对MIMO语义通信的重要意识资源配置
Yue Cao1, Youlong Wu1, Lixiang Lian1
1School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China.
Entropy (Basel, Switzerland)
|June 26, 2025
概括
本研究介绍了MIMO系统中语义通信的独立源通道编码 (SSCC) 框架. 它通过优先考虑语义忠实性而不是比特级冗余性来提高模型的可重复使用性和任务性能.
科学领域:
- 无线通信无线通信
- 人工智能的人工智能
- 信息理论 信息理论
背景情况:
- 传统的6G系统表现出位级冗余性,而联合源通道编码 (JSCC) 则在复杂环境中难以适应通道和模型重用性.
- 语义通信旨在通过传输意义而不是原始数据来提高效率,但在与现有通信框架集成方面面临挑战.
研究的目的:
- 提出一个单独的源通道编码 (SSCC) 框架,以克服JSCC在MIMO系统中的局限性.
- 通过将语义处理与通道编码脱而出来增强语义通信,以实现更好的通道适应性和模型重用性.
- 整合可解释的人工智能与资源管理,以在资源有限的环境中优化语义通信.
主要方法:
- 开发了一个三层架构,采用基于变量自编码器 (VAE) 的语义编码器/解码器来进行源代码.
- 引入了基于通信的瓶归因 (CIBA) 机制,以量化特征对学习任务的重要性.
- 实施了一个重要意识的资源分配计划,以使沟通目标与深度学习任务保持一致.
主要成果:
- 验证了CIBA在导出重要度评分方面的有效性,这些评分将学习任务和沟通优化联系起来.
- 通过特征扰动实验证明了重要意识资源配置的必要性,优于传统方法.
- 展示了SSCC在各种MIMO配置中增强的模型可重复使用性和适应性.
结论:
- 拟议的SSCC框架有效地解决了MIMO系统中的语义通信挑战,优先考虑语义忠实性和任务有效性.
- 集成CIBA和重要意识资源分配优化了沟通效率和学习任务性能.
- 这项工作为SSCC强大的语义通信系统奠定了基础,特别是在资源有限的场景中.
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