Name
Abstract | ||
|---|---|---|
In this paper, we consider the fundamental problem of channel estimation in multiple-input multiple-output (MIMO) amplify-and-forward (AF) relaying systems operating over random channels. Using the Bayesian framework, linear minimum mean square error (LMMSE) and expectation-maximization (EM) based maximum a posteriori (MAP) channel estimation algorithms are developed, that provide the destination with full knowledge of all channel parameters involved in the transmission. The performance of the proposed algorithms is evaluated in terms of the mean square error (MSE) as a function of the signal-to-noise ratio (SNR) during the training interval. Our simulation results show that the incorporation of prior knowledge into the channel estimation algorithm offers improved performance, especially in the low SNR regime. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/VETECF.2011.6093283 | 2011 IEEE VEHICULAR TECHNOLOGY CONFERENCE (VTC FALL) |
Keywords | Field | DocType |
mimo,vectors,mean square error,maximum likelihood estimation,estimation,expectation maximization | Mathematical optimization,Computer science,MIMO,Minimum mean square error,Estimation,Algorithm,Mean squared error,Maximum likelihood,Communication channel,Electronic engineering,Maximum a posteriori estimation,Bayesian probability | Conference |
ISSN | Citations | PageRank |
1550-2252 | 5 | 0.48 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Panagiota Lioliou | 1 | 44 | 2.27 |
| Mats Viberg | 2 | 1043 | 126.67 |
| Michail Matthaiou | 3 | 1842 | 108.74 |