1 research outputs found
Improved Dual Decomposition Based Optimization for DSL Dynamic Spectrum Management
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, almost all 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, CA-DSB) can be improved by one order of magnitude. Furthermore we apply
the improved dual decomposition approach to other DSM algorithms (OSB, ISB,
ASB, (MS)-DSB, 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 multi-user DSL scenarios