A method analysis of multi-modal fusion channel clustering based on attention mechanism
Bohan Liu, Yanfei Bao, Bolin Zhang · 2023
In the case where prior knowledge such as frequency, bandwidth, and modulation mode is unknown, the RF(radio frequency) signal from the receiver must be demodulated to a bitstream. In this paper, unsupervised clustering of logical channels such as communication system signals, service synchronization or physical layer signaling using deep learning methods is analyzed. After analysis, it is found that the current channel clustering mainly faces two problems: one is the need to manually annotate the key features of the signal dock; Second, different representations of signals, such as square spectrum and time-frequency graph, contain different feature information. At present, most of them are only analyzed for specific signal representations. Therefore, we propose to use the mixed-domain attention mechanism to automatically locate the areas that need to be focused on instead of manual participation. At the same time, use product layer for feature fusion of different signal representations (Identical/orthogonal format, Amplitude/Phase format, Time and Frequency diagram) to improve the recognition accuracy, which is a feasible direction for future research.