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Found 7 papers, 2 papers with code
Telco-RAG: Navigating the Challenges of Retrieval-Augmented Language Models for Telecommunications
The application of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems in the telecommunication domain presents unique challenges, primarily due to the complex nature of telecom standard documents and the rapid evolution of the field.
Predictive Handover Strategy in 6G and Beyond: A Deep and Transfer Learning Approach
Next-generation cellular networks will evolve into more complex and virtualized systems, employing machine learning for enhanced optimization and leveraging higher frequency bands and denser deployments to meet varied service demands.
FlexTrain: A Dynamic Training Framework for Heterogeneous Devices Environments
As deep learning models become increasingly large, they pose significant challenges in heterogeneous devices environments.
TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge
Afterwards, using the provided dataset, an evaluation is conducted to assess the capabilities of LLMs, including GPT-3. 5 and GPT-4.
Large Language Models for Telecom: Forthcoming Impact on the Industry
Large Language Models (LLMs), AI-driven models that can achieve general-purpose language understanding and generation, have emerged as a transformative force, revolutionizing fields well beyond Natural Language Processing (NLP) and garnering unprecedented attention.
On the Global Optimality of Whittle's index policy for minimizing the age of information
This paper examines the average age minimization problem where only a fraction of the network users can transmit simultaneously over unreliable channels.
Information Theory Networking and Internet Architecture Information Theory
The Age of Incorrect Information: an Enabler of Semantics-Empowered Communication
Interestingly, we show that the AoII-optimal policy is also error-optimal for the adopted information source model.
Information Theory Information Theory
