부산대학교 도서관

Location awareness is vital for several applications in wireless ecosystems, including fifth generation (5G) and beyond networks defined by the 3rd Generation Partnership Project (3GPP). However, complex wireless environments such as indoor factories are characterized by harsh multipath propagation and non-line-of-sight (NLOS) conditions, which are detrimental to localization accuracy. This paper introduces the concept of blockage intelligence (BI) to provide a probabilistic description of wireless propagation conditions. Then, it discusses its integration in both conventional and soft information (SI)-based localization algorithms. Case studies are presented in the 3GPP indoor factory scenario with various gNodeBs (gNBs) deployments. Results show that BI together with SI-based localization significantly outperforms existing localization techniques. The rich information provided by BI is vital to perform accurate localization in 5G and beyond networks operating in complex wireless environments.