NOMA system resource allocation method based on optimized sample sampling and storage medium

Abstract

The invention discloses an NOMA system resource allocation method based on optimized sample sampling and a storage medium, and belongs to the technical field of mobile communication and wireless networks. The problems that when resources of an NOMA system are allocated through an existing deep reinforcement learning network, samples with important values are not learned possibly, and the learning rate is low are solved. According to the method, the deep reinforcement learning network based on the sample optimization pool is designed, the current channel state information serves as input, the user sum rate serves as the optimization target, each sample TD error serves as the priority, and the optimal user grouping strategy is output through the deep reinforcement learning network; and meanwhile, the optimal distribution power of each user is output by utilizing the depth deterministic strategy gradient network. According to the method, the occurrence probability of valuable samples is improved by introducing the priorities of the samples, the learning rate of the deep reinforcement learning network can be improved, and the convergence speed is increased. The method is mainly used for resource allocation of the NOMA system.

Description

technical field [0001] The invention relates to a NOMA system resource allocation method, and belongs to the technical field of mobile communication and wireless network. Background technique [0002] In the NOMA (non-orthogonal multiple access technology) system, the transmitting end first groups all users, then allocates different powers to the users, and finally superimposes different users on the same time-frequency resource block and transmits it to the receiving end through a wireless channel. The receiving end uses serial interference elimination technology to demodulate and reconstruct the signal to restore the original signal. It can be seen that the user grouping and power allocation results of the NOMA system will directly affect the performance of the system, so these two issues are often combined for optimization, collectively referred to as NOMA system resource allocation. [0003] In recent years, deep reinforcement learning networks have been used to solve r...

AI Technical Summary

Problems solved by technology

[0004] The present invention is to solve the problem that samples with important value may not be learned when using the existing deep reinforcement learning network to allocate the resources of the NOMA system, and the resulting low learning rate

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