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A method for maximizing throughput of centralized wireless relay networks based on semi-supervised learning

A wireless relay network, semi-supervised learning technology, applied in neural learning methods, wireless communication, biological neural network models, etc., can solve problems such as huge energy consumption and greenhouse gas emissions, increase profits, maximize throughput, The effect of reducing power consumption

Inactive Publication Date: 2018-12-18
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0003] However, the energy consumption generated by densely deploying relay base stations and the resulting greenhouse gas (such as carbon dioxide) emissions are also huge.

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  • A method for maximizing throughput of centralized wireless relay networks based on semi-supervised learning
  • A method for maximizing throughput of centralized wireless relay networks based on semi-supervised learning
  • A method for maximizing throughput of centralized wireless relay networks based on semi-supervised learning

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Embodiment Construction

[0041] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0042] refer to figure 1 , a semi-supervised learning-based method for maximizing the throughput of energy-intensive wireless relay networks, in other words, through joint power allocation and time scheduling to achieve maximum system benefit by maximizing throughput end-to-end. The present invention is based on an energy-collecting wireless relay network system (such as figure 1 shown). In the energy-intensive wireless relay network system, the power allocation and time scheduling are optimized through semi-supervised learning to achieve the maximum transmission rate. The invention proposes a throughput maximization method based on semi-supervised learning for the time scheduling and power control problems in the energy-collecting wireless relay network under the condition of limited data cache and energy storage battery. The method includes the following ...

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Abstract

A method for maximizing the throughput of a centralized radio relay network based on semi-supervised learning comprises the following steps: 1) maximizing the throughput of the centralized radio relaynetwork through regenerative energy optimization management, wherein, the optimization problem is described as a multivariable optimization problem; 2) decomposing that problem P1 into two parts of optimization: power sub-optimization and time slot sub-optimization, i.e. optimizing variables pi and (shown in the description) by semi-supervised learning method to obtain the optimal ri. A method for maximizing system efficiency with maximum throughput in a centralized radio relay network by combining time scheduling and power allocation is provided.

Description

technical field [0001] The invention relates to the technical field of energy-collecting wireless relay networks, in particular to a method for maximizing the throughput of energy-collecting wireless relay networks based on semi-supervised learning. Background technique [0002] Mobile data traffic has been growing exponentially due to the proliferation of wireless devices and emerging multimedia services. Due to channel losses such as path loss, shadowing, and small-scale fading, more and more indoor and edge users may experience low-quality service performance. To overcome this obstacle, relay-assisted access technology has been proposed as a valuable solution to exploit energy efficiency and spatial diversity to improve user service quality indoors and at the cell edge. The relay base station will serve as a relay station for communication between edge users and macro cell base stations. [0003] However, the energy consumption generated by densely deploying relay base ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W52/26H04W52/46H04W72/04G06N3/04G06N3/08
CPCH04W52/267H04W52/46H04W72/0446H04W72/0473G06N3/08G06N3/045Y04S10/50Y02E40/70
Inventor 钱丽萍冯安琪冯旭黄玉蘋黄亮吴远
Owner ZHEJIANG UNIV OF TECH
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