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This paper introduces ASCENT (context Aware Spectrum Coexistence Design and Implementation) toolset, an advanced context-aware terrestrial satellite spectrum sharing toolset designed for researchers, policymakers, and regulators. It serves two essential purposes (a) evaluating the potential for harmful interference to primary users in satellite bands and (b) facilitating the analysis, design, and implementation of diverse regulatory policies on spectrum usage and sharing. Notably, ASCENT implements a closed-loop feedback system that allows dynamic adaptation of policies according to a wide range of contextual factors (e.g., weather, buildings, summer/winter foliage, etc.) and feedback on the impact of these policies through realistic simulation. Specifically, ASCENT comprises the following components (i) interference evaluation tool for evaluating interference at the incumbents in a spectrum-sharing environment while taking the underlying contexts, (ii) dynamic spectrum access (DSA) framework for providing context-aware instructions to adapt networking parameters and control secondary terrestrial network's access to the shared spectrum band according to context aware prioritization, (iii) Context broker to acquire essential and relevant contexts from external context information providers; and (iv) DSA Database to store dynamic and static contexts and the regulator's policy information. The closed-loop feedback system of ASCENT is implemented by integrating these components in a modular software architecture. A case study of sharing the lower 12 GHz Ku band (12.2-12.7 GHz) with the 5G terrestrial cellular network is considered, and the usability of ASCENT is demonstrated by dynamically changing exclusion zone's radius in different weather conditions. 

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.