针对频分双工(FDD,frequency-division duplexing)模式下可重构智能反射面(RIS,reconfigurable intelligent surface)辅助的多用户大规模多输入多输出(MIMO,multiple-input multiple-output)系统信道反馈开销大的问题,提出了一种基于流形学习的信道状态信息(CSI,channel state information)反馈框架.该框架首先通过简化CSI反馈过程实现初步的反馈开销降低,然后结合流形学习思想训练两组字典,从而实现增量CSI的降维和重构,最后在基站端恢复原始信道.仿真结果表明,在多用户和有限散射环境下,所提的CSI反馈方案与现有的方法相比具有更低的开销和复杂度,而且重构质量得到显著提高.
Research on CSI feedback of RIS-assisted massive MIMO system based on manifold learning
To solve the problem of high feedback overhead in a multi-user massive multiple-input multiple-output(MIMO)system assisted by a reconfigurable intelligent surface(RIS)in frequency-division duplexing(FDD)mode,a channel state information(CSI)feedback framework based on manifold learning was proposed.Firstly,the framework achieved initial feedback overhead reduction by simplifying the CSI feedback process.Then,the framework combined the manifold learning to train two set of dictionaries to achieve dimension reduction and reconstruction of incremental CSI.Finally,the original channel was restored at the base station.The simulation results show that the CSI feedback scheme proposed in this paper has lower overhead and complexity than the existing methods in the multi-user and limited scattering environment,and the re-construction quality is significantly improved.
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