An Extremal Inequality Motivated by Multiterminal Information Theoretic Problems

By Tie Liu, Pramod Viswanath. 2006

216 citations recorded by the source corpus.

We prove a new extremal inequality, motivated by the vector Gaussian broadcast channel and the distributed source coding with a single quadratic distortion constraint problem. As a corollary, this inequality yields a generalization of the classical vector entropy-power inequality (EPI). As another corollary, this inequality sheds insight into maximizing differential entropy of a sum of jointly distributed random variables, generalizing a classical result of Cover and Zhang

Subfields: Computational Mechanics, Computer Networks and Communications, Electrical and Electronic Engineering

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. V. Sreekanth Annapureddy
2. Venugopal V. Veeravalli
3. Amir Keyvan Khandani
4. Biao Chen
5. Abolfazl Seyed Motahari
6. Sewoong Oh
7. Prateek Jain

In Computer Networks and Communications

1. Amir Keyvan Khandani
2. Abolfazl Seyed Motahari
3. Pablo Piantanida
4. Joffrey Villard
5. Shlomo Shamai
6. Ashish Khetan
7. Gerhard Kramer
8. V. Sreekanth Annapureddy

In Electrical and Electronic Engineering

1. V. Sreekanth Annapureddy
2. Venugopal V. Veeravalli
3. Amir Keyvan Khandani
4. Abolfazl Seyed Motahari
5. Pablo Piantanida
6. Joffrey Villard
7. Gerhard Kramer
8. Shlomo Shamai

Citations in the corpus

• PacGAN: The Power of Two Samples in Generative Adversarial Networks (2017, background): “, [26], [27], [28], [29], [30], [31], [32], [33], [34]) where operational interpretations of mutual information and corresponding data processing inequalities have given rise to simple proofs of strong technical results.”
• Secure Multiterminal Source Coding With Side Information at the Eavesdropper (2011, background): “A deep analy sis ong with recent extremal inequalities [56], [57] may yield the expected converse.”
• A Note on the Secrecy Capacity of the Multi-antenna Wiretap Channel (2007, background): “Note that this provided a sufficient condition to evaluate optimality for a specific choice of ( U, X ).”
• Capacity bounds for the Gaussian Interference Channel (2008, methodology): “In [14], it is shown that for all μ ≥ 1, this optimization problem has a Gaussian optimal solution for al l positive definite matricesQZ1 andQZ2 .”
• Gaussian Interference Networks: Sum Capacity in the Low-Interference Regime and New Outer Bounds on the Capacity Region (2008, methodology): “Although the extremal inequalities proposed in [22] are more general than EPI, both are equivalent for the purpose of this paper.”
• A New Outer Bound and the Noisy-Interference Sum–Rate Capacity for Gaussian Interference Channels (2007, background): “The new bounds are based on a genie-a ided pproach and a recently proposed extremal inequality [13].”