Improving QoS for streaming data transmission over 6G networks using reconfigurable intelligent surfaces (RIS)

Area of Science:

• Wireless Communication Engineering
• Terahertz (THz) Spectrum Utilization
• Next-Generation Mobile Networks (6G)

Background:

• Ultra-reliable low-latency communication (URLLC) is critical for future 6G applications.
• Terahertz (300 GHz) bands offer vast bandwidth but face propagation challenges.
• Reconfigurable Intelligent Surfaces (RIS) can intelligently shape wireless environments.

Purpose of the Study:

• To investigate a RIS-assisted 6G framework at 300 GHz for enhanced Quality of Service (QoS).
• To analyze the impact of RIS on reliability, latency, and throughput in Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) scenarios.
• To meet stringent URLLC requirements for streaming data.

Main Methods:

• Developed a 6G framework utilizing RIS at 300 GHz.
• Modeled propagation environment, incorporating noise power, path loss, RIS array gain, and atmospheric absorption.
• Introduced coding-aware Bit Error Rate (BER) and an SNR-dependent latency relation.
• Performed multi-run statistical analysis for robustness.

Main Results:

• RIS elevated NLoS Signal-to-Noise Ratio (SNR) from -20 dB to over 45 dB.
• Reduced Bit Error Rate (BER) to below 5.5 x 10⁻¹⁰.
• Decreased packet delay from 16.5 ms to approximately 2 ms.
• Increased throughput from 1 Gbps to 20 Gbps.
• Demonstrated achievement of URLLC targets.

Conclusions:

• RIS-assisted 6G frameworks significantly enhance QoS at 300 GHz.
• RIS technology is crucial for overcoming THz propagation challenges in NLoS conditions.
• The proposed framework meets demanding URLLC performance metrics for future wireless systems.