Improving Energy Efficiency of LTE Networks by Applying Genetic Algorithm (GA)

Lin, Kongluan

doi:10.1109/dasc.2011.108

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…This approach reaches remarkable improvements in terms of coverage, throughput, and power minimization when compared with the conventional fixed antenna gain schemes. Energy efficiency, in term of optimizing power consumption in LTE networks using GAs have recently become an important topic . Regarding this, and also linked to multicast problems in LTE, Asimakis et al have explored the use of a GA to optimize the power consumption of the complete network along with assuring an acceptable spectral efficiency for most users.…”

Section: Related Workmentioning

confidence: 99%

“…Most of them tackle exclusively the resource allocation problem in DL, [104][105][106][107][108][109][110] while only a few research works focus on the uplink. 111,112 Along with these approaches of allocating resource blocks (RBs), there are other research works that also include the problem of energy saving using GAs, on the allocation of radio resources themselves (for instance, by minimizing emitted power) 110,[113][114][115] or on eNB deployment 116,117 or on redimensioning the number of eNBs for Green LTE. 118 Beginning with those studies that focus on resource allocation in the DL, Yang et al 104 successfully explores the use of a GA to assign resources in LTE by combining the conventional frequency scheme 119,120 with semismart antennas, which generate modifiable coverage patterns for eNBs.…”

Section: User Assignment In Lte Via Evolutionary Computingmentioning

confidence: 99%

“…Energy efficiency, in term of optimizing power consumption in LTE networks using GAs have recently become an important topic. [113][114][115] Regarding this, and also linked to multicast problems in LTE, Asimakis et al 114 have explored the use of a GA to optimize the power consumption of the complete network along with assuring an acceptable spectral efficiency for most users. This is done by adjusting the power levels in each eNB.…”

Section: User Assignment In Lte Via Evolutionary Computingmentioning

confidence: 99%

“…Most of them tackle exclusively the resource allocation problem in DL, while only a few research works focus on the uplink . Along with these approaches of allocating resource blocks (RBs), there are other research works that also include the problem of energy saving using GAs, on the allocation of radio resources themselves (for instance, by minimizing emitted power) or on eNB deployment or on redimensioning the number of eNBs for Green LTE…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Near‐optimal user assignment in LTE mobile networks with evolutionary computing

Arco-Vega

Cuadra

Portilla-Figueras

et al. 2016

Trans. Emerging Tel. Tech.

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This paper tackles the problem of users assignment to evolved NodeBs in long‐term evolution mobile networks. We propose a model that aims at assigning users by minimizing a function called download time of the complete system, defined as the minimum time required for all the users in the system to complete their downloads. This strategy helps the network use its resources in a more balanced way, by assigning users from cells (which otherwise would be overloaded when using a simple signal‐to‐interference‐plus‐noise ratio‐based approach) to others with less load. The user‐cell association problem is, in general, a combinatorial NP hard problem. Although other approaches indirectly tackle this complexity by transforming the problem into another whose optimal solution is less difficult (but still with very high computational cost), we tackle directly our download time of the complete system minimization problem by finding an approximate solution provided by a specific implementation of an evolutionary algorithm, which is based on a long‐term evolution domain‐specific initial population along with fully tailored operators adapted to this assignment problem. A variety of experiments in different realistic scenarios point out that the proposed method outperforms the conventional signal‐to‐interference‐plus‐noise ratio‐based and load balancing approaches, specially in urban and dense urban environments (where the assignment is more critical in terms of capacity). We have also tested our approach in simple scenarios that mimic heterogeneous networks and ultra‐dense networks in which the number of users is similar to that of nodes, proving its beneficial properties. Our method is aimed at the design/dimensioning stage and not to the operation stage, and in this sense, it is an off‐line algorithm that could be useful to make better decisions on cost‐effective network dimensioning and upgrading.

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Section: Related Workmentioning

confidence: 99%