Standard digital communication techniques allow us to set aside the meaning of the messages to concentrate on the transmission of bits efficiently and reliably. However, with the integration of artificial intelligence into communications technology and the merging of communication and computation within devices, increasing evidence suggests that the semantic aspect of communication cannot be set aside. We propose a part-of-speech-based encoding strategy and context-based decoding strategies, in which various deep learning models are presented to learn the semantic and contextual features as background knowledge. With the background knowledge, our strategies can be applied to some non-jointly-designed communication scenarios with uncertainty. We compare the performances of two proposed decoding strategies, the deep learning models of which are different, to provide model-choice design guidelines in accordance with specific communication conditions. Further, we discuss the impact of several parameters on the performance of our strategies, such as the size of the context window and the size of the feature window. Simulation results indicate the effectiveness and the reliability of our strategies in terms of decreasing the number of bits used to transmit messages and increasing the semantic accuracy between transmitted messages and recovered messages.

中文翻译：

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通过动态编程进行基于上下文的语义通信

标准的数字通信技术使我们能够抛开消息的含义，专注于有效和可靠地传输比特。然而，随着人工智能与通信技术的整合以及设备内通信和计算的融合，越来越多的证据表明，通信的语义方面不能被搁置一旁。我们提出了一种基于词性的编码策略和基于上下文的解码策略，其中提出了各种深度学习模型来学习语义和上下文特征作为背景知识。有了背景知识，我们的策略可以应用于一些具有不确定性的非联合设计的通信场景。我们比较了两种提出的解码策略的性能，不同的深度学习模型，根据具体的通信条件提供模型选择设计指南。此外，我们讨论了几个参数对我们策略性能的影响，例如上下文窗口的大小和特征窗口的大小。仿真结果表明我们的策略在减少用于传输消息的比特数和提高传输消息和恢复消息之间的语义准确性方面的有效性和可靠性。