14,768 research outputs found
Quantifying Potential Energy Efficiency Gain in Green Cellular Wireless Networks
Conventional cellular wireless networks were designed with the purpose of
providing high throughput for the user and high capacity for the service
provider, without any provisions of energy efficiency. As a result, these
networks have an enormous Carbon footprint. In this paper, we describe the
sources of the inefficiencies in such networks. First we present results of the
studies on how much Carbon footprint such networks generate. We also discuss
how much more mobile traffic is expected to increase so that this Carbon
footprint will even increase tremendously more. We then discuss specific
sources of inefficiency and potential sources of improvement at the physical
layer as well as at higher layers of the communication protocol hierarchy. In
particular, considering that most of the energy inefficiency in cellular
wireless networks is at the base stations, we discuss multi-tier networks and
point to the potential of exploiting mobility patterns in order to use base
station energy judiciously. We then investigate potential methods to reduce
this inefficiency and quantify their individual contributions. By a
consideration of the combination of all potential gains, we conclude that an
improvement in energy consumption in cellular wireless networks by two orders
of magnitude, or even more, is possible.Comment: arXiv admin note: text overlap with arXiv:1210.843
solveME: fast and reliable solution of nonlinear ME models.
BackgroundGenome-scale models of metabolism and macromolecular expression (ME) significantly expand the scope and predictive capabilities of constraint-based modeling. ME models present considerable computational challenges: they are much (>30 times) larger than corresponding metabolic reconstructions (M models), are multiscale, and growth maximization is a nonlinear programming (NLP) problem, mainly due to macromolecule dilution constraints.ResultsHere, we address these computational challenges. We develop a fast and numerically reliable solution method for growth maximization in ME models using a quad-precision NLP solver (Quad MINOS). Our method was up to 45 % faster than binary search for six significant digits in growth rate. We also develop a fast, quad-precision flux variability analysis that is accelerated (up to 60Ă— speedup) via solver warm-starts. Finally, we employ the tools developed to investigate growth-coupled succinate overproduction, accounting for proteome constraints.ConclusionsJust as genome-scale metabolic reconstructions have become an invaluable tool for computational and systems biologists, we anticipate that these fast and numerically reliable ME solution methods will accelerate the wide-spread adoption of ME models for researchers in these fields
GREEN RADIO COMMUNICATIONS IN 5G NETWORKS TO IMPROVE ENERGY EFFICIENCY AND REDUCE GLOBAL WARMING
The technology of green radio communication helps in reducing the emission of carbon and also helps in the process of reducing the consumption of energy by the base stations of wireless networks. In addition to that, with the help of tools such as Information Communication Technology (ICT) and Multi-Hop Relay Network (MHR), the functionalities and the operational attributes of the technology of green radio communication can be improved and the process of energy consumption gets better as well. It is found from the discussion that green networking technology has mainly two core components and the two core components are energy awareness and energy efficiency. The ability of the network to measure the cost per packet is called energy awareness. On the other hand, the ability of a network to decrease the contribution of carbon and extend the lifetime of the network can be called energy efficiency. In addition, the implementation of the technology of green radio communication helps in mitigating the issue of future energy crises. Additionally, it has also been understood that Green communication in terms of energy efficiency can help IT industry which has been extensively criticised for the contribution of the carbon emissions as well as the failure to respond to the negative impact on the whole climate. In fact, the next generation networks have imposed the challenges in terms of the provision of the energy efficient solutions which are provided and the transportation of the data along with the huge range of the quality of the services requirement as well as the tolerance of lower optimum services.The technology of green radio communication helps in reducing the emission of carbon and also helps in the process of reducing the consumption of energy by the base stations of wireless networks. In addition to that, with the help of tools such as Information Communication Technology (ICT) and Multi-Hop Relay Network (MHR), the functionalities and the operational attributes of the technology of green radio communication can be improved and the process of energy consumption gets better as well. It is found from the discussion that green networking technology has mainly two core components and the two core components are energy awareness and energy efficiency. The ability of the network to measure the cost per packet is called energy awareness. On the other hand, the ability of a network to decrease the contribution of carbon and extend the lifetime of the network can be called energy efficiency. In addition, the implementation of the technology of green radio communication helps in mitigating the issue of future energy crises. Additionally, it has also been understood that Green communication in terms of energy efficiency can help IT industry which has been extensively criticised for the contribution of the carbon emissions as well as the failure to respond to the negative impact on the whole climate. In fact, the next generation networks have imposed the challenges in terms of the provision of the energy efficient solutions which are provided and the transportation of the data along with the huge range of the quality of the services requirement as well as the tolerance of lower optimum services
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