“…In [1], the authors proposed the unified framework in which both rate maximization and ATP minimization are objectives. The ATP optimization was also addressed in recent publications [6][7] [8]. However when reducing the ATPs below a certain threshold (say 11.5 dBm), the additional decrease in line driver (LD) power consumption is marginal.…”
DSL systems have traditionally been tuned to maximize rate or margin, subject to constraints on transmitted power. Recently attention is focusing on reducing power consumption. We formulate a new DSL tuning framework in which the line driver power consumption is part of the objective function. The framework uses a model for line driver power consumption as a function of transmitted power, along with the traditional model for data rate as a function of transmitted power. Using this framework, we show that it is possible to achieve significant reductions in line driver power consumption while incurring modest reductions in data rate. To solve the resulting nonlinear problem efficiently, we propose a new heuristic algorithm based on the combination of linearization of the objective and subsequent linear programming techniques. Numerical studies illustrate the tradeoffs that are available in multi-line systems.
“…In [1], the authors proposed the unified framework in which both rate maximization and ATP minimization are objectives. The ATP optimization was also addressed in recent publications [6][7] [8]. However when reducing the ATPs below a certain threshold (say 11.5 dBm), the additional decrease in line driver (LD) power consumption is marginal.…”
DSL systems have traditionally been tuned to maximize rate or margin, subject to constraints on transmitted power. Recently attention is focusing on reducing power consumption. We formulate a new DSL tuning framework in which the line driver power consumption is part of the objective function. The framework uses a model for line driver power consumption as a function of transmitted power, along with the traditional model for data rate as a function of transmitted power. Using this framework, we show that it is possible to achieve significant reductions in line driver power consumption while incurring modest reductions in data rate. To solve the resulting nonlinear problem efficiently, we propose a new heuristic algorithm based on the combination of linearization of the objective and subsequent linear programming techniques. Numerical studies illustrate the tradeoffs that are available in multi-line systems.
“…More recently, researchers have begun to apply spectral optimization techniques to power minimization. Some recent approaches take ATP minimization as the objective, subject to rate constraints [10,13,14]. In [4] and [5], the authors proposed a unified optimization framework in which the objective can incorporate rate maximization and ATP minimization, with constraints also based on rate and power.…”
Section: Approach 2: Joint Optimization Of Power Consumptionmentioning
Growth of high-throughput applications, such as video services, peer-to-peer (P2P) communication, and online storage, are driving upgrades to the capacity of access networks. At the same time, the quality of service (QoS) requirements of many of these applications require more sophisticated access equipment with rich features. These features normally come at the expense of higher power consumption. The challenge today is to build access network hardware and software that is capable of reducing power consumption while meeting the increased QoS and capacity requirements.
“…(c) Figure 8 Fair greening simulation results for two-user DSL scenario of Fig. 5a: a Normalized data rate sum performance versus greening for different greening policies, b Normalized data rates (top) and normalized transmit powers (bottom) for two modems (blue: modem 1, red: modem 2) for different greening policies when 50% greening is applied, c Greening fairness index F (22) versus greening for different greening policies. For Greening 4B and 4C, the values δ = 4 10 5 and δ = 90 were used, respectively.…”
Section: ) Fair Greening Simulations and Analysismentioning
confidence: 99%
“…Recently, also a limited number of energy-aware DSM designs have been proposed [7,20,22,24], such as the minimization of the (weighted) sum of transmit powers subject to data rate constraints…”
Section: A Green Dsl Frameworkmentioning
confidence: 99%
“…In [4] iterative distributed DSM algorithms are proposed for minimizing the sum power for each user. In [7,[20][21][22][23][24] the problem of minimizing the (weighted) sum transmit powers of the multiuser DSL system has been considered, where different solution procedures are proposed: in [20,24] procedures are proposed based on iterative geometric programming (GP) approximation; in [21,23], power back-off-based procedures are proposed; in [22,25,26], solutions are proposed that make use of existing rate-adaptive DSM algorithms; in [27], power consumption is reduced and stability is improved by using a band-preference method. All the above research contributions on improving the power efficiency of DSL systems by the usage of DSM are understood to fall under a common term referred to as 'Green DSL'.…”
Dynamic spectrum management (DSM) is recognized as a promising technology to reduce power consumption in DSL access networks. However, the correct formulation of power-aware DSM problem statements requires a proper understanding of greening, i.e., reducing power consumption. In this paper, we, therefore, investigate greening and show that it can be decomposed into two dimensions: the price of greening and the fairness of greening. We first analyze the price of greening, providing theoretical bounds on the power-rate trade-off and identifying the typical trends that can be expected in practice, with some particularly promising results. Then, we introduce the fairness dimension, where we show that fairness becomes crucial when reducing power consumption. We propose four different fair greening policies that can be used to obtain a favorable trade-off between fast, fair and green DSL operation. Finally, we evaluate and quantify the corresponding trade-offs for realistic DSL access networks.
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