Distributed applications (dApps) bring AI to the microsecond-to-millisecond band beside the 5G distributed unit (DU), but every public dApp framework realizes them the same way: an external process that receives an indication and returns a control message. That boundary is right for sensing and advisory workloads. It c...
Timothy J. O'Shea, M. Pennybacker, A. Kharchenko· 0 citations
This article integrates two-sided model processing into the 5G New Radio protocol stack and validate its operation alongside conventional NR on a real-world testbed, and presents gradient-free zeroth-order fine-tuning that requires only scalar feedback, facilitating multi-vendor interoperability.
Yong-Nam Oh, Zi-Han Chen, Timothy J. O'Shea et al.· 0 citations
Machine 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 e...
Timothy J. O'Shea, M. Pennybacker, A. Kharchenko· 0 citations
This paper describes DeepSig's CUDA-based acceleration backend for the OCUDU physical layer and O-RAN fronthaul path, integrated through acceleration interfaces that are largely independent of the underlying acceleration mechanism. The design accelerates PDSCH, PUSCH, SRS, PRACH, split-8 lower-PHY transforms, and O-RAN...
M. Pennybacker, Wanze Liu, A. Kharchenko et al.· 2 citations
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