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.

翻译：无线接入网络无关通信技术能够在无先验知识的情况下识别未知信号的内在特征，借助该技术，同一非授权频段内互不兼容的无线接入网络可实现比当前基于先听后说机制更优的共存性能。盲调制识别是其关键组成部分，该技术无需任何先验知识即可对不兼容信号的调制类型进行盲识别。现有盲调制识别方案基于深度神经网络构建，但仅限于单载波信号识别，对于调制方式随时间和频率变化的频谱-时间域OFDMA信号缺乏实用性。为此，本文提出RiSi——一种专为OFDMA频谱图设计的语义分割神经网络，该网络采用扁平化卷积以更有效地识别OFDMA资源块网格状结构。我们使用包含多种信道损伤的真实OFDMA数据集对RiSi进行训练，在BPSK、QPSK、16-QAM、64-QAM四种调制类型上平均达到86%的调制识别准确率。进一步通过域泛化方法提升RiSi的泛化性能（将不同FFT尺寸或不同循环前缀长度视为不同域），实验表明经泛化处理的RiSi在未见数据上仍能保持良好性能。