Name
Abstract | ||
|---|---|---|
Underwater acoustic (UWA) channel is typically sparse. In this paper, a complex Homotopy algorithm is presented and then applied for UWA OFDM channel estimation. Two enhancements that exploit UWA channel temporal correlation for the compressed-sensing(CS)-based channel estimators are proposed. The first one is based on a first-order Gauss-Markov (GM) model which uses the previous channel estimate to assist current one. The other is to use the recursive least-squares (RLS) algorithm together with the CS algorithms to track the time-varying UWA channel. Simulation results show that the Homotopy algorithm offers faster and more accurate UWA channel estimation performance than other sparse recovery methods, and the proposed enhancements offer further performance improvement. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICC.2012.6363756 | ICC |
Keywords | Field | DocType |
homotopy,sparse recovery method,uwa channel temporal correlation,underwater acoustic channel estimation,cs-based channel estimators,time-varying uwa channel,ofdm modulation,complex homotopy algorithm,rls algorithm,underwater acoustic communication,sparse recovery,time-varying channels,acoustic correlation,least squares approximations,compressed sensing,underwater acoustic (uwa) channel,uwa ofdm channel estimation,first-order gm model,recursive estimation,markov processes,recursive least-squares algorithm,first-order gauss-markov model,compressed-sensing-based channel estimators,channel estimation,underwater acoustics,ofdm,correlation,doppler effect | Mathematical optimization,Markov process,Underwater acoustic communication,Computer science,Algorithm,Communication channel,Real-time computing,Homotopy,Compressed sensing,Orthogonal frequency-division multiplexing,Performance improvement,Estimator | Conference |
Volume | Issue | ISSN |
null | null | 1550-3607 E-ISBN : 978-1-4577-2051-2 |
ISBN | Citations | PageRank |
978-1-4577-2051-2 | 3 | 0.39 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chenhao Qi | 1 | 209 | 26.34 |
| Lenan Wu | 2 | 700 | 62.18 |
| Xiaodong Wang | 3 | 35 | 4.73 |