Node Activation for SI-based xG Localization: 3GPP Case Studies using xG-Loc Dataset

Location awareness is crucial for emerging services and enhanced resource orchestration in next generation (xG) wireless networks. To provide efficient high-accuracy localization, xG networks require both algorithms for position inference and strategies for resource optimization. While the exploitation of soft information (SI) provides significant gains in localization performance, node activation strategies benefit resource utilization by selecting an adequate set of nodes to perform measurements. This paper develops a data-driven node activation strategy for efficient SI-based localization in xG networks. First, we formulate the node activation problem considering an SI-based position estimator. Then, we propose a data-driven node activation strategy for determining an adequate set of active nodes given only a position estimate. To validate the proposed strategy, we employ xG-Loc, a dataset for location-aware xG networks fully compliant with 3rd Generation Partnership Project (3GPP) specifications. Case studies in 3GPP scenarios show the benefits of the proposed node activation strategy.