Blind SINR estimation based on graph sparsity
Document Type
Conference Proceeding
Publication Date
12-1-2020
Abstract
In this paper, a novel blind signal-to-noise-plus-interference ratio (SINR) estimator is proposed based on graph sparsity. The graph representation, which can serve as a new promising alternative to time-, frequency-, and transform-domain waveforms, is capable of extracting sufficient and concise information for communication signals. We discover that the sparsity of the ultimate graph converted from the original signal waveform can be utilized to measure the SINR of the received communication signal without any need of training symbols. For this purpose, a new blind SINR estimation algorithm is introduced in this work. According to Monte Carlo simulations in comparison with the well-known blind SINR estimator using higher-order statistics, our proposed new graph-based SINR estimation scheme has demonstrated the excellent performance, especially for relatively small sample size.
Publication Source (Journal or Book title)
Proceedings - IEEE Global Communications Conference, GLOBECOM
Recommended Citation
Yan, K., Wu, H., & Huang, X. (2020). Blind SINR estimation based on graph sparsity. Proceedings - IEEE Global Communications Conference, GLOBECOM, 2020-January https://doi.org/10.1109/GLOBECOM42002.2020.9348021