Massive MIMO Indoor Localization with 64-Antenna Uniform Linear Array
| Autor: | Sofie Pollin, Sibren De Bast, Bin Liu, Andrea P. Guevara, Qing Wang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Beamforming
Computer science MIMO Testbed 020206 networking & telecommunications 02 engineering and technology Communications system Bayesian inference Field (computer science) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Angular resolution Antenna (radio) Algorithm Computer Science::Information Theory |
| Zdroj: | VTC Spring |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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