Localization is crucial for nowadays’ communication systems, especially for beamforming techniques in massive MIMO systems. Large-scale MIMO systems have exhibited their advantages in communications. In the meantime, they also have the potential to provide accurate localization with their high angular resolution. In this paper, we study indoor localization performance of a Massive MIMO system with a 64-antenna Uniform Linear Array (ULA). Based on the sparse reconstruction method, we propose a Mixed field Sparse Bayesian Learning (MSBL) algorithm to localize devices for both near-field and far-field scenarios. Using the measurement results from our massive MIMO testbed, we show that our proposed MSBL algorithm can improve the localization accuracy by 49% with only a few snapshots. The performance of our algorithm is also robust to low Signal-to-Noise Ratio (SNR) conditions.
Massive MIMO Indoor Localization with 64-Antenna Uniform Linear Array
2020-05-01
185080 byte
Conference paper
Electronic Resource
English
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