Abstract: 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 traditionalsuper 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.

Key words: Channel state information, Estimation error, Extreme learning machine, Massive multiple-input multiple-output, Superimposed CSI feedback