A Hierarchical Multimodal Feature Fusion Physical Layer Key Generation Scheme for Non-Reciprocal Channels

Physical layer key generation (PLKG) enables wireless devices to establish shared secret keys by exploiting channel reciprocity. However, most existing PLKG methods rely on single-modality features, which provide limited shared randomness and are sensitive to channel non-reciprocity in frequency division duplexing (FDD) systems, resulting in high key disagreement rates (KDR). To address this challenge, this work proposes a hierarchical multimodal feature fusion (HiMFF) scheme that jointly leverages time, frequency, and spatial modality features to capture complementary channel characteristics, and thereby provides rich and consistent shared randomness for reliable key generation under the non-reciprocal conditions of FDD systems. First, a preprocessing module is employed to transform raw multimodal channel observations into structured feature representations. Then, a multimodal fusion module is developed to align and integrate multimodal features across various scales effectively. Finally, a large language model is adapted through fine-tuning to produce reciprocal channel features, ensuring reliable key generation. Simulation results show that the proposed scheme outperforms current baselines, reducing KDR by up to 9.2%.