Robust Power Management via Learning and Game Design

By Zhengyuan Zhou, Panayotis Mertikopoulos, Aris L. Moustakas, N. Bambos, Peter W. Glynn. 2021

30 citations recorded by the source corpus.

Descending to Stability: Robust Power Control for Stochastic Wireless Networks

Subfields: 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 Computer Networks and Communications

1. Tianyi Lin
2. Michael I. Jordan
3. Panayotis Mertikopoulos
4. Zhengyuan Zhou
5. Yi Ma
6. Jan Peters
7. Csaba Szepesvari
8. Nando de Freitas

In Electrical and Electronic Engineering

1. Tianyi Lin
2. Michael I. Jordan
3. Panayotis Mertikopoulos
4. Zhengyuan Zhou
5. Yi Ma

Citations in the corpus

• Optimistic Dual Extrapolation for Coherent Non-monotone Variational Inequalities (2021, methodology): “Meanwhile, [43] used the plain stochastic gradient descent algorithm and obtained the O 1 K result for strongly monotone variational inequalities, which can also be extended to the setting that -weak solution exists.”
• Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games (2020, methodology): “One appealing feature of strongly monotone games is that the finite-time convergence rate can be derived in terms of x t − x ∗ , where x ∗ is the unique Nash equilibrium [Zhou et al., 2020b] (under convex and compact action sets).”