Deep Learning assisted Port-Cycling based Channel Sounding for Precoder Estimation in Massive MIMO Arrays

Future wireless systems are expected to employ a substantially larger number of transmit ports for channel state information (CSI) estimation compared to current specifications. Although scaling ports improves spectral efficiency, it also increases the resource overhead to transmit reference signals across the time-frequency grid, ultimately reducing achievable data throughput. In this work, we propose an deep learning (DL)-based CSI reconstruction framework that serves as an enabler for reliable CSI acquisition in future 6G systems. The proposed solution involves designing a port-cycling mechanism that sequentially sounds different portions of CSI ports across time, thereby lowering the overhead while preserving channel observability. The proposed CSI Adaptive Network (CsiAdaNet) model exploits the resulting sparse measurements and captures both spatial and temporal correlations to accurately reconstruct the full-port CSI. The simulation results show that our method achieves overhead reduction while maintaining high CSI reconstruction accuracy.