Energy Efficiency with a low power consumption of 5G Networks by using Machine Learning

dc.contributor.authorTasin, Md. Sami Al Jaber
dc.contributor.authorHossain, Md. Tuzammal
dc.date.accessioned2022-05-31T03:30:39Z
dc.date.available2022-05-31T03:30:39Z
dc.date.issued2020-04
dc.description.abstractIn an industry close to green power generation and minimizing power loss, power performance is more important than ever in the Wi-Fi community. Basic elements of community research and design. The 5G community is expected to offer a wide variety of products, including improved mobile broadband, word of mouth, highly reliable large equipment, and intermittent delays. A diverse community that uses many technological advances to provide a wide range of Wi-Fi products. The 5G network adopts a variety of technologies such as software defined networking, community function virtualization, third party computing, cloud computing, and small base stations to meet many needs. Therefore, the performance of electricity is the most important. To help achieve the mission of power productivity in the device community approach, you must be in a prime position and then win a huge fan in the studio community., Consider the Device Art utility. Master the 5G community strategy to power accessible, adjacent, and central communities. Based on the overview, we proposed the classification of plans to introduce 5G equipment to improve power productivity. We have discussed many of the challenges that device mastering can solve in terms of 5G power performance. Finally, we talk about many of the problems we want to solve. Use the full capabilities of the device domain to improve electrical performance in 5G networks. The survey provides a wide range of ideas related to the proliferation of 5G devices that will solve power performance issues in virtualization and help optimize, power distribution, and implement 5G technology. Decorate the power show.
dc.identifier.otherhttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/8113
dc.identifier.urihttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/8113
dc.language.isoen_US
dc.publisherDaffodil International University
dc.sourceDIU Institutional Repository
dc.subjectEnergy efficiency
dc.subjectPower
dc.subject5G network
dc.subjectMachine learning
dc.titleEnergy Efficiency with a low power consumption of 5G Networks by using Machine Learning
dc.typeArticle

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