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

Abstract

A NOMA system resource allocation method and storage medium based on optimized sample sampling, belonging to the technical field of mobile communication and wireless network. In order to solve the problem that samples of 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. The present invention designs a sample optimization pool-based deep reinforcement learning network that takes the current channel state information as input, the user sum rate as the optimization target, and the TD error of each sample as the priority, and uses it to output the optimal user grouping strategy , while using a deep deterministic policy gradient network to output the optimal assigned power for each user. The present invention improves the occurrence probability of valuable samples by introducing the priority of samples, can improve the learning rate of the deep reinforcement learning network, and accelerate the convergence speed. It is mainly used for resource allocation of 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 recover 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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