存在CSI估计错误的增强型ELM叠加CSI反馈方法

Enhanced ELM-based Superimposed CSI Feedback Method with CSI Estimation Errors

在大规模多输入多输出(Massive-Multiple Input and Multiple-Output,mMIMO)系统中,叠加信道状态信息(Channel State Information,CSI)反馈可避免上行带宽资源占用,但叠加干扰会造成接收机计算复杂度高、反馈精度低等问题,且均未考虑存在CSI估计错误的实际应用场景。为此,针对存在CSI估计错误场景下的叠加CSI反馈,在改进极限学习机(Extreme Learning Machine,ELM)的基础上,提出基于增强型ELM的叠加CSI反馈方法。首先,基站对接收信号进行预均衡处理,初步消除上行信道干扰;然后对传统叠加CSI反馈进行迭代展开,构建增强型ELM网络,通过规范化各个ELM网络的隐藏层输出来增强网络学习数据分布的能力,从而改善恢复下行CSI和上行用户数据序列(Uplink User Data Sequence,UL-US)的精确性。仿真实验表明,与经典和时新的叠加CSI反馈方法相比,所提方法能够获得相似或更好的下行CSI和上行用户数据的恢复精确性;同时,针对不同的参数影响,性能改善具有鲁棒性。

In massive multiple-input multiple-output(mMIMO)systems,the superimposed channel state information(CSI)feedback avoids the occupation of uplink bandwidth resources,while causing high calculation complexity and low feedback accuracy due to the superimposed interference,yet the actual application scenarios with CSI estimation errors are not considered.For these reasons,aiming at the superimposed CSI feedback in the scenario of CSI estimation errors and based on improving the extreme learning machine(ELM),this paper proposes enhanced ELM-based superimposed CSI feedback.First,the base station performs pre-equalization processing on the received signal to initially eliminate uplink channel interference.Then,the traditional super imposed CSI feedback is iteratively unfolded by constructing an enhanced ELM network.This operation enhances the ability of the network to learn data distribution by standardizing the hidden layer output of each ELM network,thereby improving the accuracy of recoveries for downlink CSI and uplink user data sequences(UL-US).Experimental simulations show that compared with the classic and novel superimposed CSI feedback methods,the proposed method can obtain similar or better recovery accuracies for the downlink CSI and UL-US,while retaining the improvement robustness against the influence of different parameters.