RiSi: Spectro-temporal RAN-agnostic Modulation Identification for OFDMA Signals

TL;DR

RiSi is a neural network designed to identify modulation types in OFDMA signals by analyzing spectrograms, improving spectrum coexistence and signal recognition in complex wireless environments.

Contribution

This paper introduces RiSi, a novel semantic segmentation neural network tailored for spectro-temporal OFDMA signals, addressing limitations of existing deep learning methods.

Findings

Achieved 86% accuracy in modulation identification across four types.

Demonstrated improved generalization with domain adaptation techniques.

Effective recognition despite channel impairments and varying parameters.

Abstract

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.