Deep-Learning-Based High-Precision Localization With Massive MIMO

High-precision localization and machine learning (ML) are envisioned to be key technologies in future wireless systems. This paper presents an ML pipeline to solve localization tasks. It consists of multiple parallel processing chains, each trained using a different fingerprint to estimate the position of the user equipment. In this way, ensemble learning can be utilized to fuse all chains to improve localization performance. Nevertheless, a common problem of ML-based techniques is that network training and fine-tuning can be challenging due to the increase in network sizes when applied to (massive) multiple-input multiple-output (MIMO) systems. To address this issue, we utilize a subarray-based approach. We divide the large antenna array into several subarrays, feeding the fingerprints of the subarrays into the pipeline. In our case, such an approach eases the training process while maintaining or even enhancing the performance. We also use the Nyquist sampling theorem to gain insight on how to appropriately sample and average training data. Finally, an indoor measurement campaign is conducted at 3.7GHz using the Lund University massive MIMO testbed to evaluate the approaches. Localization accuracy at a centimeter level has been reached in this particular measurement campaign.