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.
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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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