Authors:
X. Zhang, Y. Ma, Y. Liu, S. Wu, J. Jiao, Y. Gao, and Q. Zhang
Published in:
IEEE Wireless Communications Letters, vol. 12, no. 10, pp. 1712–1715, 2023
Abstract:
Compressive sensing has been proved as an effective approach for the wireless communications. However, it is challenging to efficiently recover the wideband spectrum signals with low signal-to-noise ratio (SNR). To solve this problem, we exploit a deep neural network-based robust alternating direction method of multipliers (R-ADMM) network. This neural network can suppress the noise and optimize the learnable parameters and operations of the signal reconstruction to speed up the convergence. Furthermore, we adopt the numerical differential-based gradient computation method to enhance the robustness of the network training. Numerical results show that the proposed R-ADMM achieves markedly improved noise robustness in low SNRs and reduced reconstruction time with different experiments on the real-world and simulated signals.
site: https://ieeexplore.ieee.org/abstract/document/10164627