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Reconfigurable Intelligent Surface (RIS) is considered as an energy-efficientsolution for future wireless communication networks due to its fast andlow-cost configuration. In this letter, we consider the estimation of cascadedchannels in a double-RIS aided massive multiple-input multiple-output system,which is a critical challenge due to the large number of antennas equipped atthe base station and passive RIS elements. To tackle this challenge, we proposea skip-connection attention (SC-attention) network that utilizes self-attentionlayers and skip-connection structure to improve the channel estimationperformance from the noisy pilot-based observations. Simulation results comparethe proposed SC-attention network with other benchmark methods and show thatSC-attention network can effectively improve the accuracy performance onnormalized mean square error (NMSE) for cascaded links in a double-RIS aidedsystem.

Deep Learning-Based Channel Estimation for Double-RIS Aided Massive MIMO System