Abstract
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 signaling constellations with limited peak-to-average ratios (m-PSK, m-QAM, etc.) are used in lieu of the ideal Gaussian signals. This paper gives the power allocation policy that maximizes the mutual information over 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 (MMSE) proves key to solving the power allocation problem.
Original language | English (US) |
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Pages (from-to) | 3033-3051 |
Number of pages | 19 |
Journal | IEEE Transactions on Information Theory |
Volume | 52 |
Issue number | 7 |
DOIs | |
State | Published - Jul 2006 |
All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Science Applications
- Library and Information Sciences
Keywords
- Channel capacity
- Gaussian channels
- Minimum mean-square error (MMSE)
- Mutual information
- Power allocation
- Waterfilling