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Energy collection wireless relay network throughput maximum method based on deep reinforcement learning

A wireless relay network and reinforcement learning technology, applied in the field of energy-intensive wireless relay networks, can solve the problems of huge energy consumption and greenhouse gas emissions, and achieve the effects of increasing profits, maximizing throughput, and reducing power consumption.

Active Publication Date: 2019-01-11
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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  • Energy collection wireless relay network throughput maximum method based on deep reinforcement learning
  • Energy collection wireless relay network throughput maximum method based on deep reinforcement learning
  • Energy collection wireless relay network throughput maximum method based on deep reinforcement learning

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Embodiment Construction

[0052] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0053] refer to figure 1, a method for maximizing the throughput of energy-intensive wireless relay networks based on deep reinforcement learning, in other words, through joint time scheduling and power allocation to achieve maximum system benefit with end-to-end maximum throughput. 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, time scheduling and power allocation are optimized through deep reinforcement learning to achieve the maximum transmission rate. The invention proposes a renewable energy optimization method for maximizing throughput for the time scheduling and power control problems in energy-collecting wireless relay networks under the condition of limited data cache and energy storage batteries. The method includes the following ...

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Abstract

An energy collection wireless relay network throughput maximum method based on deep reinforcement learning comprises the following steps of: 1) achieving the maximum throughput through reuseable energy optimization management in an energy collection wireless relay network, wherein the optimization problem describes a multi-variable optimization problem; 2) decomposing a problem P1 into two portions for optimization: power sub optimization and slot time sub optimization, namely, the reinforcement learning algorithm is employed to optimize a variable pi and a variable as shown in the descriptionto obtain an optimal ri. The present invention provides a method for achieving the maximum throughput and the maximum system benefit in the energy collection wireless relay network through combination of time dispatching and power distribution.

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 deep reinforcement 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 ba...

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Application Information

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IPC IPC(8): H04W52/02H04W72/04
CPCH04W52/0206H04W72/0446H04W72/0473Y02D30/70
Inventor 黄亮冯旭冯安琪黄玉蘋钱丽萍吴远
Owner ZHEJIANG UNIV OF TECH
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