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StatusOngoing
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Status date2025-06-10
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Activity Code3F.047
3F.047 aims to enhance radio resource management in densely populated areas through machine learning applied to open-source 5G-Non-Terrestrial Network (NTN) systems. The objective is to produce a software solution for high performance and efficient resource allocation, for resources such as channels, modulations, and beams, within low size, weight, and power (SWaP) base stations. This project will produce technology that enables robust, secure, and high-capacity connections in previously unreachable locations and will be demonstrated on a prototype hardware system using commercial-off-the-shelf components. The project sets the stage for high-performance regenerative gNodeB’s as part of future NTN networks.
The key challenges of the project involve building an inference subsystem that can handle large volumes of IQ data with very low latency in a very resource constrained package.
The key benefit of the technology is to create more adaptive, dynamic, and interference resistant Radio Access Network’s (RAN), which would be better able to serve access in increasing client density.
The product consists of a software plug-in to srsRAN that runs on the same hardware as the Distributed Unit (DU) of a RAN. The product infers optimal assignments dynamically as the gNodeB operates, with latencies that are faster than a 5G frame. The system does not impact RAN operations and results in very low packet loss due.
The system gNodeB consists of a Commercial Off-The-Shelf (COTS) Universal Software Radio Peripheral (USRP) on a low size, weight and power processor (ARM or x86-based) which runs all software to run a RAN, including a modified version of srsRAN, an open5GS 5G core, and the Qoherent Radio Inference Engine.
The project is split into 6 milestones:
- Requirements Analysis, Alignment and Design
- RIARAN-NTN GnodeB & Network Development
- ML Workflow & Model Development
- Integration and Lab Testing
- Field Testing and Validation
- Project reporting and closure
Qoherent has done preliminary design work, hardware target benchmarking and performed field testing to inform testbed requirements. Development activities within the project such as development of the scheduler and construction of the project testbed is underway.
