Year: 2023
Type: article
Abstract: The time-varying radio frequency interference has strong nonlinear dynamic characteristics, which is difficult to be predicted by linear method effectively, making the anti-interference decision without sufficient information support. To solve this problem, a recurrent neural network for spectrum prediction based on time-frequency correlation features is proposed. A sliding window is used to characterize the two-dimensional correlation of time-frequency series, and the spectrum prediction problem is transformed into a problem similar to spatiotemporal sequence prediction. A gradient bridge structure across time frames is added to reduce the attenuation of the gradient in the long time and multi-level network propagation. The training efficiency and network performance are improved by the loss function with better matching. Simulation and experimental results verify the validity of the network prediction results.
Authors: Pengcheng WAN, Weike FENG, Ningning TONG, Wei WEI
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,162
OpenAlex ID: W4385465002