AI for CSI feedback: encoder-decoder neural networks for channel estimation

CSI Feedback Bottleneck

Channel State Information feedback is critical for MIMO precoding — the gNB needs accurate knowledge of the downlink channel to each UE to compute optimal beamforming weights. In traditional NR, the UE quantizes the channel using a codebook, reporting PMI, RI, and CQI indices. As MIMO scales to 32 or 64 antenna ports, the codebook grows exponentially and the feedback becomes increasingly lossy. Type II codebook in Rel-15 improved accuracy but requires substantial uplink overhead. This tension between feedback accuracy and overhead is the fundamental bottleneck AI-based CSI aims to solve.