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A non-orthogonal access downlink transmission time optimization method based on deep reinforcement learning

A technology of transmission time and reinforcement learning, applied in the field of communication, can solve problems such as long downlink transmission time, excessive downlink transmission time, and large total energy consumption of base stations, so as to achieve high-quality wireless network experience quality and improve system transmission efficiency.

Active Publication Date: 2021-08-03
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0003] In order to overcome the shortcomings of the prior art that the downlink transmission time is long and the total energy consumption of the base station is large, the present invention provides a non-orthogonal access downlink transmission time based on deep reinforcement learning that minimizes the downlink transmission time and the total energy consumption of the base station Optimization method, the present invention aims at the difficulty of excessive downlink transmission time, mainly considers the use of non-orthogonal access technology to transmit data, and studies a non-orthogonal access downlink transmission time optimization method based on deep reinforcement learning

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  • A non-orthogonal access downlink transmission time optimization method based on deep reinforcement learning
  • A non-orthogonal access downlink transmission time optimization method based on deep reinforcement learning
  • A non-orthogonal access downlink transmission time optimization method based on deep reinforcement learning

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[0054] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0055] refer to figure 1 and figure 2 , a non-orthogonal access downlink transmission time optimization method based on deep reinforcement learning. The implementation of this method can minimize the downlink transmission time and the total energy consumption of the base station under the condition of ensuring that the base station transmits and completes the data volume of all mobile users at the same time. Improve the wireless network experience quality of the entire system. The present invention can be applied to wireless networks, such as figure 1in the scene shown. Designing an optimization method for this problem includes the following steps:

[0056] (1) There are a total of 1 mobile users under the coverage of the base station, and the mobile users use the set Indicates that the base station uses non-orthogonal access technology to send data to...

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Abstract

A non-orthogonal access downlink transmission time optimization method based on deep reinforcement learning, comprising the following steps: (1) There are a total of I mobile users under the coverage of the base station, and a QoS that satisfies the mobile users is proposed, Minimize the downlink transmission time of the base station and the total energy consumption of the base station when the download amount of the mobile user is given; (2) find an optimal downlink transmission time t through the reinforcement learning algorithm * , so that there is an optimal downlink resource consumption; (3) Repeat the iterative process until the optimal downlink transmission time t is obtained * , so that there is optimal downlink resource consumption. The present invention provides a non-orthogonal access downlink transmission time optimization method based on deep reinforcement learning that minimizes downlink transmission time and total energy consumption of a base station.

Description

technical field [0001] The invention belongs to the field of communication, and relates to a non-orthogonal access downlink transmission time optimization method based on deep reinforcement learning. Background technique [0002] The rapid development of mobile Internet services has caused enormous traffic pressure on the cellular wireless access network. Due to limited wireless resources, using non-orthogonal access technology to allow mobile users to share the same channel at the same time provides an effective method for wireless access to achieve ultra-high throughput and large-scale connections in the future 5G network. We aim to minimize the downlink transmission time and the total energy consumption of the base station during the transmission of data from the base station to the corresponding mobile user. We propose a non-orthogonal access downlink transmission time optimization method based on deep reinforcement learning. Contents of the invention [0003] In ord...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04W24/02
CPCH04W24/02Y02D30/70
Inventor 吴远张成倪克杰陈佳钱丽萍黄亮
Owner ZHEJIANG UNIV OF TECH
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