A Survey on Energy-Efficient in Semantic Communication: Techniques, Challenges, and Future Directions

As communication networks evolve to accommodate the increasing demands of Artificial intelligence (AI) applications, Internet of Things (IoT) devices, and edge computing, energy efficiency has become a critical concern. By focusing on transmitting only the most important information rather than raw data, semantic communication (SemCom) presents a promising solution for reducing bandwidth usage. However, this shift introduces new challenges in managing energy consumption, particularly in dynamic and resource-constrained environments. We survey recent advances in energy-efficient techniques for semantic communication, focusing on methods such as task offloading, resource optimization, and adaptive transmission strategies. In addition, integration of energy harvesting and the deployment of semantic communication in 6G networks are explored as a promising approach to sustaining long-term energy efficiency. Emerging trends in multimodal semantic communication highlight the challenges of optimizing energy when transmitting different types of data simultaneously. Finally, we discuss the open challenges and future research directions needed to ensure the practical scalability of energy-efficient semantic communication systems in next-generation networks.