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Deep learning has revolutionized the design of the channel state information(CSI) feedback module in wireless communications. However, designing theoptimal neural network (NN) architecture for CSI feedback can be a laboriousand time-consuming process. Manual design can be prohibitively expensive forcustomizing NNs to different scenarios. This paper proposes using neuralarchitecture search (NAS) to automate the generation of scenario-customized CSIfeedback NN architectures, thereby maximizing the potential of deep learning inexclusive environments. By employing automated machine learning andgradient-descent-based NAS, an efficient and cost-effective architecture designprocess is achieved. The proposed approach leverages implicit scene knowledge,integrating it into the scenario customization process in a data-driven manner,and fully exploits the potential of deep learning for each specific scenario.To address the issue of excessive search, early stopping and elastic selectionmechanisms are employed, enhancing the efficiency of the proposed scheme. Theexperimental results demonstrate that the automatically generated architecture,known as Auto-CsiNet, outperforms manually-designed models in bothreconstruction performance (achieving approximately a 14% improvement) andcomplexity (reducing it by approximately 50%). Furthermore, the paper analyzesthe impact of the scenario on the NN architecture and its capacity.

Auto-CsiNet: Scenario-customized Automatic Neural Network Architecture Generation for Massive MIMO CSI Feedback