12 papers
CRAFT: Fine-Tuning Pre-hoc Explainability in AI-native 6G RAN
Pranshav Gajjar, Vijay K Shah
The next generation of mobile networks is envisioned as fully AI-native, with AI-RAN architectures embedding small language models (SLMs) to perform reasoning over real-time teleme…
TeleSWEBench: A Commit-Driven Benchmark for Evaluating LLM-Powered Software Engineering in Telecommunications
Pranshav Gajjar, Ali Mamaghani, Dinesh Bharadia +1
With the telecommunications field embracing zero touch management alongside novel O-RAN and AI-RAN frameworks, contemporary telecom networks now function as immensely intricate and…
Agents Should Replace Narrow Predictive AI as the Orchestrator in 6G AI-RAN
Pranshav Gajjar, Vijay K Shah
This position paper argues that to achieve Level 5 autonomous 6G networks, the next generation of Artificial Intelligence in Radio Access Networks (AI-RAN) should transition away f…
TeleResilienceBench: Quantifying Resilience for LLM Reasoning in Telecommunications
Pranshav Gajjar, Emmanuel Ojo, Vijay K Shah
Deploying large language models in telecommunications requires more than task accuracy. In realistic workflows, a model may inherit partially completed reasoning from a prior step,…
TeleEmbedBench: A Multi-Corpus Embedding Benchmark for RAG in Telecommunications
Pranshav Gajjar, Vijay K Shah
Large language models (LLMs) are increasingly deployed in the telecommunications domain for critical tasks, relying heavily on Retrieval-Augmented Generation (RAG) to adapt general…
LLM-AUG: Robust Wireless Data Augmentation with In-Context Learning in Large Language Models
Pranshav Gajjar, Manan Tiwari, Sayanta Seth +1
Data scarcity remains a fundamental bottleneck in applying deep learning to wireless communication problems, particularly in scenarios where collecting labeled Radio Frequency (RF)…
Enhancing Confidence Estimation in Telco LLMs via Twin-Pass CoT-Ensembling
Anton Saenko, Pranshav Gajjar, Abiodun Ganiyu +1
Large Language Models (LLMs) are increasingly applied to complex telecommunications tasks, including 3GPP specification analysis and O-RAN network troubleshooting. However, a criti…
Tele-LLM-Hub: Building Context-Aware Multi-Agent LLM Systems for Telecom Networks
Pranshav Gajjar, Cong Shen, Vijay K Shah
This paper introduces Tele-LLM-Hub, a user friendly low-code solution for rapid prototyping and deployment of context aware multi-agent (MA) Large Language Model (LLM) systems tail…
Black-Box Evasion Attacks on Data-Driven Open RAN Apps: Tailored Design and Experimental Evaluation
Pranshav Gajjar, Molham Khoja, Abiodun Ganiyu +4
The impending adoption of Open Radio Access Network (O-RAN) is fueling innovation in the RAN towards data-driven operation. Unlike traditional RAN where the RAN data and its usage…
AI5GTest: AI-Driven Specification-Aware Automated Testing and Validation of 5G O-RAN Components
Abiodun Ganiyu, Pranshav Gajjar, Vijay K Shah
The advent of Open Radio Access Networks (O-RAN) has transformed the telecommunications industry by promoting interoperability, vendor diversity, and rapid innovation. However, its…
ORANSight-2.0: Foundational LLMs for O-RAN
Pranshav Gajjar, Vijay K. Shah
Despite the transformative impact of Large Language Models (LLMs) across critical domains such as healthcare, customer service, and business marketing, their integration into Open…
ORAN-Bench-13K: An Open Source Benchmark for Assessing LLMs in Open Radio Access Networks
Pranshav Gajjar, Vijay K. Shah
Large Language Models (LLMs) can revolutionize how we deploy and operate Open Radio Access Networks (O-RAN) by enhancing network analytics, anomaly detection, and code generation a…