Operations Research
Convex Optimization
Power Control in Wireless Cellular Networks
Foundations and Trends® in Networking
Crosstalk channel estimation via standardized two-port measurements
EURASIP Journal on Advances in Signal Processing
Energy-robustness tradeoff in cellular network power control
IEEE/ACM Transactions on Networking (TON)
Green DSL: energy-efficient DSM
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Robust minimum variance beamforming
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Wireless Communications
Cross-Layer Design for Lifetime Maximization in Interference-Limited Wireless Sensor Networks
IEEE Transactions on Wireless Communications
Price-based distributed algorithms for rate-reliability tradeoff in network utility maximization
IEEE Journal on Selected Areas in Communications
A framework for uplink power control in cellular radio systems
IEEE Journal on Selected Areas in Communications
| Hi-index | 35.68 |
In recent years an increasing effort was made to reduce the energy consumption in digital subscriber line equipment. Dynamic spectrum management (DSM) has been identified as one promising method to achieve energy-efficiency in discrete multitone based systems. An open research question is how to ensure system robustness when applying highly optimized energy-efficient spectrum management. In this paper, we study the problem of uncertainty in crosstalk noise and parameters, the knowledge of which is indispensable for many DSM algorithms. We introduce robust optimization for spectrum balancing as a technique to achieve feasibility of the optimal power-allocation under a deterministic parameter uncertainty model. This can be seen as an extension of current schemes for spectrum balancing. As a special case we consider the simple strategy of scaling the crosstalk parameters to theirworst-case values, which corresponds to a specific uncertainty model and entails no changes to current DSM algorithms. Finally, we quantify the benefit in worst-case performance and the price in terms of energy by simulations.