Name
Abstract | ||
|---|---|---|
Distributed MIMO communications involve multiple transmitters and receivers organizing themselves into virtual antenna arrays. As these carry individual clocks and oscillators that drift, maintaining sychronization requires that the frequency and the unwrapped phase of each oscillator be tracked using Kalman filters. Kalman filters in turn are sensitive to how well the process and measurement noise variances are known. Existing methods for estimating unknown system variances do not work well for oscillators as the process noise variances are very small (orders of 10
<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-21</sup>
). In this paper we modify the most advanced technique for estimating the noise variances to develop a scheme that leads to faster and more accurate estimation of the noise variances, using fewer observations. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ACSSC.2015.7421127 | 2015 49th Asilomar Conference on Signals, Systems and Computers |
Keywords | Field | DocType |
Kalman filtering,oscillator tracking,distributed MIMO communications,virtual antenna arrays,system variances | Oscillation,Computer science,Control theory,Process noise,MIMO,Electronic engineering,Kalman filter | Conference |
Citations | PageRank | References |
0 | 0.34 | 17 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Benjamin Peiffer | 1 | 5 | 1.76 |
| Goguri, S. | 2 | 2 | 2.74 |
| Soura Dasgupta | 3 | 679 | 96.96 |
| R. Mudumbai | 4 | 1020 | 70.72 |