2017–Present · Lead, platform & testbed
GPU-Based AI-RAN
A through-line in my work since 2017: running RAN workloads on commodity GPUs. Started with LDPC decoding for vRAN, now driving AI-RAN testbed development on a cuBB DU with a proprietary MIMO RU.
- GPU / CUDA
- AI-RAN
- O-RAN
- cuBB
- LDPC
- vRAN
The disaggregated-RAN bet has always been that you can run a base station’s L1 on a general-purpose accelerator and still hit the timing budget. The work has moved with the question: in 2017–2021 the binding workloads were LDPC decoding for vRAN (high-throughput, low-latency, codec-rate flexibility) and massive-MIMO array linearization (per-antenna DPD that scales). The contribution on the LDPC side was a GPU decoder integrated into the OpenAirInterface NR stack; on the linearization side, a GPU implementation that made per-antenna DPD across a MIMO array tractable. Today the binding question is how do you bring AI features into the RAN end-to-end — develop in simulation, train against representative data, drop into a real DU/RU stack OTA, validate in the field. That is what the current Samsung AI-RAN testbed work is about: GPU-based AI-RAN on a cuBB-based DU with a proprietary MIMO RU and an end-to-end “sim-to-field” workflow.
Same fundamental bet (GPU-on-RAN), seven years apart, different binding constraints.
Selected publications
AI-RAN (2024–Present)
- Sim2Field: End-to-End Development of AI RANs for 6G — 2nd ACM Workshop on Open and AI RAN (2025). End-to-end methodology for developing AI features for 6G RANs: simulation, training, and field validation as one pipeline.
GPU-Accelerated RAN Workloads (2017–2021)
- GPU-Based, LDPC Decoding for 5G and Beyond — IEEE Open Journal of Circuits and Systems (2021). Journal-length treatment with the full set of techniques: reduced-precision LLRs, multi-stream parallelization, and OpenAirInterface integration.
- GPU-Based LDPC Decoding for vRAN Systems in 5G and Beyond — IEEE ISCAS 2020. The conference introduction of the decoder, including 8-bit LLR representation and multi-stream parallelism to hit 5G latency targets.
- GPU-Based Linearization of MIMO Arrays — IEEE SiPS 2020. GPU implementation of per-antenna DPD across a MIMO array — the linearization-side counterpart to the LDPC work in the same era. (Also appears under AI-based Virtual DPD for MIMO where it supports the NN DPD line.)
- Application-Specific Accelerators for Communications — Handbook of Signal Processing Systems (book chapter). Broader chapter on accelerator design for communications workloads, with vRAN LDPC as one of the case studies.