使用基于树的回归器对GFDM无线传感器网络进行混合VLC-RF通道估计
Azam Isam Aladwani1, Tarik Adnan Almohamad1, Abdullah Talha Sözer1
1Electrical and Electronics Engineering Department, Faculty of Engineering, Karabuk University, Karabuk 78050, Türkiye.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
一个新的基于树的回归模型为无线传感器网络 (WSN) 提供了高效的混合通道估计,使用了通用频率分割复杂化 (GFDM). 这个模型优先考虑实时应用程序的速度和低计算成本,而不是边际准确度的增长.
科学领域:
- 无线通信系统无线通信系统
- 信号处理 信号处理
- 机器学习应用 机器学习应用
背景情况:
- 在无线传感器网络 (WSN) 中,通过可见光通信 (VLC) 和射频 (RF) 链路使用通用频率分割复合 (GFDM) 进行混合通道估计至关重要.
- 现实的混合通道涉及附加的白色高斯噪声 (AWGN) 和雷利衰减,对MMSE和LMMSE等传统估计器构成挑战,因为它们在非线性条件下具有刚性.
- 现有的方法与VLC/RF频道组合的异质和非线性性质作斗争,需要新的方法来进行准确和高效的估计.
研究的目的:
- 提出一种新的基于树的回归模型,用于在支持GFDM的WSN中进行混合通道估计.
- 解决复杂,现实的道环境中传统估计器的局限性.
- 为资源有限的WSN开发数据驱动的解决方案,以平衡精度和计算效率.
主要方法:
- 一个决策树回归器被开发和训练使用一个数据集的18000个信号样本在36个信号噪声比 (SNR) 的水平.
- 该模型与支持矢量机 (SVM) 和随机森林算法进行了评估,用于混合通道估计.
- 性能指标包括测试数据集的准确性,比特错误率 (BER) 和推断时间.
主要成果:
- 拟议的树模型实现了竞争力的准确性 (90.83%在10 dB,97.63%在30 dB) 和低BER (0.0917在10 dB,0.0237在30 dB).
- 推理效率是一个关键优势,树模型在45.53秒内完成预测,比随机森林 (140.09s) 和SVM (189.35s) 快得多.
- 观察到一个权衡:树模型提供了大量的计算节约,但与合并方法相比,预测性能略低.
结论:
- 基于树的回归模型为WSN中的混合通道估计提供了高效的解决方案,特别适用于实时和低功耗应用.
- 它的快速推断时间使其适合于对延迟敏感的无线系统,其中计算开销是一个关键问题.
- 该模型代表了一种实用的方法,用于优先考虑速度和资源效率的场景,而不是估计准确度的边际改进.
相关概念视频
01:26Linear Approximation in Frequency Domain
136
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
136
01:30Classification of Signals
903
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
903
01:15Receiver Operating Characteristic Plot
336
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
336
01:08Determination of Expected Frequency
2.3K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.3K
01:21Linear Approximation in Time Domain
128
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
128
01:03IR Frequency Region: Fingerprint Region
1.1K
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
1.1K