Semantic Communication for Future Wireless Networks

Dear Colleagues,

Semantic communication is expected to be a cornerstone of future wireless networks. Unlike conventional bit-centric systems, semantic communication focuses on preserving the meaning and intent of transmitted messages. This enables senders to omit information that is irrelevant to the receiver’s task, reducing communication redundancy and saving bandwidth. As a result, more network resources can be allocated to additional users or services. Semantic approaches can thus dramatically improve efficiency in data-intensive applications, allowing systems to convey higher-quality content without simply increasing bit rates.

Despite its potential, semantic communication is still nascent and presents fundamental challenges. Today’s networks lack a universal metric for “semantic fidelity,” and core concepts such as semantic information must be rigorously defined. Addressing these issues—for example by developing efficient semantic encoders/decoders, lightweight algorithms for edge devices, and robust causal inference mechanisms—is crucial for translating the promise of semantic wireless communication into practical systems.

This Special Issue of Electronics (MDPI) seeks original research that addresses unanswered questions in semantic communication for next-generation wireless networks. We invite submissions that describe, define, and quantify key challenges and propose innovative solutions or prototypes. Contributions may range from theoretical analyses to algorithm design and practical demonstrations. In particular, we encourage works showcasing prototype implementations or testbeds of semantic communication systems with real-world applicability (e.g., in IoT, autonomous systems, smart industries, etc.).

Topics of interest include, but are not limited to, the following:

• Machine learning and deep learning-enabled joint source-channel coding for semantic communication.
• Task-oriented (goal-driven) semantic communication protocols.
• Causal reasoning-based semantic communication among intelligent devices.
• Lightweight deep learning-enabled semantic communication for edge/IoT devices.
• Cross-layer design and optimization of semantic communication networks.
• Security, privacy, and trust in semantic communication systems.
• Prototype implementations, testbeds, and demonstrators of semantic communication systems.
• Applications of semantic communication in IoT, autonomous vehicles, XR/AR/VR, smart manufacturing, and other industries.

Dr. Xuan Loc Nguyen
Dr. Sheikh Salman Hassan
Guest Editors

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