基于深度展开的大规模MIMO系统CSI反馈算法

CSI feedback algorithm based on deep unfolding for massive MIMO systems

针对现阶段大规模MIMO系统中基于深度学习的信道状态信息(CSI)反馈算法待训练参数过多、可解释性不强的问题,提出了2种基于深度展开的CSI反馈算法。一种是基于可学习参数的近似消息传递(AMP)算法,该算法利用深度学习中的可学习参数将AMP算法中阈值函数的阈值和Onsager校正项的参数替换,增强了阈值函数在应对非严格稀疏数据时的非线性能力。另一种是基于卷积网络的AMP算法,该算法将阈值函数模块替换为卷积残差学习模块,利用该模块去除AMP算法中每轮迭代产生的高斯随机噪声。仿真分析表明,所提算法具有比AMP算法更好的CSI反馈表现,其中基于卷积网络的AMP算法具有比基于深度学习的代表性方法更优异的CSI重构性能。

In order to solve the problem that the channel state information(CSI) feedback algorithm based on deep learning in massive MIMO systems at present had too many parameters to be trained and could not be explained well, two CSI feedback algorithms based on depth expansion were proposed. The first one was approximate message delivery(AMP)algorithm based on learnable parameters. The learnable parameters in deep learning were used to replace the threshold value of the threshold function in the AMP algorithm and the parameters of the Onsage correction term. The nonlinear ability of threshold function in dealing with non-strict sparse data was enhanced. The other was the AMP algorithm based on convolutional network, which replaced the threshold function module with the convolutional residual learning module,and used the module to remove the Gaussian random noise generated by each iteration of the AMP algorithm. Simulation results show that the proposed two algorithms have better CSI feedback performance than AMP algorithm, and the AMP algorithm based on convolutional network has better CSI reconstruction performance than the representative method based on deep learning.