面向下一代无线网络的高效自动调制分类
Efficient Automatic Modulation Classification for Next-Generation Wireless Networks
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中文总结 AI 辅助
针对6G网络对自动调制分类算法的需求，提出阈值去噪递归神经网络（TDRNN）模型，结合自适应阈值去噪算法与递归神经网络，经联合优化，在准确性、速度和计算复杂度方面优于现有方法，为6G无线通信系统提供理想方案。
中文摘要 AI 辅助
随着第六代（6G）网络的即将发展，对高精度、计算高效且推理时间短的自动调制分类（AMC）算法有需求。为此，我们提出一种新的基于深度学习的AMC模型——阈值去噪递归神经网络（TDRNN）。它结合自适应阈值去噪（TD）算法和递归神经网络（RNN），TD模块降低接收信号噪声水平，RNN模块对去噪结果进行调制分类，二者联合优化。对不同调制方案和信噪比评估TDRNN，实验结果表明其在准确性、速度和计算复杂度方面优于现有方法，是6G无线通信系统的理想解决方案。
英文摘要
With the imminent development of sixth-generation (6G) networks, there will be a demand for high-accuracy, computationally-efficient, and low-inference time automatic modulation classification (AMC) algorithms. To address this need, we propose a new deep-learning based model for AMC that is called the threshold denoise recurrent neural network (TDRNN). The TDRNN combines an adaptive threshold denoising (TD) algorithm and a recurrent neural network (RNN) that together achieve high accuracy and fast inference. The TD module adaptively reduces the noise level of the received signal, while the RNN module performs the modulation classification on the denoised result. The two subsystems are jointly optimized to reach the optimal architecture. The proposed TDRNN is evaluated for various modulation schemes and signal-to-noise ratios (SNR). The experimental results demonstrate that the TDRNN outperforms existing methods in terms of accuracy, speed, and computational complexity making it an ideal solution for 6G wireless communication systems.
