Key Responsibilities
· AI-RAN Architecture: Design and deploy AI-RAN virtualization pipelines, integrating deep learning models (e.g., CNN, LSTM) with RAN systems using PyTorch and TensorRT on NVIDIA GH200 platforms for real-time 5G optimization.
· 5G NR Physical Layer Development: Architect and optimize CP-OFDM and DFT-s-OFDM waveforms, supporting flexible subcarrier spacing (15/30/60 kHz), and implement Polar and LDPC channel coding for PUSCH/PDCCH per 3GPP TS 38.212.
· Channel Estimation and Beamforming: Develop DMRS/PTRS-based channel estimation algorithms (LS, MMSE) under multi-path fading conditions, and optimize Massive MIMO beamforming to enhance spectral efficiency.
· Protocol Stack Implementation: Engineer RRC, PDCP, MAC, and RLC layers, implementing slot-based scheduling and dynamic TDD per 3GPP TS 38.321/38.322/38.323/38.331, ensuring robust protocol performance.
· Fronthaul and Time Synchronization: Design eCPRI/CPRI fronthaul interfaces and configure PTP synchronization (C1/C2/C3 formats) using ptp4l, ts2phc, and phc2sys for sub-microsecond precision across CU, DU, and RU.
· Cloud-Native Deployment: Deploy 5G L1/L2/L3 stacks on Kubernetes and Openshift, leveraging SR-IOV, KMMO, and Operator frameworks to achieve high availability and scalability.
· Embedded Systems Optimization: Develop DPDK-based KNI modules and PCIe/QDMA interfaces for FPGA integration, optimizing thread design, core binding, and memory control for ultra-low-latency 5G processing.
Qualifications
Required Skills and Experience
· Education: Bachelor’s degree in Electrical Engineering, Telecommunications, or related field.
· Programming Expertise: 10+ years of experience in C/C++ and CUDA C programming, with proficiency in GPU-accelerated 5G signal processing (e.g., CP-OFDM, Massive MIMO).
· 5G NR Expertise: In-depth knowledge of 3GPP NR standards (TS 38.211, 38.212, 38.213, 38.321, 38.322, 38.323, 38.331), with hands-on experience in Polar/LDPC coding, DMRS-based channel estimation, and slot-based scheduling.
· AI-RAN Virtualization: Proven track record in deploying AI-RAN systems on cloud-native platforms (Kubernetes, Docker, Openshift), with experience in SR-IOV, KMMO, and Operator development.
· Embedded Systems: Expertise in Linux kernel programming, multi-threaded development, and DPDK/ODP for KNI and QDMA bus optimization.
· Time Synchronization: Proficiency in 5G PTP synchronization (ptp4l, ts2phc, phc2sys) for C1/C2/C3 formats.
· Cloud-Native Technologies: Strong experience with microservices, load balancing, and distributed systems (e.g., Spring Boot, Zookeeper, Kafka).
Preferred Skills
· Experience with NVIDIA GH200 or Marvell 95XX/105XX platforms for 5Gacceleration.
· Familiarity with SCF FAPI and LTE physical layer protocols (TS 36.211, 36.212, 36.213).
· Certification in Openshift Operator Development or 3GPP NR Physical Layer Specialization.
· Proficiency in Python, Perl, Java, or TCL for automated test architecture development.