Application of compressive sensing to sparse channel estimation
By Christian R. Berger, Zhaohui Wang, Jianzhong Huang, Shengli Zhou. IEEE Communications Magazine, 2010
585 citations recorded by the source corpus.
Subfields: Computational Mechanics, Electrical and Electronic Engineering, Ocean Engineering
View Paper: https://www.semanticscholar.org/paper/8a9604bea1727dd71f26e6eeb8f96b9943300d68
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Paolo Rocca
- Giacomo Oliveri
- Andrea Massa
- Byonghyo Shim
- Dong In Kim
- Jun Won Choi
- Yacong Ding
- Mugen Peng
In Electrical and Electronic Engineering
- Paolo Rocca
- Giacomo Oliveri
- Andrea Massa
- Yaohua Sun
- Mugen Peng
- Chonggang Wang
- Dong In Kim
- Bhaskar D. Rao
In Ocean Engineering
Citations in the corpus
- Compressed Sensing for Wireless Communications: Useful Tips and Tricks (2015, background): “paths, the channel can be readily modeled as a sparse vector [23].”
- Channel estimation for OFDM systems (2017, background): “The improvement in the performance of sparse tap-based channel estimation which results from using finer, over-complete dictionaries comes at increased computational complexity, and further studies revealed a strongly diminishing return for finer dictionaries [7].”
- Compressive Sensing in Electromagnetics - A Review (2015, background): “, imaging [3, 4], audio/video capture [5], and communications [6]) has been strongly motivated.”
- Distributed Compressive CSIT Estimation and Feedback for FDD Multi-User Massive MIMO Systems (2014, background): “There is one important observation for massive MIMO systems, which can help to address the above issues.”
- Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues (2016, methodology): “The sparsity patterns in signa l processing have been exploited for a high efficient system design [62].”