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The open radio access network (O-RAN) represents a paradigm shift in RAN architecture, integrating intelligence into communication networks via xApps -- control applications for managing RAN resources. This integration facilitates the adoption of AI for network optimization and resource management. However, there is a notable gap in practical network performance analyzers capable of assessing the functionality and efficiency of xApps in near real-time within operational networks. Addressing this gap, this article introduces a comprehensive network performance analyzer, tailored for the near-real time RAN intelligent controller. We present the design, development, and application scenarios for this testing framework, including its components, software, and tools, providing an end-to-end solution for evaluating the performance of xApps in O-RAN environments.more » « lessFree, publicly-accessible full text available September 27, 2025
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Openness and intelligence are two enabling features to be introduced in next generation wireless networks, for example, Beyond 5G and 6G, to support service heterogeneity, open hardware, optimal resource utilization, and on-demand service deployment. The open radio access network (O-RAN) is a promising RAN architecture to achieve both openness and intelligence through virtualized network elements and well-defined interfaces. While deploying artificial intelligence (AI) models is becoming easier in O-RAN, one significant challenge that has been long neglected is the comprehensive testing of their performance in realistic environments. This article presents a general automated, distributed and AI-enabled testing framework to test AI models deployed in O-RAN in terms of their decision-making performance, vulnerability and security. This framework adopts a master-actor architecture to manage a number of end devices for distributed testing. More importantly, it leverages AI to automatically and intelligently explore the decision space of AI models in O-RAN. Both software simulation testing and software-defined radio hardware testing are supported, enabling rapid proof of concept research and experimental research on wireless research platforms.more » « less
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Abstract Nitrogen (N) availability has been considered as a critical factor for the cycling and storage of soil organic carbon (SOC), but effects of N enrichment on the SOC pool appear highly variable. Given the complex nature of the SOC pool, recent frameworks suggest that separating this pool into different functional components, for example, particulate organic carbon (POC) and mineral‐associated organic carbon (MAOC), is of great importance for understanding and predicting SOC dynamics. Importantly, little is known about how these N‐induced changes in SOC components (e.g., changes in the ratios among these fractions) would affect the functionality of the SOC pool, given the differences in nutrient density, resistance to disturbance, and turnover time between POC and MAOC pool. Here, we conducted a global meta‐analysis of 803 paired observations from 98 published studies to assess the effect of N addition on these SOC components, and the ratios among these fractions. We found that N addition, on average, significantly increased POC and MAOC pools by 16.4% and 3.7%, respectively. In contrast, both the ratios of MAOC to SOC and MAOC to POC were remarkably decreased by N enrichment (4.1% and 10.1%, respectively). Increases in the POC pool were positively correlated with changes in aboveground plant biomass and with hydrolytic enzymes. However, the positive responses of MAOC to N enrichment were correlated with increases in microbial biomass. Our results suggest that although reactive N deposition could facilitate soil C sequestration to some extent, it might decrease the nutrient density, turnover time, and resistance to disturbance of the SOC pool. Our study provides mechanistic insights into the effects of N enrichment on the SOC pool and its functionality at global scale, which is pivotal for understanding soil C dynamics especially in future scenarios with more frequent and severe perturbations.