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Enhancing convolution recurrent network with graph signal processing: High suppressive interference mitigation
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DOI：10.23919/JCC.fa.2023-0608.202601.png)
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Abstract
En 中文
In this paper, we propose a novel graph signal processing convolution recurrent network (GSP CRN) for signal enhancement against high suppressive interference (HSI) in wireless communications. GSPCRN consists of the short-time graph signal processing (SGSP) approach and a modified convolution recurrent network. Similar to the traditional short-time time-frequency transformation, SGSP frames the complex-valued communication signal and transforms it to the graph-domain representations, where the connection and weight flexibility of each vertex are fully taken into account. In the presence of HSI, SGSP can extract signal features from new graph-domain dimensions and empower neural networks for weak signal enhancement. Two SGSP methods, adjacency singular value decomposition and implicit graph transformation, are designed to capture relationships among the sampling points in the segmented signals. Simulation results demonstrate that our proposed GSPCRN outperforms existing classic methods in extracting weak signals from the HSI environment. When the interference-to-signal ratio exceeds 27dB, only our proposed GSPCRN can achieve the interference mitigation.
Keywords:
adjacency matrix
short-time graph signal processing
signal enhancement
wireless communications
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