OAIC-T is an open-source AI cellular testing platform which supports automated, distributed, and AI-enhanced testing of xApps in O-RAN.
While AI models are enablers to achieve intelligent next-G wireles networks, comprehensive testing of their performance is cumbersome and in many cases non-existent. This is mainly due to the inability of the current theory to explain or prevent failures in the AI models which are mainly trained in data-driven manners. Hence, it is necessary to have a framework and appropriate environment for testing AI models in their capacity of cellular RAN controllers. From the ongoing research and development and expected deployment of O-RAN components in Next-G networks, there is a urgent need for methods, platforms, and tools that facilitate testing various AI models in the radio network in a production like environment.
- Software-defined and modular to enable customization;
- Invasive/non-invasive testing during O-RAN operation in isolated or production environment to capture data in relevant operating conditions;
- Open test interfaces to enable the development of new test methods and processes;
- Test configuration files that enable specifying and reproducing a test;
- Support for automated and AI-enhanced testing to assess the operation of AI-enabled cellular radio network controllers under a myriad of channel and contextual conditions (large search spaces)
- Support for multitasking and distributed testing to enable a multi-user testing environment (e.g., producing different traffic scenarios)
An OAIC-T test involves automated setup of the testing environment, automated test execution, and automated generation of testing performance report. The OAIC-T framework consists of three major components: the OAIC-T server which sets up the testing environment as described in test configuration files and orchestrates the test execution as defined in test cases, the OAIC-T actor which executes test steps as instructed by the OAIC server, and the test repository which stores various test assets (e.g., test files, data files, log files, test results, etc.).
- Phase I (Expected Date: Jan. 1, 2023) with implementations of core OAIC-T framework.
- Phase II (Expected Date: June. 15, 2023) with implementations of AI testing methods, including Fuzzing, AI-Fuzzing, and adversarial learning.
- Phase III (Expected Date: Dec. 15, 2023) with implementations of multitasking.
- Phase IV (Expected Date: June. 15, 2024) with system integration and testing.
Please refer to the OAIC-T document for details.