DBLG: An Innovative Deep-Broad Learning and GAN Framework for CSI Fingerprint Database Refinement

With the rapid development of Integrated Sensing and Communication in 6G, Channel State Information (CSI)-based fingerprint indoor localization technology is becoming crucial. However, during the offline phase, the fingerprint database update process using crowdsourcing techniques is prone to noise interference and incomplete coverage, and fitting Gaussian regression models requires extensive computational resources. In this paper, we propose a fine-grained CSI fingerprint database update method based on Deep-Broad Learning system (DeepBLS) and Generative Adversarial Networks (GAN), termed DBLG. Firstly, we employ the combined DeepBLS network for the initial construction of the global CSI fingerprint database. Subsequently, we utilize GAN to extract features from the raw data, predict and update the global CSI fingerprint database, and construct a high-precision fingerprint database using confidence coefficients. Finally, We implement the proposed algorithm in two real-world environments and conduct extensive experiments to verify its performance. Compared to several existing methods, our approach shows superior performance in updating the CSI finger-print database, achieving a 46.78 % improvement in localization accuracy.