Gradient of mutual information in linear vector Gaussian channels

By D. P. Palomar, Sergio Verdú. IEEE Transactions on Information Theory, 2006

365 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/4dab87f807d5d8442fb5865806dc51aa46b31821

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. E. de Carvalho
2. Rajiv Agarwal
3. John M. Cioffi
4. Rick S. Blum
5. Søren Skovgaard Christensen
6. Yang Yang

In Computer Networks and Communications

1. Rick S. Blum
2. David Gesbert
3. E. de Carvalho
4. Rajiv Agarwal
5. Søren Skovgaard Christensen
6. John M. Cioffi
7. Yang Yang
8. Wei Yu

Citations in the corpus

• Complex-Valued Matrix Differentiation: Techniques and Key Results (2007, background): “Four additional references that give a brief treatment of the case of real-valued scalar functions which depend complex-valued vectors are Appendix B of [6], Appendix 2.”
• MIMO radar waveform design based on mutual information and minimum mean-square error estimation (2007, result): “It is noteworthy that this fundamental relationship was recently unveiled in [38] and [39], demonstrating that the derivative of the MI (nats) with respect to the SNR is equal to the MMSE, regardless of the input statistics.”
• Weighted sum-rate maximization using weighted MMSE for MIMO-BC beamforming design (2008, methodology): “The MMSE-receive fi lter at user k is given as: The MSE-matrix for user k given that the MMSE-receive fi lter is applied can be written as [15]: We refer to E k as the MMSE-matrix.”