  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.
