Education
- Ph.D. in Computer Engineering (current) - Northeastern University, Boston, MA, USA
- B.Sc. in Electrical Engineering (2023) - Sharif University of Technology, Tehran, Iran
Research Interests
- 5G and beyond cellular networks
- O-RAN
- Network automation
- Energy analysis and optimization in O-RAN
Ravis is a PhD Candidate at the Institute for the Wireless Internet of Things at Northeastern University under Prof. Tommaso Melodia. She received her B.Sc. in Electrical Engineering with minor concentration on communication systems from Sharif University of Technology in Spring 2023. Her research focuses on Open Radio Access Networks in next-generation cellular networks, mainly on network automation and automated testing. She also works on power consumption across O-RAN components and exploring optimization approaches for network energy efficiency.
Publications
2026
Journals and Magazines
Modern cellular networks adopt a software-based and disaggregated approach to support diverse requirements and mission-critical reliability needs. While softwarization introduces flexibility, it also increases the complexity of the network architectures, which calls for robust automation frameworks that can deliver efficient and fully-autonomous configuration, scalability, and multi-vendor integration. This paper presents AutoRAN, an automated, intent-driven framework for zero-touch provisioning of open, programmable cellular networks. Leveraging cloud-native principles, AutoRAN employs virtualization, declarative infrastructure-as-code templates, and disaggregated micro-services to abstract physical resources and protocol stacks. Its orchestration engine integrates Large Language Models (LLMs) to translate high-level intents into machine-readable configurations, enabling closed-loop control via telemetry-driven observability. Implemented on a multi-architecture OpenShift cluster with heterogeneous compute (x86/ARM CPUs, NVIDIA GPUs) and multi-vendor Radio Access Network (RAN) hardware (Foxconn, NI), AutoRAN automates deployment of O-RANcompliant stacks-including OpenAirInterface, NVIDIA ARC RAN, Open5GS core, and O-RAN Software Community (OSC) RIC components-using Continuous Integration and Continuous Delivery/Deployment (CI/CD) pipelines. Experimental results demonstrate that AutoRAN is capable of deploying an end-toend Private 5G network in less than 60 seconds with 1.6 Gbps throughput, validating its ability to streamline configuration, accelerate testing, and reduce manual intervention with similar performance than non cloud-based implementations. With its novel LLM-assisted intent translation mechanism, and performanceoptimized automation workflow for multi-vendor environments, AutoRAN has the potential of advancing the robustness of nextgeneration cellular supply chains through reproducible, intentbased provisioning across public and private deployments.
LinkConference Papers
The transition to disaggregated and interoperable Open Radio Access Network (RAN) architectures and the introduction of RAN Intelligent Controllers (RICs) in O-RAN creates new resource optimization opportunities and fine-grained tuning and configuration of network components to save energy while fulfilling service demand. However, unlocking this potential requires fine-grained and accurate energy measurements across heterogeneous deployments. Three factors make this particularly challenging [...]. To address these challenges, we design the TENORAN framework, an automated measurement scaffold for fine-grained energy efficiency profiling of O-RAN deployments, and prototype it on a heterogeneous OpenShift cluster. TENORAN instruments an end-to-end deployment based on high-level specifications (e.g., gNB software stack and split options, traffic profiles), and collects synchronized performance metrics and power measurements for individual RAN components while the network is under controlled workloads including over-the-air traffic. Our experimental results demonstrate energy profiling of end-to-end experiments with xApps in the loop, energy efficiency differences between two RAN stacks, OpenAirInterface and srsRAN, in uplink and downlink, and core network power consumption trends.
Link2024
Conference Papers
While the availability of large datasets has been instrumental to advance fields like computer vision and natural language processing, this has not been the case in mobile networking. Indeed, mobile traffic data is often unavailable due to privacy or regulatory concerns. This problem becomes especially relevant in Open Radio Access Network (RAN), where artificial intelligence can potentially drive optimization and control of the RAN, but still lags behind due to the lack of training datasets. While substantial work has focused on developing testbeds that can accurately reflect production environments, the same level of effort has not been put into twinning the traffic that traverse such networks.To fill this gap, in this paper, we design a methodology to twin real-world cellular traffic traces in experimental Open RAN testbeds. We demonstrate our approach on the Colosseum Open RAN digital twin, and publicly release a large dataset (more than 500 hours and 450 GB) with PHY-, MAC-, and App-layer Key Performance Measurements (KPMs), and protocol stack logs. Our analysis shows that our dataset can be used to develop and evaluate a number of Open RAN use cases, including those with strict latency requirements.
Link