在FDD系统中基于多源表征的CSI反模型
Fei Pan1,2, Xiaoyu Zhao1,2, Boda Zhang1,2
1College of Information Engineering, Sichuan Agricultural University, Ya'an 625014, China.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
本研究介绍了Mix_Multi_TransNet,这是一种用于无线系统中高效通道状态信息 (CSI) 反的新型神经网络. 该模型以更少的参数实现更高的准确性,优化频谱和能源使用.
科学领域:
- 无线通信无线通信
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 准确的通道状态信息 (CSI) 对无线系统的频谱和能源效率至关重要.
- 频分复制 (FDD) 系统面临的挑战是,CSI反消耗频谱资源.
- 现有的CSI反的深度学习模型往往具有复杂的结构和众多的参数,限制了资源有限的环境中的性能.
研究的目的:
- 为了开发一种基于神经网络的CSI反模型,Mix_Multi_TransNet.
- 为了提高CSI反的准确性,同时减少模型复杂性和参数数量.
- 解决当前资源有限的无线通信系统中深度学习算法的局限性.
主要方法:
- 拟议的Mix_Multi_TransNet,一个包含空间和时间通道特征的神经网络模型.
- 与传统的CSI反网络对模型的性能进行了评估.
- 在室内和室外场景中进行了各种压缩比的实验.
主要成果:
- 与传统方法相比,Mix_Multi_TransNet在室内和室外环境中表现出更高的准确性.
- 在多个压缩比 (η) 中观察到显著的正常化平均平方误差 (NMSE) 收益.
- 在室内场景中,NMSE收益范围从4.06dB到6.47dB. 在户外场景中,收益范围从2.93dB到6.24dB.
结论:
- Mix_Multi_TransNet有效地平衡了高反准确度和降低模型复杂度.
- 拟议的模型为FDD系统中高效的CSI反提供了一个有希望的解决方案.
- 这些发现表明,在需要性能和资源效率的场景中,这些发现具有实际适用性.
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