Functionalities and Capabilities

Experimental Capabilities

The software architecture of the CCI xG Testbed enables a wide range of experimental capabilities:

  • End-to-End O-RAN Experimentation: The testbed supports end-to-end O-RAN experimentation using SDRs and open-source components, including AIMLFW, Non-RT RIC, Near-RT RIC, RAN (4G and 5G), and UE.

  • CBRS Ecosystem Experimentation: The testbed provides an end-to-end CBRS ecosystem for experimentation, including SDR-based CBSDs and ESC nodes, OpenSAS, and CBRS PAL.

  • AI/ML-based Network Optimization: The testbed enables AI/ML-based network optimization through its native AI/ML framework, supporting research in areas such as resource allocation, energy efficiency, and QoS/QoE optimization.

  • Spectrum Sharing Research: The software architecture supports spectrum sharing research, including homogeneous and heterogeneous dynamic spectrum sharing, priority protection, interference management, and coexistence in CBRS and other multi-tier spectrum sharing ecosystems.

Detailed Capabilities

The CCI xG Testbed offers the following detailed functionalities and capabilities for wireless network experimentation:

End-to-End O-RAN Ecosystem

  • Non-RT RIC (Non-Real-Time RAN Intelligent Controller)

  • Near-RT RIC (Near-Real-Time RAN Intelligent Controller)

  • AI/ML integration for network optimization and management

  • Radio access network components (O-RU, O-DU, O-CU)

  • A1 and A2 interfaces for communication between RIC components

End-to-End CBRS Ecosystem

  • Complete CBRS (Citizens Broadband Radio Service) experimentation platform

  • Spectrum Access System (SAS) integration

  • Priority Access License (PAL) and General Authorized Access (GAA) tiers

  • Environmental Sensing Capability (ESC) simulation

OpenSAS CBSD SDR-Based Prototype

  • Open-source Spectrum Access System (OpenSAS) implementation

  • CBSD (Citizens Broadband Radio Service Device) based on Software-Defined Radio

  • Flexible and programmable radio access for CBRS experimentation

  • Customizable SAS-CBSD protocol implementation

SDR-Based Massive MIMO

  • Software-Defined Radio implementation of Massive MIMO systems

  • Scalable antenna arrays for beamforming experiments

  • Real-time signal processing capabilities

  • Configurable for various frequency bands and channel models

AI-ML Experimentation

  • Machine learning frameworks for wireless network optimization

  • Real-time data collection and analysis

  • Reinforcement learning for dynamic spectrum access

  • Neural networks for signal classification and prediction

  • Edge AI capabilities for distributed intelligence