A proximal-based decomposition method for convex minimization problems
Mathematical Programming: Series A and B
Approximations in proximal bundle methods and decomposition of convex programs
Journal of Optimization Theory and Applications
Alternating direction splittings for block angular parallel optimization
Journal of Optimization Theory and Applications
Understanding digital subscriber line technology
Understanding digital subscriber line technology
Smooth minimization of non-smooth functions
Mathematical Programming: Series A and B
Duality gap estimation and polynomial time approximation for optimal spectrum management
IEEE Transactions on Signal Processing
SCALE: a low-complexity distributed protocol for spectrum balancing in multiuser DSL networks
IEEE Transactions on Information Theory
Fair greening for DSL broadband access
ACM SIGMETRICS Performance Evaluation Review
Green DSL: energy-efficient DSM
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Autonomous Spectrum Balancing for Digital Subscriber Lines
IEEE Transactions on Signal Processing
Global Concave Minimization for Optimal Spectrum Balancing in Multi-User DSL Networks
IEEE Transactions on Signal Processing - Part I
Distributed Spectrum Management Algorithms for Multiuser DSL Networks
IEEE Transactions on Signal Processing - Part I
Power Control By Geometric Programming
IEEE Transactions on Wireless Communications
Dynamic spectrum management for next-generation DSL systems
IEEE Communications Magazine
Distributed multiuser power control for digital subscriber lines
IEEE Journal on Selected Areas in Communications
Computational Optimization and Applications
| Hi-index | 35.68 |
Dynamic spectrum management (DSM) has been recognized as a key technology to significantly improve the performance of digital subscriber line (DSL) broadband access networks. The basic concept of DSM is to coordinate transmission over multiple DSL lines so as to mitigate the impact of crosstalk interference amongst them. Many algorithms have been proposed to tackle the nonconvex optimization problems appearing in DSM, many of them relying on a standard subgradient based dual decomposition approach. In practice however, this approach is often found to lead to extremely slow convergence or even no convergence at all, one of the reasons being the very difficult tuning of the stepsize parameters. In this paper we propose a novel improved dual decomposition approach inspired by recent advances in mathematical programming. It uses a smoothing technique for the Lagrangian combined with an optimal gradient based scheme for updating the Lagrange multipliers. The stepsize parameters are furthermore selected optimally removing the need for a tuning strategy. With this approach we show how the convergence of current state-of-the-art DSM algorithms based on iterative convex approximations (SCALE and CA-DSB)can be improvedby one order of magnitude. Furthermore we apply the improved dual decomposition approach to other DSM algorithms (OSB, ISB, ASB, (MS)-DSB, and MIW) and propose further improvements to obtain fast and robust DSM algorithms. Finally, we demonstrate the effectiveness of the improved dual decomposition approach for a number of realistic multiuser DSL scenarios.