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Found 12 papers, 1 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.
Telecom Language Models: Must They Be Large?
The increasing interest in Large Language Models (LLMs) within the telecommunications sector underscores their potential to revolutionize operational efficiency.
Linguistic Intelligence in Large Language Models for Telecommunications
Despite their evaluation across a multitude of analytical and reasoning tasks in various scientific domains, a comprehensive exploration of their knowledge and understanding within the realm of natural language tasks in the telecommunications domain is still needed.
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.
Accordion: A Communication-Aware Machine Learning Framework for Next Generation Networks
In this article, we advocate for the design of ad hoc artificial intelligence (AI)/machine learning (ML) models to facilitate their usage in future smart infrastructures based on communication networks.
Power Consumption Modeling of 5G Multi-Carrier Base Stations: A Machine Learning Approach
The fifth generation of the Radio Access Network (RAN) has brought new services, technologies, and paradigms with the corresponding societal benefits.
Machine Learning and Analytical Power Consumption Models for 5G Base Stations
The energy consumption of the fifth generation(5G) of mobile networks is one of the major concerns of the telecom industry.
An Online Algorithm for Computation Offloading in Non-Stationary Environments
We consider the latency minimization problem in a task-offloading scenario, where multiple servers are available to the user equipment for outsourcing computational tasks.
Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks
Network densification and millimeter-wave technologies are key enablers to fulfill the capacity and data rate requirements of the fifth generation (5G) of mobile networks.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Beamwidth Optimization and Resource Partitioning Scheme for Localization Assisted mm-wave Communications
We study a mm-wave wireless network deployed along the roads of an urban area, to support localization and communication services simultaneously for outdoor mobile users.
