Resource-Efficient Multicast URLLC Service in 5G Systems.

Area of Science:

• Telecommunications Engineering
• Wireless Communication Systems
• Network Performance Analysis

Background:

• Emerging applications like factory automation and intelligent transportation demand multicast Ultra-Reliable Low-Latency Communications (mURLLC).
• 5G systems, since 3GPP Release 17, natively support multicast features, but efficient usage for mURLLC remains underexplored.
• Existing specifications and studies lack comprehensive guidance for optimizing mURLLC performance.

Purpose of the Study:

• To conduct the first system-level evaluation of mURLLC in 5G networks.
• To identify key differences between mURLLC and traditional multicast broadband wireless communications.
• To propose and evaluate paradigm-shifting approaches for efficient mURLLC provision.

Main Methods:

• Leveraging insights from 3GPP specifications for system-level analysis.
• Comparative evaluation of mURLLC against traditional broadband multicast solutions.
• Simulation-based assessment of optimized multicast mechanisms and algorithms for mURLLC traffic.

Main Results:

• mURLLC presents unique challenges distinct from broadband multicast.
• Specific approaches require significant paradigm shifts from existing multicast solutions.
• Proper configuration of multicast mechanisms can reduce resource consumption by up to three times compared to baseline broadband multicast.

Conclusions:

• Efficient mURLLC requires tailored approaches beyond traditional multicast.
• Optimized multicast configurations are crucial for satisfying stringent latency and reliability demands.
• Significant increases in 5G system capacity for mURLLC are achievable through efficient resource management.