Low Dimensional Fingerprint Positioning for Massive MIMO Communication Systems

With the growth of positioning demand, fingerprintbased positioning, which utilizes multi-path information to improve positioning accuracy, has attracted academic and industrial attentions. Current position fingerprints often incur significant storage overhead and time complexity for machine learning due to their large dimension. This paper investigates positioning in massive multiple-input multiple-output (MIMO) communication systems using a low dimensional fingerprints called the angledelay power coordinates matrix (ADECM). We first use a dimensionality reduction algorithm for the widely used position fingerprint angle-delay channel power matrix (ADCPM) in existing works, and define the reduced matrix as the ADECM. Subsequently, recognizing the excellent performance of generative adversarial network in regression tasks, we propose the Feature Separation Generative Adversarial Network (FSGAN). This innovative network is specifically designed to accurately estimate the position of user terminals. Simulation results show that the proposed positioning method outperforms existing methods.