Accurate radio maps will be very much needed to provide environmental awareness and effectively manage future wireless networks. Most of the research so far has focused on developing power mapping algorithms for single and omnidirectional antenna systems. In this letter, we investigate the construction of crowdsourcing-based radio maps for 5G cellular systems with massive directional antenna arrays (spatial multiplexing), proposing an original technique based on semi-parametric Gaussian regression. The proposed method is model-free and provides highly accurate estimates of the radio maps, outperforming fully parametric and non-parametric solutions.
Model-free radio map estimation in massive MIMO systems via semi-parametric Gaussian regression / Dal Fabbro, Nicolo; Rossi, Michele; Pillonetto, Gianluigi; Schenato, Luca; Piro, Giuseppe. - In: IEEE WIRELESS COMMUNICATIONS LETTERS. - ISSN 2162-2337. - STAMPA. - 11:3(2022). [10.1109/LWC.2021.3132458]
Model-free radio map estimation in massive MIMO systems via semi-parametric Gaussian regression
Giuseppe Piro
2022-01-01
Abstract
Accurate radio maps will be very much needed to provide environmental awareness and effectively manage future wireless networks. Most of the research so far has focused on developing power mapping algorithms for single and omnidirectional antenna systems. In this letter, we investigate the construction of crowdsourcing-based radio maps for 5G cellular systems with massive directional antenna arrays (spatial multiplexing), proposing an original technique based on semi-parametric Gaussian regression. The proposed method is model-free and provides highly accurate estimates of the radio maps, outperforming fully parametric and non-parametric solutions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
