在无线网络中的语义信息恢复
Edgar Beck1, Carsten Bockelmann1, Armin Dekorsy1
1Department of Communications Engineering, University of Bremen, 28359 Bremen, Germany.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
机器学习通过传输信息含义而不是精确数据来增强语义通信. 这种方法显著提高了效率,并减少了无线系统中的数据传输需求.
科学领域:
- 无线通信无线通信
- 信息理论 信息理论
- 机器学习 机器学习
背景情况:
- 语义通信的概念,专注于传递信息意义,由于机器学习 (ML) 的进步而重新浮出水面.
- 与Shannon的范式不同,语义通信的目标是对精确的数据传输进行意义保护,从而实现潜在的信息速率节省.
研究的目的:
- 将语义通信建模扩展到整个通信马尔科夫链.
- 定义和解决数据减少,可靠的消息传输的任务,以实现最佳的语义保存.
主要方法:
- 在通信马尔科夫链中使用隐藏的随机变量建模语义.
- 制定语义通信任务作为一个端到端的信息瓶问题.
- 为分布式多点场景提出并使用基于ML的语义通信系统SINFONY.
主要成果:
- 在分布式多点设置中,SINFONY成功地恢复了语义,将图像处理为消息示例.
- 数字结果表明,与传统通信系统相比,SNR在速率正常化上有显著的提高,高达20dB.
结论:
- 建议的信息瓶方法有效地实现了压缩,同时保留了基本的语义信息.
- 像SINFONY这样的ML驱动的语义通信系统在无线通信效率方面提供了实质性的性能提升.
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