FastAdapter: Codeword-Conditioned Adapter Generation for Cross-Encoder Alignment in CSI Feedback

Independently trained CSI feedback encoders can produce incompatible codeword representations, degrading reconstruction by a fixed base-station decoder. Training an alignment model for each new encoder incurs repeated optimization. This paper proposes FastAdapter, which generates a complete adaptation module from unlabeled codewords received at the base station while preserving both endpoints. Offline, codeword refinement and reconstruction constraints prepare decoder-compatible target Adapters, and hidden-unit realignment gives functionally equivalent parameters consistent coordinates. A Gram–Raw condition encoder combines codeword coordinates, magnitudes, and inter-sample geometry to construct position-specific context. Contrastive pretraining associates this context with target parameters, and conditional diffusion generates reusable Adapters for unseen encoders. Evaluations on COST2100, WAIR-D, and DeepMIMO cover unseen training configurations and unseen architectures under a shared CSI distribution and feedback interface. Against the strongest generative baseline in each setting, FastAdapter lowers NMSE by $0.34$–$2.07$ dB for unseen training configurations and $0.17$–$1.17$ dB for unseen architectures. The generated Adapter reconstructs subsequent feedback without paired CSI supervision or gradient updates during adaptation, transferring offline adaptation experience across encoder models.