论文标题
6G网络：超越香农迈向语义和面向目标的通信
6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications
论文作者
论文摘要
本文的目的是促进这样的想法，即未来6G网络中包括语义和面向目标的方面可以在系统效率和可持续性方面产生重大飞跃。语义交流超出了公共的香农范式，即保证每个单个传输数据包的正确接收，而与数据包所传达的含义无关。这个想法是，每当沟通传达意义或实现目标时，真正重要的是对数据包的正确接收/解释将对目标成就产生的影响。专注于语义和目标方面，并可能将它们结合在一起，有助于确定相关信息，即严格恢复发射器意图或实现目标的含义所必需的信息。将知识表示和推理工具与机器学习算法相结合为建立语义学习策略的方式铺平了道路，从而使当前的机器学习算法能够获得更好的解释能力和对比对抗性攻击。 6G语义网络可以在网络边缘带来语义学习机制，同时，语义学习可以帮助6G网络提高其效率和可持续性。
The goal of this paper is to promote the idea that including semantic and goal-oriented aspects in future 6G networks can produce a significant leap forward in terms of system effectiveness and sustainability. Semantic communication goes beyond the common Shannon paradigm of guaranteeing the correct reception of each single transmitted packet, irrespective of the meaning conveyed by the packet. The idea is that, whenever communication occurs to convey meaning or to accomplish a goal, what really matters is the impact that the correct reception/interpretation of a packet is going to have on the goal accomplishment. Focusing on semantic and goal-oriented aspects, and possibly combining them, helps to identify the relevant information, i.e. the information strictly necessary to recover the meaning intended by the transmitter or to accomplish a goal. Combining knowledge representation and reasoning tools with machine learning algorithms paves the way to build semantic learning strategies enabling current machine learning algorithms to achieve better interpretation capabilities and contrast adversarial attacks. 6G semantic networks can bring semantic learning mechanisms at the edge of the network and, at the same time, semantic learning can help 6G networks to improve their efficiency and sustainability.
