Self-Detecting Traffic Interference Control for Multi-Zone Services under 5G-Based Cellular Networks.

Area of Science:

• Wireless communication networks
• 5G cellular technology
• Traffic management

Background:

• Cellular and Device-to-Device (D2D) networks face performance challenges due to traffic interference.
• Optimizing Quality of Service (QoS) and throughput in mixed traffic environments is crucial for 5G.
• Existing methods struggle to efficiently manage diverse traffic types and congestion patterns.

Purpose of the Study:

• To propose a multi-zone service control scheme for maximizing performance in 5G networks with numerous cellular and D2D zones.
• To minimize traffic interference by dividing traffic into real-time and non-real-time categories.
• To introduce a novel self-detection traffic interference control technique (STICS) for D2D and Cellular-to-Device (C2D) communication.

Main Methods:

• Development of the Self-detecting Traffic Interference Control Scheme (STICS).
• Classification of traffic into real-time and non-real-time categories based on characteristics.
• Distinguishing between short-term and long-term traffic congestion processes for adaptive control.
• Application and simulation of STICS within a 5G cellular network environment.

Main Results:

• STICS efficiently classifies traffic types based on flow characteristics in 5G networks.
• The scheme effectively reduces traffic overload by minimizing interference between D2D and C2D links.
• Simulations demonstrate STICS outperforms existing comparison schemes in terms of QoS and throughput.

Conclusions:

• The proposed STICS is an effective mechanism for enhancing communication performance in 5G networks.
• Traffic classification and adaptive interference control are key to improving QoS and throughput.
• STICS offers a promising solution for managing complex traffic dynamics in future wireless systems.