area of Science:

• Wireless Communication
• Network Engineering
• Signal Processing

Background:

• Dense deployment of resource-constrained Internet of Things (IoT) devices in 5G networks causes significant cross-tier interference.
• Interference degrades network performance, impacting reliable and efficient data transmission.
• Existing mitigation techniques often treat interference as static, failing to adapt to dynamic IoT environments.

Purpose of the Study:

• To propose a novel approach for cross-tier interference mitigation in 5G communication systems with dense, resource-constrained IoT networks.
• To develop a dynamic interference modeling and optimization technique using an incremental Radial Basis Function (RBF).
• To improve network capacity, reliability, and overall system performance through adaptive resource allocation.

Main Methods:

• Utilizing an incremental Radial Basis Function (RBF) technique to model and optimize interference patterns.
• Dynamically updating the RBF model to adapt to the changing nature of IoT networks.
• Intelligently allocating resources and optimizing mitigation parameters based on IoT device spatial distribution, location, and density.

Main Results:

• Demonstrated effectiveness of the incremental RBF-based approach in mitigating cross-tier interference.
• Achieved substantial improvements in communication performance through extensive experiments and simulations.
• Observed increased throughput, decreased packet loss, and reduced latency in 5G IoT networks.

Conclusions:

• The proposed incremental RBF method offers a dynamic and adaptive solution for interference mitigation in dense 5G IoT networks.
• This approach effectively addresses the challenges posed by resource-constrained IoT devices and their spatial distribution.
• The findings highlight significant enhancements in key performance indicators, paving the way for more robust 5G IoT communication.