Deep Learning Super-Resolution-Based Channel Completion for Massive MISO Systems

Keke Zu, Yuhan He, Hongyang Chen, Yu Zheng, Martin Haardt

Abstract: With the deployment of large-scale antenna arrays, the already limited time-frequency resources are becoming increasingly scarce. In this study, we propose a novel Laplacian Pyramid Channel Completion Network (LPCCNet) designed for channel completion, thereby reducing the demand for time-frequency resources in massive MIMO systems. Compared with existing network models, the proposed LPCCNet, by employing a progressive upsampling architecture, effectively mitigates aliasing effects, suppresses error propagation, and achieves a substantial reduction in computational complexity. The simulation results show that LPCCNet achieves a superior channel completion quality compared to existing methods, particularly in rapidly time-varying scenarios.

External IDs:dblp:journals/spl/ZuHCZH25