arXiv:2501.16726v2 Announce Type: replace-cross Abstract: Semantic communications aim to enhance transmission efficiency by jointly optimizing source coding, channel coding, and modulation. While prior research has demonstrated promising performance in simulations, real-world implementations often face significant challenges, including noise variability and nonlinear distortions, leading to performance gaps. This article investigates these challenges in a multiple-input multiple-output (MIMO) and orthogonal frequency-division multiplexing (OFDM)-based semantic communication system, focusing on
The increasing complexity and data demands of AI, especially in real-time applications, necessitate more efficient communication protocols, making semantic communication a critical area of focus now.
This research is important because it addresses the significant challenges of real-world implementation for semantic communication systems, crucial for the reliable and efficient deployment of advanced AI applications over wireless networks.
This research refines the practical application of semantic communication over complex wireless environments (MIMO-OFDM), moving it closer from simulation to deployable systems that can optimize data transmission for meaning rather than raw bits.
- · AI developers
- · Telecommunication companies
- · Edge computing providers
- · Wireless device manufacturers
- · Traditional communication protocol developers
- · Data-intensive, latency-sensitive applications using current protocols
Improved efficiency and reliability of data transmission for AI applications over wireless networks.
Accelerated development and deployment of distributed AI systems and AI agents that rely on robust wireless communication.
Potential for new communication standards that are fundamentally 'semantic-aware,' enabling more sophisticated and autonomous machine-to-machine interactions.
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Read at arXiv cs.AI