This paper explores opportunities and challenges of task (goal)-oriented and semantic communications for next-generation (NextG) communication networks through the integration of multi-task learning. This approach employs deep neural networks representing a dedicated encoder at the transmitter and multiple task-specific decoders at the receiver, collectively trained to handle diverse tasks including semantic information preservation, source input reconstruction, and integrated sensing and communications. To extend the applicability from point-to-point links to multi-receiver settings, we envision the deployment of decoders at various receivers, where decentralized learning addresses the challenges of communication load and privacy concerns, leveraging federated learning techniques that distribute model updates across decentralized nodes. However, the efficacy of this approach is contingent on the robustness of the employed deep learning models. We scrutinize potential vulnerabilities stemming from adversarial attacks during both training and testing phases. These attacks aim to manipulate both the inputs at the encoder at the transmitter and the signals received over the air on the receiver side, highlighting the importance of fortifying semantic communications against potential multi-domain exploits. Overall, the joint and robust design of task-oriented communications, semantic communications, and integrated sensing and communications in a multi-task learning framework emerges as the key enabler for context-aware, resource-efficient, and secure communications ultimately needed in NextG network systems.

翻译：本文探讨了通过集成多任务学习，在下一代（NextG）通信网络中实现任务（目标）导向与语义通信的机遇与挑战。该方法采用深度神经网络，在发射端部署专用编码器，在接收端部署多个任务特定解码器，通过联合训练以处理多样化任务，包括语义信息保持、信源输入重建以及集成感知与通信。为将应用场景从点对点链路扩展至多接收端设置，我们设想在各接收端部署解码器，其中分散式学习可应对通信负载与隐私挑战，并利用联邦学习技术将模型更新分发至分散节点。然而，该方法的有效性取决于所采用深度学习模型的鲁棒性。我们深入剖析了训练与测试阶段因对抗性攻击而产生的潜在脆弱性——此类攻击旨在操纵发射端编码器的输入信号以及接收端空中接收信号，突显了强化语义通信以抵御多域安全威胁的重要性。总体而言，在多任务学习框架下对任务导向通信、语义通信及集成感知与通信进行联合鲁棒设计，将成为下一代网络系统实现上下文感知、资源高效与安全通信的关键使能技术。