xG-Loc: 3GPP-compliant datasets for xG location-aware networks

The following instructions summarize the xG-Loc [1] structure and characteristics, which are described in detail in the open access associate publication [2].

xG-Loc is structured into 28 compressed directories, representing 28 possible configurations of 3GPP-standardized scenarios, bandwidth B, and central frequencies f_c both in FR1 at microWaves, in FR2 at mmWaves, and in the upper mid-band (FR3).

Specifically, the scenarios considered are:

• 3GPP 38.901 Indoor factory dense-high (InF-DH)
• 3GPP 38.901 Indoor factory sparse-high (InF-SH)
• 3GPP 38.901 Indoor open office (IOO)
• 3GPP 38.901 Urban microcell (UMi)
• 3GPP 38.901 Urban macrocell (UMa)
• 3GPP 38.901 Rural macro (RMa)

The directory naming convention is scenario_B_ fc. For example, the directory containing the data for the UMi scenario with 200MHz bandwidth and a central frequency of 30GHz is named umi_200_30.zip.

The configurations available in xG-Loc are reported in the following table:

Each configuration directory includes 100 instantiations (each of which referred to as RUN) of the 3GPP-standardized scenario. At each RUN, the random parameters of the scenario, including the wireless channel characteristics and non-line-of-sight (NLOS) conditions are re-computed. For each RUN, 10 user equipments (UEs) are deployed with random positions and orientations. The structure of the directories is as follows:

A simple example showing how data can be read from a configuration directory of xG-Loc is reported in the Matlab file read_files.m.

In more detail, for each UE, two compressed folders are provided, which respectively include:

• received positioning reference signals (PRSs) for each wireless link between the UE and the base stations;
• received sounding reference signals (SRSs) for each wireless link between the UE and the base stations;

The transmitted PRS and SRS are also provided.

In addition, some synthetic data and analytics are reported in a json file, including:

• distance estimates in downlink based on the PRS transmission for each wireless link between the UEs and the base stations;
• distance estimates in uplink based on the SRS transmission for each wireless link between the UEs and the base stations;
• angle-of-departure (AOD) estimates based on the PRS transmission with beam-sweeping for the indoor scenarios in FR2;
• estimates of the UEs position via soft information (SI)-based localization [3],[4],[5];
• estimates of the wireless channel quality via blockage intelligence (BI) [6] for each link between the UEs and the base stations; and
• other additional information useful to evaluate the performance of location-aware networks.

Note

If using xG-Loc, include explicit citation to both xG-Loc IEEE DataPort [1] and its associated publication [2]. If using specific features of xG-Loc, such as estimates of the UE position via SI-based localization [3],[4],[5] or estimates of the wireless channel quality via BI [6], also include explicit citation to the related publications.

We suggest using a sentence as follows:

"To evaluate the performance of the algorithm, we use xG-Loc, a 3GPP-compliant dataset for next generation (xG) location-aware networks [1],[2]."

Acknowledgment

The fundamental research connected to xG-Loc was supported, in part, by the Office of Naval Research under Grant N62909-22-1-2009, in part by the National Science Foundation under Grant CNS-2148251, and in part by the Federal Agencies and Industry Partners in the RINGS program. The xG-Loc November 2024 update was supported, in part, by the European Union through the NextGenerationEU Italian NRRP under Grant PE00000001 Program RESTART Project TAP-IN.

References

[1] A. Conti, G. Torsoli, C. A. Gómez-Vega, A. Vaccari, and M. Z. Win, “xG-Loc: 3GPP-compliant datasets for xG location-aware networks,” IEEE Dataport, 2023. [Online]. Available: https://dx.doi.org/10.21227/rper-vc03

[2] A. Conti, G. Torsoli, C. A. Gómez-Vega, A. Vaccari, G. Mazzini, and M. Z. Win, “3GPP-compliant datasets for xG location-aware networks,” IEEE Open J. Veh. Tech, vol. 5, pp. 473-484, Apr. 2024.

[3] A. Conti, S. Mazuelas, S. Bartoletti, W. C. Lindsey, and M. Z. Win, “Soft information for localization-of-things,” Proc. IEEE, vol. 107, no. 11, pp. 2240-2264, Nov. 2019.

[4] A. Conti, F. Morselli, Z. Liu, S. Bartoletti, S. Mazuelas, W. C. Lindsey, and M. Z. Win, Location awareness in beyond 5G networks, IEEE Commun. Mag., vol. 59, no. 11, pp. 2227, Nov. 2021

[5] F. Morselli, S. M. Razavi, M. Z. Win, and A. Conti, “Soft information based localization for 5G networks and beyond,” IEEE Trans. Wireless Commun., vol. 22, , vol. 22, no. 12, pp. 9923-9938, Dec. 2023.

[6] G. Torsoli, M. Z. Win, and A. Conti, “Blockage intelligence in complex environments for beyond 5G localization,” IEEE J. Sel. Areas Commun., vol. 41, no. 6, pp. 1688-1701, Jun. 2023.

For more information, visit wcln.unife.it