基于谱商累积量的信道鲁棒自动调制分类
Channel-robust Automatic Modulation Classification Using Spectral Quotient Cumulants
AI总结：
针对多径衰落信道导致的性能下降，提出基于谱商累积量的信道鲁棒调制分类框架SQCC，通过谱商变换、异常值过滤和神经网络分类，在未知莱斯信道下实现高准确率。
摘要：
自动调制分类（AMC）旨在识别受到信道效应和噪声干扰的接收信号的调制格式。现有的大多数工作关注噪声的影响，而对信道效应影响的关注相对较少。然而，多径衰落信道带来的不稳定性会导致显著的性能下降。为了减轻多径信道的不利影响，我们针对正交频分复用（OFDM）系统提出了一种名为谱商累积量分类（SQCC）的信道鲁棒调制分类框架。具体而言，我们首先通过谱循环移位除法运算将接收信号转换为谱商（SQ）序列。其次，提出了一种异常值检测器来过滤SQ序列中的异常值。最后，我们从滤波后的SQ序列中提取谱商累积量（SQC）作为输入来训练人工神经网络（ANN）分类器，并使用训练好的ANN做出最终决策。仿真结果表明，与现有其他方法相比，我们提出的SQCC方法在各种未知的莱斯多径衰落信道下表现出分类鲁棒性和优越性。具体来说，在多个信道下测试但在AWGN信道下训练时，SQCC方法在信噪比（SNR）为4dB时实现了接近90%的分类准确率。
英文摘要：
Automatic modulation classification (AMC) is to identify the modulation format of the received signal corrupted by the channel effects and noise. Most existing works focus on the impact of noise while relatively little attention has been paid to the impact of channel effects. However, the instability posed by multipath fading channels leads to significant performance degradation. To mitigate the adverse effects of the multipath channel, we propose a channel-robust modulation classification framework named spectral quotient cumulant classification (SQCC) for orthogonal frequency division multiplexing (OFDM) systems. Specifically, we first transform the received signal to the spectral quotient (SQ) sequence by spectral circular shift division operations. Secondly, an outlier detector is proposed to filter the outliers in the SQ sequence. At last, we extract spectral quotient cumulants (SQCs) from the filtered SQ sequence as the inputs to train the artificial neural network (ANN) classifier and use the trained ANN to make the final decisions. Simulation results show that our proposed SQCC method exhibits classification robustness and superiority under various unknown Rician multipath fading channels compared with other existing methods. Specifically, the SQCC method achieves nearly 90% classification accuracy at the signal to noise ratio (SNR) of 4dB when testing under multiple channels but training under AWGN channel.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
