Evolutionary Knowledge-Enhanced Semantic Communication: Transition From SISO to MIMO

IF 7.3 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2026-02-09 DOI:10.1109/TNSE.2026.3662778

Bizhu Wang;Huachao Xiong;Shujun Han;Mengying Sun;Daiqi Zhang;Xiaodong Xu;Ping Zhang

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引用次数: 0

Abstract

Semantic communication (SemCom) conveys semantic meaning rather than raw data to address growing data demands within limited spectrum resources. However, existing SemCom systems remain confined to single-input single-output (SISO) configurations, creating a gap with modern multiple-input multiple-output (MIMO) infrastructure. While some recent studies have explored MIMO-enabled SemCom, they assume perfect channel state information (CSI), an impractical requirement given real-world interference and complex propagation. To bridge this gap, we propose the MIMO-augmented Knowledge-Enhanced SemCom (MIMO-KESC) system, integrating MIMO-specific modules including layer mapping, precoding, and channel equalization into conventional SemCom architecture. Its key innovation is a complex-valued neural equalizer that processes inherently complex signals to preserve phase information, delivering more accurate equalization than conventional minimum mean square error (MMSE) methods without perfect CSI. The system also incorporates a knowledge enhancement component for decoding highly compressed semantics. Simulations demonstrate that MIMO-KESC delivers robustly superior transmission quality (e.g., higher Bilingual Evaluation Understudy (BLEU) scores) under diverse wireless conditions and antenna setups, paving the way for scalable, high-performance SemCom in future MIMO networks.

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进化知识增强的语义通信：从SISO到MIMO的过渡

语义通信（SemCom）传递语义而不是原始数据，以在有限的频谱资源内满足不断增长的数据需求。然而，现有的SemCom系统仍然局限于单输入单输出（SISO）配置，与现代多输入多输出（MIMO）基础设施存在差距。虽然最近的一些研究已经探索了支持mimo的SemCom，但它们假设了完美的信道状态信息（CSI），这是一个不切实际的要求，因为现实世界的干扰和复杂的传播。为了弥补这一差距，我们提出了mimo增强知识增强SemCom （MIMO-KESC）系统，将mimo特定模块包括层映射，预编码和信道均衡集成到传统的SemCom架构中。它的关键创新是一种复杂值神经均衡器，它处理固有的复杂信号以保持相位信息，比传统的最小均方误差（MMSE）方法提供更精确的均衡，没有完美的CSI。该系统还包含一个知识增强组件，用于解码高度压缩的语义。仿真表明，MIMO- kesc在不同的无线条件和天线设置下提供了强大的卓越传输质量（例如，更高的双语评估替代（BLEU）分数），为未来MIMO网络中可扩展的高性能SemCom铺平了道路。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.