针对MIMO通信系统的高效基于深度学习的检测方案
Roilhi F Ibarra-Hernández1, Francisco R Castillo-Soria1, Carlos A Gutiérrez1
1Faculty of Science, Autonomous University of San Luis Potosí, Av. Chapultepec 1570, Privadas del Pedregal, San Luis Potosí 78295, Mexico.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
深度学习 (DL) 为多输入多输出 (MIMO) 系统提供了高效的信号检测. 新的DL方案减少了未来无线通信的复杂性和比特错误率 (BER).
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:
- 多输入多输出 (MIMO) 对于下一代无线系统至关重要.
- 在MIMO系统中,探测器的复杂性随着天线数量的增加而增加,这给大规模MIMO带来了挑战.
- 平衡检测复杂性和比特错误率 (BER) 是非常重要的.
研究的目的:
- 为MIMO系统开发基于深度学习 (DL) 的高效信号检测策略.
- 引入新的DL方案,在复杂性和性能方面改进传统方法.
- 评估检测复杂性和BER性能之间的权衡.
主要方法:
- 一个基于DL的信号检测策略,结合了新的输入信号标记预处理阶段.
- 建议和评估两个新的方案:每天线OH (OHA) 和直接符号编码 (DSE).
- 拟议方案与传统的一热 (OH) 和最大概率 (ML) 方法的比较.
主要成果:
- 在OHA和DSE计划的分类性能方面,F1得分为0.97.
- 两种拟议方案的计算复杂性都低于传统的OH和ML方案.
- 与OH方案相比,OHA和DSE的BER性能损失分别小于1dB和2dB.
结论:
- 提出的基于DL的策略有效地减少了MIMO系统中的检测复杂性.
- OHA和DSE方案在BER性能和计算成本之间提供了有利的权衡.
- 这种方法适用于具有有限计算资源的自适应式无线系统.
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