Optimum power allocation for parallel Gaussian channels with arbitrary input distributions
The mutual information of independent parallel Gaussian-noise channels is maximized, under an average power constraint, by independent Gaussian inputs whose power is allocated according to the waterfilling policy. In practice, discrete signalling constellations with limited peak-to-average ratios (m...
| Autores: | , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2006 |
| País: | España |
| Institución: | Universitat Pompeu Fabra |
| Repositorio: | Repositorio Digital de la UPF |
| OAI Identifier: | oai:repositori.upf.edu:10230/16123 |
| Acceso en línea: | http://hdl.handle.net/10230/16123 http://dx.doi.org/10.1109/TIT.2006.876220 |
| Access Level: | acceso abierto |
| Palabra clave: | Ràdio -- Interferències Tractament del senyal Gaussian Channels Power Allocation Waterfilling Channel Capacity Mutual Information MMSE |
| Sumario: | The mutual information of independent parallel Gaussian-noise channels is maximized, under an average power constraint, by independent Gaussian inputs whose power is allocated according to the waterfilling policy. In practice, discrete signalling constellations with limited peak-to-average ratios (m-PSK, m-QAM, etc) are used in lieu of the ideal Gaussian /nsignals. This paper gives the power allocation policy that maximizes the mutual information /nover parallel channels with arbitrary input distributions. Such policy admits a graphical interpretation, referred to as mercury/waterfilling, which generalizes the waterfilling solution and allows retaining some of its intuition. The relationship between mutual information of Gaussian channels and nonlinear minimum mean-square error proves key to solving the power allocation problem. |
|---|