Cooperative Resource Allocation Based on Soft Actor-Critic With Data Augmentation in Cellular Network

This letter investigates the cooperative resource allocation of cellular networks with simultaneous wireless information and power transfer in the time-varying channel environment. The soft actor-critic (SAC) algorithm is exploited to tackle the optimization problem which aims to find a feasible resource allocation policy to maximize the data rate and system fairness while minimizing the channel switching penalty. Considering the costly agent-to-environment interactions and the restricted empirical dataset of the SAC algorithm, this letter explores the permutation equivalence of the optimization objective, and designs two data augmentation schemes for the experience replay buffer of SAC. The cumulative discount reward shows that data augmentation assisted algorithms outperform the baseline in the learning speed. The simulation results referring to the average data rate and system fairness show that the proposed schemes benefit to the training model and effectively improve the performance of algorithms.

Automated summary

This letter investigates the cooperative resource allocation of cellular networks with simultaneous wireless information and power transfer in the time-varying channel environment. The soft actor-critic (SAC) algorithm is exploited to tackle the optimization problem which aims to find a feasible resource allocation policy to maximize the data rate and system fairness while minimizing the channel switching penalty. Considering the costly agent-to-environment interactions and the restricted empirical dataset of the SAC algorithm, this letter explores the permutation equivalence of the optimization objective, and designs two data augmentation schemes for the experience replay buffer of SAC. The cumulative discount reward shows that data augmentation assisted algorithms outperform the baseline in the learning speed. The simulation results referring to the average data rate and system fairness show that the proposed schemes benefit to the training model and effectively improve the performance of algorithms.