Small cells, specific femtocells aim to improve throughput and customer experience indoors, which prospectively develop into an indispensable part to complement macrocells. Since network operators progressively consid...
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Small cells, specific femtocells aim to improve throughput and customer experience indoors, which prospectively develop into an indispensable part to complement macrocells. Since network operators progressively consider ecological and economical sustainable development, energy-saving and interference free have grown to be crucial challenges for "green" femtocells, especially under ultra-dense deployment. To this end, we introduce coordinated multi-point (CoMP) femto-base stations (FBSs) to exploit intra-femto interference and multiple-input single-output beamforming in cognitive underlay to mitigate cross-tier interference, integrated with another key effort to best conserve overall FBSs power. It is formulated as a quasi mixed-integer convex optimization problem, decomposed and carried out separately via innovative transformations. A novel FBSs CoMP-selecting scheme, sensitive to femto-user traffic rate, is proposed to rapidly obtain the effective solution to fundamental CoMP beamforming issue. Furthermore, a policy of diminishing the CoMP set by deactivating feasible no-load FBSs to snooze, is proposed to actually minimize entire power consumption, wherein FBSs static, dynamic and backhauling power is jointly evaluated by accordingly proposed two hierarchical iterative algorithms. Network-level simulations under practical configurations, in contrast to prior work, demonstrate the proposals can achieve a proper tradeoff between retaining significant energy-efficient FBSs operation and assuring preferable quality of consumers experience. (C) 2017 Elsevier B.V. All rights reserved.
In this paper we investigate the effects of replacing the objective function of a 0-1 mixed-integerconvex program (MIP) with a "proximity" one, with the aim of using a black-box solver as a refinement heuri...
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In this paper we investigate the effects of replacing the objective function of a 0-1 mixed-integerconvex program (MIP) with a "proximity" one, with the aim of using a black-box solver as a refinement heuristic. Our starting observation is that enumerative MIP methods naturally tend to explore a neighborhood around the solution of a relaxation. A better heuristic performance can however be expected by searching a neighborhood of an integer solution-a result that we obtain by just modifying the objective function of the problem at hand. The relationship of this approach with primal integer methods is also addressed. Promising computational results on different proof-of-concept implementations are presented, suggesting that proximity search can be quite effective in quickly refining a given feasible solution. This is particularly true when a sequence of similar MIPs has to be solved as, e.g., in a column-generation setting.
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