相互信息神经估计驱动的星座塑造设计和性能分析
Xiuli Ji1, Qian Wang1, Liping Qian1
1Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, China.
Entropy (Basel, Switzerland)
|April 26, 2025
概括
使用相互信息神经估计 (MINE) 的新型深度学习技术优化了对高速通信的星座塑造. 这些方法提高了相互信息 (MI) 的性能,为无线和光学系统提供了更简单,更有效的方法.
科学领域:
- 光学和无线通信.
- 信息理论 信息理论
- 深度学习应用程序
背景情况:
- 星座设计显著影响无线和光通信性能.
- 高速系统中的高阶调制需要先进的星座塑造以增加容量.
- 传统的成型方法往往涉及复杂的计算和结构要求.
研究的目的:
- 提出基于相互信息神经估计 (MINE) 的新型几何,概率和联合星座塑造方案 (MINE-GCS,MINE-PCS,MINE-JCS).
- 在使用深度学习 (DL) 的高速连贯通信系统中最大限度地提高相互信息 (MI).
- 开发一种传输器侧优化方法,避免传统成型技术的复杂性.
主要方法:
- 引入一个MINE模块,通过反向传播估计和最大化MI,独立于通道状态信息.
- 训练编码器和概率生成器网络在各种信号噪声比率上,以优化星座点分布和概率.
- 将MI计算转化为使用MINE.的参数优化问题.
主要成果:
- 基于MINE的方案显示出优越的MI性能,与未成形的正方形振幅调制 (QAM) 和其他基于DL的联合成形方案相比.
- 在添加式白色高斯噪声下,MINE-JCS实现了最佳性能.
- 一个低级的8aryMINE-GCS显示MI增益大约为0.1比特/符号超过无形的Star-8QAM.
结论:
- 提出的基于MINE的星座塑造方案有效地最大限度地提高了相互信息.
- 这些方法在实施复杂性和MI性能之间提供了平衡.
- 这些方案可以适应各种噪音和色条件的实际应用.
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