Rate splitting with semantics as a generalized multi-access framework for intelligent reflecting surfaces

Area of Science:

• Wireless Communication Engineering
• Information Theory
• Signal Processing

Background:

• Modern communication demands advanced multi-access frameworks and rate splitting with semantic awareness.
• Intelligent reflecting surfaces (IRS) offer dynamic control for radio signal propagation and network performance.
• Integrating rate-splitting multi-access (RSMA) with semantic communication presents challenges for IRS platforms.

Purpose of the Study:

• To propose novel design strategies for RSMA-IRS systems by synergizing reconfigurable intelligent surfaces with semantic communication principles.
• To address the need for intelligent radio signal propagation in advanced wireless networks.
• To enhance connectivity and network performance for diverse users.

Main Methods:

• Development of a novel RSMA-IRS model incorporating semantic communication principles.
• Design strategies for reconfigurable intelligent surfaces tailored for semantic-aware RSMA.
• Experimental analysis and performance evaluation in Beyond 5G/6G contexts.

Main Results:

• The proposed RSMA-IRS system effectively integrates semantic communication principles.
• Experimental analysis validates the effectiveness of the design guidelines.
• A 2.94% reduction in delay was observed in the semantic RSMA-IRS framework compared to a non-semantic RSMA-IRS system, even with increased users.

Conclusions:

• The semantic-enhanced RSMA-IRS model presents a promising solution for future wireless networks.
• The integration of semantic communication with RSMA and IRS is crucial for overcoming current wireless challenges.
• The proposed approach demonstrates significant performance improvements, particularly in reducing transmission delay.