Name
Abstract | ||
|---|---|---|
Many a blind beamforming algorithm, such as C-CAB, uses the signal characteristics to estimate the steering vector. Then it adapts the conventional LCMV algorithm to get the optimum solution. However, the LCMV-like methods are sensitive to the mismatch. In this paper, the cause of this mismatch is precisely discussed. A robust blind beamforming algorithm is presented. Using a neural network structure, the algorithm can decrease the computational complexity and turn into realize in real time. Results of computer simulations are included to support our analysis. |
Year | DOI | Venue |
|---|---|---|
1999 | null | IJCNN |
Keywords | Field | DocType |
neural network,algorithm design and analysis,signal detection,real time,digital signal processing,computer simulation,neural networks,signal analysis,computational complexity,helium | Detection theory,Computer science,Beamforming algorithm,Time delay neural network,Artificial intelligence,Artificial neural network,Machine learning,Computational complexity theory | Conference |
Volume | Issue | ISSN |
5 | null | null |
Citations | PageRank | References |
1 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuxin Chen | 1 | 3 | 1.73 |
| Zhenya He | 2 | 1 | 0.34 |