Beyond 5G Localization at mmWaves in 3GPP Urban Scenarios with Blockage Intelligence

Accurate positional information is crucial for numerous emerging applications in fifth generation (5G) and beyond wireless ecosystems. However, the localization requirements defined by the 3rd Generation Partnership Project (3GPP) are particularly challenging to achieve, especially in complex environments such as urban scenarios, due to non-line-of-sight conditions, outdoor-to-indoor penetration loss, and multipath propagation. Such effects are detrimental to localization accuracy, especially at mmWaves. This paper introduces the concept of blockage intelligence (BI) to provide a probabilistic representation of wireless propagation conditions. Such representation is then exploited in soft information (SI)-based localization to overcome the limitations of conventional localization approaches. Localization case studies are presented according to the 3GPP-standardized urban microcell (UMi) scenario at mmWaves with fully 3GPP-compliant simulations. Results show that BI together with SI-based localization is able to provide a significant performance gain with respect to existing techniques in 5G and beyond wireless networks.