期刊
IEEE WIRELESS COMMUNICATIONS LETTERS
卷 12, 期 10, 页码 1647-1651出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LWC.2023.3243117
关键词
Autoformer; series channel-spatial attention; long-term spectrum prediction
In this letter, we propose an Autoformer with a series channel-spatial attention module (CSAM) (Autoformer-CSA) for long-term spectrum prediction. The CSAM creatively replaces 2D convolution with 1D convolution in image attention, significantly improving the learning ability of the Autoformer. Experimental results demonstrate that the Autoformer-CSA outperforms state-of-the-art benchmarks.
In this letter, we develop an Autoformer with a series channel-spatial attention module (CSAM) (Autoformer-CSA) for long-term spectrum prediction. More specifically, the CSAM ingeniously replaces 2-dimensional (2D) convolution in image attention (including channel attention and spatial attention) with 1-dimensional (1D) convolution. The CSAM replaces Autoformer's feed-forward network. It is used to assign different concentrations to features mapped to the high-dimensional space, improving the learning ability of the Autoformer. We follow the series decomposition block and auto-correlation mechanism of the Autoformer. Experiments on a real-world dataset show that the Autoformer-CSA is superior to the state-of-the-art benchmarks.
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