This academic article delves into the potential and challenges of task-oriented and semantic communications for next-generation (NextG) networks. It discusses the integration of multi-task learning, using deep neural networks for encoding and decoding tasks. These tasks span semantic information preservation, source input reconstruction, and integrated sensing and communications. The paper also explores potential vulnerabilities from adversarial attacks during both training and testing phases. The authors envision a joint and robust design of task-oriented communications, semantic communications, and integrated sensing and communications as key for context-aware, resource-efficient, and secure communications in NextG network systems.
Publication date: 4 Jan 2024
Project Page: Not provided
Paper: https://arxiv.org/pdf/2401.01531
