The OCUDU dApp Platform: An Open Runtime and E3 Interface for Real-Time AI-RAN
OCUDU open runtime lets third-party signed AI-RAN dApps run inside production 5G distributed units under three timing contracts, released as BSD-3 preview.
The OCUDU dApp platform provides an open runtime and E3 interface for executing signed AI-RAN applications inside a production 3GPP NR distributed unit, where prior dApp frameworks could only observe export streams. Modules run under three typed timing contracts: GPU receive-chain residency (Class A), the scheduler's 100 microsecond deadline (Class B), or non-blocking observer (Class C). On a GB10 gNB, dApps including an out-of-tree neural equalizer ran on a live cell without fallback. Platform, SDK, and a zero-hardware quickstart are public under BSD-3-Clause-Clear as a preview of the OCUDU AI-RAN Working Group 2.
- First open runtime letting independently built software execute in the sub-10 ms 5G DU band
- Three timing contracts: GPU receive chain, 100 us scheduler deadline, non-blocking observer
- Neural equalizer variants compared over the air via lifecycle operations on a live cell
- Public BSD-3-Clause-Clear preview ahead of upstreaming into the OCUDU mainline
- Vendors
- OCUDU
- Products
- OCUDU dApp PlatformOCUDU SDK
- Organizations
- OCUDU AI-RAN Working Group 2
From the original article
1 min read at arxiv.orgMachine learning has shown its largest gains in the band below 10 ms inside a 3GPP new radio (NR) 5G distributed unit (DU): link adaptation, per-slot scheduling, channel estimation, and the receiver itself. No open platform has let independently built software run there. Prior dApp frameworks reached the band only as external observers of an export stream.
By Timothy O'Shea · arxiv.org
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