Vijay K. Shah

Md Sharif Hossen, Zifan Zhang, Dara Ron et al.

The open radio access network (O-RAN) RAN intelligent controller (RIC) hosts data-driven xApps and rApps to optimize network performance. However, two challenges hinder ML-driven xApp/rApp development: (i) key performance metric (KPM) data scarcity caused by interface latency, and (ii) network disruption risks when testing and validating AI models directly on live networks. We develop OpenTwin, a digital twin framework built on an open-source O-RAN simulator (ns-O-RAN-flexRIC) and KPM streaming via the O1 interface, deployed within the non-RT RIC. OpenTwin uses a two-step ML approach: an XGBoost model that learns time-varying network behavior to generate simulator configuration parameters, followed by a time-aware recursive least squares (RLS) tuner that continuously corrects KPM deviations between the twin and real-world measurements. A deviation-aware scoring mechanism monitors twin fidelity and automatically triggers resynchronization upon detecting network drift. We demonstrate OpenTwin with an energy-saving xApp that validates control policies in the virtual space before applying reconfigurations to the physical network. Experimental results show that OpenTwin mirrors real-world KPMs with up to 96% accuracy and enables the xApp to significantly reduce energy consumption without disrupting live operations.

Anton Saenko, Pranshav Gajjar, Abiodun Ganiyu et al.

Large Language Models (LLMs) are increasingly applied to complex telecommunications tasks, including 3GPP specification analysis and O-RAN network troubleshooting. However, a critical limitation remains: LLM-generated confidence scores are often biased and unreliable, frequently exhibiting systematic overconfidence. This lack of trustworthy self-assessment makes it difficult to verify model outputs and safely rely on them in practice. In this paper, we study confidence calibration in telecom-domain LLMs using the representative Gemma-3 model family (4B, 12B, and 27B parameters), evaluated on TeleQnA, ORANBench, and srsRANBench. We show that standard single-pass, verbalized confidence estimates fail to reflect true correctness, often assigning high confidence to incorrect predictions. To address this, we propose a novel Twin-Pass Chain of Thought (CoT)-Ensembling methodology for improving confidence estimation by leveraging multiple independent reasoning evaluations and aggregating their assessments into a calibrated confidence score. Our approach reduces Expected Calibration Error (ECE) by up to 88% across benchmarks, significantly improving the reliability of model self-assessment. These results highlight the limitations of current confidence estimation practices and demonstrate a practical path toward more trustworthy evaluation of LLM outputs in telecommunications.

Pranshav Gajjar, Manan Tiwari, Sayanta Seth et al.

Data scarcity remains a fundamental bottleneck in applying deep learning to wireless communication problems, particularly in scenarios where collecting labeled Radio Frequency (RF) data is expensive, time-consuming, or operationally constrained. This paper proposes LLM-AUG, a data augmentation framework that leverages in-context learning in large language models (LLMs) to generate synthetic training samples directly in a learned embedding space. Unlike conventional generative approaches that require training task-specific models, LLM-AUG performs data generation through structured prompting, enabling rapid adaptation in low-shot regimes. We evaluate LLM-AUG on two representative tasks: modulation classification and interference classification using the RadioML 2016.10A dataset, and the Interference Classification (IC) dataset respectively. Results show that LLM-AUG consistently outperforms traditional augmentation and deep generative baselines across low-shot settings and reaches near oracle performance using only 15% labeled data. LLM-AUG further demonstrates improved robustness under distribution shifts, yielding a 29.4% relative gain over diffusion-based augmentation at a lower SNR value. On the RadioML and IC datasets, LLM-AUG yields a relative gain of 67.6% and 35.7% over the diffusion-based baseline. The t-SNE visualizations further validate that synthetic samples generated by better preserve class structure in the embedding space, leading to more consistent and informative augmentations. These results demonstrate that LLMs can serve as effective and practical data augmenters for wireless machine learning, enabling robust and data-efficient learning in evolving wireless environments.

Geon Kim, Dara Ron, Sukhdeep Singh et al.

Key Performance Measurement (KPM) forecasting is essential for proactive network management of 5G and next-generation telecom networks. However, existing machine learning (ML) approaches face significant limitations in scalability and explainability, restricting their effectiveness in real-world deployments. We propose TelcoAgent, a foundation model-based framework that enables accurate, scalable, and explainable forecasting of multiple KPMs across diverse network cells without the need for site-specific training. Specifically, the framework comprises three key components: (i) an automated three-agent pipeline that constructs a 3rd Generation Partnership Project (3GPP) knowledge graph directly from specification documents, (ii) a scalable, time-series foundation model (TSFM)-based prediction pipeline to deliver accurate, zero-shot forecasting, and finally (iii) a reasoning and explanation pipeline that provides actionable, domain-grounded diagnostics. Evaluated using a 3-month, real-world, city-scale 5G KPM dataset from a U.S.-based network operator, TelcoAgent demonstrates high forecasting accuracy for all 7 considered KPMs per cell across 200 cells, while delivering explainable insights and actionable instructions to address network degradations.

Md Sharif Hossen, Vijay K. Shah, Ismail Guvenc

Unmanned aerial vehicles (UAVs) are increasingly integrated into cellular networks to support emerging Internet of Things (IoT) applications. In such settings, reliable communication is critical for electronic conspicuity (EC), enabling UAV detection and tracking in shared airspace. However, UAVs operate at elevated altitudes where enhanced line-of-sight (LOS) visibility leads to simultaneous exposure to multiple base stations, resulting in strong inter-cell interference. This article presents a system-level analysis of how UAV altitude influences the radio environment and affects EC reliability. Using spatial and network-level metrics, including serving distance, association behavior, and aggregate received power, we show that increasing altitude leads to stronger multi-cell interaction, reduced dominance of nearby sectors, and interference-dominated connectivity. These effects result in fragmented association regions and increased variability in link performance. The analysis is supported by measurement data from a helikite-based spectrum monitoring campaign and corresponding simulation results. Despite differences in experimental conditions, both approaches exhibit consistent altitude-dependent trends. These findings provide practical insights for designing altitude-aware and interference-aware cellular systems to support reliable UAV operation.

Md Sharif Hossen, Vijay K. Shah, Ismail Guvenc

Cellular-connected unmanned aerial vehicles (UAVs) in 5G NR networks experience propagation and interference conditions that vary significantly with altitude and differ substantially from those experienced by terrestrial users. This is primarily caused by the down-tilted antenna sectors in 5G NR networks, which cause UAVs to be served (and interfered with) by the sidelobes. In this paper, we develop a 3GPP-compliant system-level framework for the consistent characterization of key performance indicators (KPIs) such as reference signal received power (RSRP), reference signal received quality (RSRQ), and signal-to-interference-and-noise ratio (SINR) in a multi-site tri-sector deployment with realistic antenna patterns and probabilistic models for line-of-sight (LOS) and non-LOS (NLOS) conditions. Simulation results demonstrate that a critical transition for aerial users is experienced when going from coverage-limited to interference-limited conditions at higher altitudes. Although RSRP is affected by large-scale propagation characteristics and degrades gradually with increasing altitude and inter-site distance (ISD), SINR degrades much faster due to increased interference caused by LOS conditions. On the contrary, increasing ISD improves SINR and RSRQ due to lower interference, even as received power is reduced.

Seyed Bagher Hashemi Natanzi, Pranshav Gajja, Bo Tang et al.

The Open Radio Access Network (O-RAN) architecture allows AI to be embedded directly into the RAN through modular xApps and rApps, yet creating these applications collecting data, training models, writing code, and deploying them safely remains slow and largely manual. Large Language Models (LLMs) offer strong reasoning and code-generation capabilities but are unsuited for the fast, deterministic inference required in real-time RAN control. We present a proof-of-concept Dual-Brain architecture that combines both strengths: an LLM-based orchestrator translates operator intents into data-collection policies and deployment code, while an automated ML engine, NeuralSmith, trains lightweight classifiers on demand via an API. We describe the architecture and provisioning workflow, share practical insights from a containerized O-RAN 5G~SA testbed, and discuss open research directions.