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In frequency-division duplexing (FDD) massive multiple-input multiple-output(MIMO), deep learning (DL)-based superimposed channel state information (CSI)feedback has presented promising performance. However, it is still facing manychallenges, such as the high complexity of parameter tuning, large number oftraining parameters, and long training time, etc. To overcome these challenges,an extreme learning machine (ELM)-based superimposed CSI feedback is proposedin this paper, in which the downlink CSI is spread and then superimposed onuplink user data sequence (UL-US) to feed back to base station (BS). At the BS,an ELM-based network is constructed to recover both downlink CSI and UL-US. Inthe constructed ELM-based network, we employ the simplified versions ofELM-based subnets to replace the subnets of DL-based superimposed feedback,yielding less training parameters. Besides, the input weights and hidden biasesof each ELM-based subnet are loaded from the same matrix by using its full orpartial entries, which significantly reduces the memory requirement. Withsimilar or better recovery performances of downlink CSI and UL-US, the proposedELM-based method has less training parameters, storage space, offline trainingand online running time than those of DL-based superimposed CSI feedback.

ELM-based Superimposed CSI Feedback for FDD Massive MIMO System