论文标题
RISI：OFDMA信号的光谱速率ran-ran-nostic调制识别
RiSi: Spectro-temporal RAN-agnostic Modulation Identification for OFDMA Signals
论文作者
论文摘要
无智力通信可以在没有任何先验知识的情况下识别未知信号的固有特征，在同一未经许可的频段中，与当今基于LBT的共存相比，不兼容的不兼容可以实现更好的共存性能。盲调标识是其关键构件，它盲目地识别了不兼容信号的调制类型，而没有任何先验知识。最近的盲调鉴定方案是建立在深神网络上的，这些神经网络仅限于单载波信号识别，因此并不是务实地识别调制随时间和频率而变化的频闪仪OFDMA信号。因此，本文提出了RISI是一种语义分割神经网络，旨在使用OFDMA的频谱图，该网络采用了扁平的卷积，以更好地识别OFDMA资源块的网格样模式。我们用现实的OFDMA数据集培训了RISI，包括各种通道障碍，并在四种调制类型的BPSK，QPSK，16-QAM，64-QAM的调制类型中平均达到了86％的调制识别精度。然后，我们通过应用域的概括方法来增强RISI的概括性能，同时将不同的FFT大小或不同cp长度作为不同的域，表明因此，将其化为RISI可以通过看不见的数据进行合理的表现。
RAN-agnostic communications can identify intrinsic features of the unknown signal without any prior knowledge, with which incompatible RANs in the same unlicensed band could achieve better coexistence performance than today's LBT-based coexistence. Blind modulation identification is its key building block, which blindly identifies the modulation type of an incompatible signal without any prior knowledge. Recent blind modulation identification schemes are built upon deep neural networks, which are limited to single-carrier signal recognition thus not pragmatic for identifying spectro-temporal OFDMA signals whose modulation varies with time and frequency. Therefore, this paper proposes RiSi, a semantic segmentation neural network designed to work on OFDMA's spectrograms, that employs flattened convolutions to better identify the grid-like pattern of OFDMA's resource blocks. We trained RiSi with a realistic OFDMA dataset including various channel impairments, and achieved the modulation identification accuracy of 86% on average over four modulation types of BPSK, QPSK, 16-QAM, 64-QAM. Then, we enhanced the generalization performance of RiSi by applying domain generalization methods while treating varying FFT size or varying CP length as different domains, showing that thus-generalized RiSi can perform reasonably well with unseen data.
