Covariance Matrix Estimation in Massive MIMO
| Autor: | Wolfgang Utschick, David Neumann, Michael Joham |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Minimum mean square error Computer science Covariance matrix Information Theory (cs.IT) Computer Science - Information Theory Applied Mathematics Maximum likelihood 010401 analytical chemistry MIMO 02 engineering and technology Covariance 021001 nanoscience & nanotechnology 01 natural sciences 0104 chemical sciences Matrix (mathematics) Signal Processing Telecommunications link Coherence (signal processing) Electrical and Electronic Engineering 0210 nano-technology Algorithm Computer Science::Information Theory Coherence (physics) Communication channel |
| Zdroj: | IEEE Signal Processing Letters. 25:863-867 |
| ISSN: | 1558-2361 1070-9908 |
| Popis: | Interference during the uplink training phase significantly deteriorates the performance of a massive MIMO system. The impact of the interference can be reduced by exploiting second order statistics of the channel vectors, e.g., to obtain minimum mean squared error estimates of the channel. In practice, the channel covariance matrices have to be estimated. The estimation of the covariance matrices is also impeded by the interference during the training phase. However, the coherence interval of the covariance matrices is larger than that of the channel vectors. This allows us to derive methods for accurate covariance matrix estimation by appropriate assignment of pilot sequences to users in consecutive channel coherence intervals. submitted to IEEE Signal Processing Letters |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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