Maxime Elkael

Journals & Magazines

S. Maxenti, R. Shirkhani, M. Elkael, L. Bonati, S. D'Oro, T. Melodia, and M. Polese. “AutoRAN: Automated and Zero-Touch Open RAN Systems.” IEEE Transactions on Mobile Computing (2026)Journal

DOI Link

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.

M. Elkael, M. Polese, R. Prasad, S. Maxenti, and T. Melodia. “ALLSTaR — Automated LLM-Driven Scheduler Generation and Testing for Intent-Based RAN.” IEEE Transactions on Mobile Computing (2026)Journal

DOI Link

The evolution toward open, programmable O-RAN and AI-RAN 6G networks creates unprecedented opportunities for Intent-Based Networking (IBN) to dynamically optimize Radio Access Network (RAN) operations based on dynamic operators requirements. However, applying IBN effectively to the RAN scheduler - a critical component determining resource allocation and system performance - remains a significant challenge. Current approaches predominantly rely on coarse-grained network slicing, lacking the granularity for dynamic adaptation to individual user conditions and traffic patterns. Despite the existence of a vast body of scheduling algorithms that could potentially translate high-level intents into executable policies, their practical utilization is hindered by implementation heterogeneity, insufficient systematic evaluation in production environments, and the complexity of developing high-performance scheduler implementations. This necessitates a more granular, flexible, and verifiable approach to align scheduler behavior with operator-defined intents. To address these limitations, we propose ALLSTaR (Automated LLm-driven Scheduler generation and Testing for intent-based RAN), a novel framework leveraging Large Language Models (LLMs) for automated, intent-driven scheduler design, implementation, and evaluation. ALLSTaR interprets natural language intents, automatically generates functional scheduler code from the research literature using Optical Character Recognition (OCR) and LLMs, and intelligently matches operator intents to the most suitable scheduler(s). Our implementation deploys these schedulers as O-RAN dApps, enabling on-the-fly deployment and comprehensive testing on a production-grade, multi-vendor 5G-compliant testbed. This approach has enabled the most extensive Over-The-Air (OTA) experimental comparison of scheduling algorithms to date, evaluating 18 distinct algorithms synthesized from the academic literature and validated through the testing pipeline. The resulting performance profiles serve as the input for our Intent-Based Scheduling (IBS) framework, which dynamically selects and deploys appropriate schedulers that optimally satisfy operator intents.We validate our approach through multiple use cases unattainable with current slicing-based optimization techniques, demonstrating fine-grained control based on buffer status, physical layer conditions, and heterogeneous traffic types.

Conference Papers

R. Shirkhani, S. Maxenti, L. Bonati, N. Mohamadi, M. Elkael, U. Hashmi, J. Mitra, M. Polese, T. Melodia, and S. D’Oro. “TENORAN: Automating Fine-grained Energy Efficiency Profiling in Open RAN Systems.” IEEE INFOCOM 2026 - IEEE Conference on Computer Communications (2026)Conference

DOI Link

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. First, data collection and energy profiling require extensive, repeatable end-to-end tests that must coordinate heterogeneous components and automatically collect power and performance measurements to evaluate energy efficiency. Second, softwarization in O-RAN enables continuous updates and improvements, but results in frequent code releases that might alter energy consumption profiles and calls for continuous data collection and testing. Third, no single power measurement tool can observe all parts of an O-RAN deployment that spans hardware and software domains: rack-level power distribution units, container-level estimators, and dedicated power meters for radio units field-deployed at cell sites all provide partial and heterogeneous views that are difficult to integrate into a unified measurement pipeline.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.

Journals & Magazines

Conference Papers

Conference Papers