Standardized Machine-Readable Point-Data Format for Consolidating Wireless Propagation Across Environments, Frequencies, and Institutions

The necessity of new spectrum for 6G has intensified global interest in radio propagation measurements across emerging frequency bands, use cases, and antenna types. These measurements are vital for understanding the fundamentals of radio channel properties in diverse environments, and involve time-consuming, labor-intensive, and expensive campaigns. A major challenge for the effective utilization of the data generated from propagation measurement campaigns has been the lack of a standardized format for reporting and archiving results. Although organizations such as NIST, NGA, and 3GPP have made commendable efforts toward data pooling, a unified machine-readable data format for consolidating measurements across different institutions and frequencies remains a critical missing piece in advancing global standardization efforts and maximizing the utility of collected data. This paper introduces a new, standardized point-data format for wireless propagation measurements and demonstrates how multiple institutions may merge disparate measurement campaigns into a common format for global access. This data format, when used alongside an environmental map and a measurement summary metadata table, enables the seamless integration of radio propagation data from disparate sources by using a structured representation of key propagation parameters. Here, we show the efficacy of the proposed point-data format standard using data gathered from two independent sub-THz urban microcell (UMi) campaigns: 142 GHz measurements at New York University (NYU) and 145 GHz measurements at the University of Southern California (USC). A joint path loss analysis using the close-in path loss model with a 1 m reference distance yields a refined estimate of the path loss exponent (PLE) employing the proposed standard to pool measurements from the two different organizations. Other key statistics such as RMS delay spread and angular spread are determined using datasets from the two different institutions cast in the standardized format. Adopting this simple, unified format will accelerate comprehensive channel model development, build multi-institutional datasets, and feed AI/ML applications with reliable training data in a common format from many sources.