Understanding digital subscriber line technology
Understanding digital subscriber line technology
DSL Advances
Convex Optimization
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 Power Allocation With Rate Constraints in Gaussian Parallel Interference Channels
IEEE Transactions on Information Theory
Dynamic spectrum management for next-generation DSL systems
IEEE Communications Magazine
Vectored transmission for digital subscriber line systems
IEEE Journal on Selected Areas in Communications
Distributed multiuser power control for digital subscriber lines
IEEE Journal on Selected Areas in Communications
Impact of crosstalk channel estimation on the DSM performance for DSL networks
EURASIP Journal on Advances in Signal Processing
An evolutionary algorithm for improved diversity in DSL spectrum balancing solutions
EURASIP Journal on Advances in Signal Processing
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Spectrum balancing (SB) techniques optimize transmission and can significantly improve digital subscriber lines (DSL) services. In the literature, the DSL system optimization is typically formulated as a rate maximization problem. However, there is an increasing interest in minimizing the considerable amount of power consumed by telecommunication networks. Few works in the SB literature have explored algorithms for power minimization. It is known that some existing solutions for rate maximization can be converted into power minimization algorithms. This relation has not been fully explored and, consequently, the area lacks results regarding what can be achieved with power minimization SB algorithms. This work aims to diminish this gap. First, the equivalence between rate maximization and power minimization problems is formalized. Second, extended versions of some rate maximization SB algorithms are proposed for power minimization purposes and evaluated through simulations. In addition, the power-usage capabilities and convergence characteristics of each extended SB algorithms is discussed.