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

   https://doi.org/10.21227/rper-vc03  

   Conti, Andrea Conti; Torsoli, Gianluca Torsoli; Gómez-Vega, Carlos Antonio; Vaccari, Alessandro Vaccari; Win, Moe Z. (January 2023, IEEE DataPort)

   Location awareness is vital in next generation (xG) wireless networks to enable different use cases, including location-based services (LBSs) and efficient network management. However, achieving the service level requirements specified by the 3rd Generation Partnership Project (3GPP) is challenging. This calls for new localization algorithms as well as for 3GPP-standardized scenarios to support their systematic development and testing. In this context, the availability of public datasets with 3GPP-compliant configurations is essential to advance the evolution of xG networks. xG-Loc is the first open dataset for localization algorithms and services fully compliant with 3GPP technical reports and specifications. xG-Loc includes received localization signals, measurements, and analytics for different network and signal configurations in indoor and outdoor scenarios with center frequencies from micro-waves in frequency range 1 (FR1) to millimeter-waves in frequency range 2 (FR2). Position estimates obtained via soft information-based localization and wireless channel quality via blockage intelligence are also included in xG-Loc. The rich set of data provided by xG-Loc enables the characterization of localization algorithms and services under common 3GPP-standardized scenarios in xG networks. 

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2. 3GPP-Compliant Datasets for xG Location-Aware Networks

   https://doi.org/10.1109/OJVT.2023.3340993  

   Conti, Andrea; Torsoli, Gianluca; Gómez-Vega, Carlos A.; Vaccari, Alessandro; Mazzini, Gianluca; Win, Moe Z. (January 2024, IEEE Open Journal of Vehicular Technology)
3. Sidelink-Enabled Cooperative Localization for xG Non-Terrestrial Networks

   https://doi.org/10.1109/MILCOM61039.2024.10773683  

   Morrison, James C; Schatz, Nathan; Kim, Seungnyun; Kwon, Girim; Tedeschini, Bernardo Camajori; Weerackody, Vijitha; Conti, Andrea; Win, Moe Z (October 2024, IEEE)
4. Wixor: Dynamic TDD Policy Adaptation for 5G/xG Networks

   https://doi.org/10.1145/3696395  

   Hassan, Ahmad; Aggarwal, Shivang; Ibrahim, Mohamed; Sharma, Puneet; Qian, Feng (December 2024, Proceedings of the ACM on Networking)

   In the era of 5G and beyond, dynamic Time Division Duplex (TDD) has become essential for supporting applications that demand high bandwidth and low latency. Emerging uplink-intensive use cases such as real-time video analytics, autonomous vehicles and augmented reality further complicate the balance between uplink and downlink resources. Despite their potential, TDD policies employed by current 5G networks remain underexplored. Our investigation reveals that existing TDD policies are static and predominantly downlink-focused, failing to adapt to fluctuating network demands. We introduce Wixor, a robust dynamic TDD policy adaptation system tailored for 5G and next-generation (xG) networks. It proactively adjusts the allocation of TDD resources between uplink and downlink, addressing various practical challenges. Prototyped on a programmable testbed, Wixor demonstrates substantial performance improvements across diverse applications, achieving up to 96.5% enhancement in Quality of Experience (QoE) compared to existing baselines. 

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5. Wixor: Dynamic TDD Policy Adaptation for 5G/xG Networks

   Hassan, A; Aggarwal, S; Ibrahim, M; Sharma, P; Qian, F (December 2024, CoNEXT 2024)