Distributed multiuser power control for digital subscriber lines

By Wei Yu, G. Ginis, John M. Cioffi. IEEE J. Sel. Areas Commun., 2002

882 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/9ab45f8865a1ce2c970550b1ddb25316f092e61b

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

Optimized signaling for MIMO interference systems with feedback (2003, methodology): “A distributed power control scheme involving iterative water-filling was proposed [10] for the digital subscriber line (DSL) interference channel.”
Learning to Rest: A Q-Learning Approach to Flying Base Station Trajectory Design with Landing Spots (2018, result): “While the literature on power control is abundant including for decentralized cases [3] [4], and moreover deep learning approaches [5] [6], this research departs in significant ways from past work in that we consider that each decision making agent (i.”
Spectrum sharing for unlicensed bands (2005, background): “In the next section we analyze how the lack of cooperation among systems may affect the set of achievable rates.”
A game-theoretic approach to energy-efficient power control in multicarrier CDMA systems (2005, background): “We will briefly comment on the joint power and rate control problem at the end of Section IV.”
Convex Optimization in Signal Processing and Communications (2009, background): “It can be seen from the preceding lemma that under the Slater condition, the dual optimal set M * is nonempty.”
Learning to Cooperate in Decentralized Wireless Networks (2018, result): “While the literature on power control is abundant including for decentralized cases [3] [4], and moreover deep learning approaches [5] [6], this research departs in significant ways from past work in that we consider that each decision making agent (i.”
Dual methods for nonconvex spectrum optimization of multicarrier systems (2006, background): “This new approach bears some resemblance to the IWF al-gorithm [7].”
Learning to Optimize: Training Deep Neural Networks for Interference Management (2017, neutral): “in addressing wireless resource management problems. Well-known optimization-based algorithms for such purposes include those developed for power control (e.g., iterative water-ﬁlling type algorithms [4], [5], interference pricing [6], SCALE [7]), transmit/receive beamformer design (e.g., the WMMSE ”
Optimal Linear Precoding Strategies for Wideband Noncooperative Systems Based on Game Theory—Part I: Nash Equilibria (2007, methodology): “A vector power control game was proposed in [23] to maximize the information rates (under constraints on the transmit power) of two users in a DSL system, modeled as a frequency-selective Gaussian interference channel.”
Optimal multiuser spectrum balancing for digital subscriber lines (2006, methodology): “Included are iterative waterfilling [6], the reference noise method, flat PBO, and the reference PSD method, which is currently adopted in VDSL standards [15].”
Distributed interference compensation for wireless networks (2006, methodology): “Figure 3 compares the performance of the PADP algorithm with the iterative waterfilling (IWF) algorithm [10], in which each transmitter allocates power to maximize 8”
Decomposition by Partial Linearization: Parallel Optimization of Multi-Agent Systems (2013, background): “Unfortunately, the gradient schemes [26] suffer from slow convergence and do not exploit any degree of convexity that might be present in the sum-utility function; [27]–[29] hinge crucially on the special log-structure of the users’ rate functions; and [30] is based on the connection with a…”
A Distributed, Asynchronous and Incremental Algorithm for Nonconvex Optimization: An ADMM Based Approach (2014, background): “In [5], [6], the authors show that the well-known iterative water-ﬁlling algorithm [8], [9] can be implemented in a totally asynchronous manner, as long as the interference among the users are weak enough.”
Convex Optimization, Game Theory, and Variational Inequality Theory (2010, methodology): “In Figure 5, we show an example of application of the sequential and the simultaneous version of Algorithm 1, which are the well-known sequential IWFA and simultaneous IWFA [5], [6], [8], and [35].”
Cognitive computation and communication: A complement solution to cloud for IoT (2016, background): “An example of such an utility function is the multi-channel sum data rates in waterfilling games [68].”