Optimized transmission for fading multiple-access and broadcast channels with multiple antennas
By Mehdi Mohseni, Rui Zhang, John M. Cioffi. 2006
195 citations recorded by the source corpus.
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
- Rui Zhang
- Emil Björnson
- Yong Zeng
- Eduard Axel Jorswieck
- Ying-Chang Liang
- Tony Q. S. Quek
- Sang Hyun Lee
- Hoon Lee
In Electrical and Electronic Engineering
- Rui Zhang
- Emil Björnson
- Yong Zeng
- Bruno Clerckx
- Ying-Chang Liang
- Eduard Axel Jorswieck
- Robert Schober
- Qingqing Wu
Citations in the corpus
- Deep Learning for Distributed Optimization: Applications to Wireless Resource Management (2019, background): “A class of a long-term design constraint includes the average transmit power budget and the average quality-of-service constraints at users evaluated over fast fading channel coefficients [23], [24].”
- Optimal Resource Allocation in Coordinated Multi-Cell Systems (2013, background): “and sufficient characterization of the Pareto boundary can however be achieved as follows, based on the line of work in [16, 126, 185, 325].”
- Optimal Resource Allocation for OFDMA Downlink Systems (2006, methodology): “In order to find µ∗ � 0 that maximizes g(µ) ,t he update of µ can be efficiently done by using the ellipsoid method until every user’s rate converges [6][ 10 ].”
- Communications and Signals Design for Wireless Power Transmission (2016, methodology): “On the other hand, with the power-profile method [143], the Pareto boundary of C can be characterized by solving the following optimization problem with any given power profile vector α = (α1, · · ·αK) for the K ERs,”
- Optimal beamforming for two-way multi-antenna relay channel with analogue network coding (2008, background): “[ 27 ], an interesting concept so-called rate profile was introduced to efficiently characterize boundary”
- Constrained Deep Learning for Wireless Resource Management (2019, background): “Special cases of (P1) have been widely studied in various network configurations such as cognitive radios [11] and multi-antenna multi-user systems [10].”