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Found 11 papers, 0 papers with code
GenAINet: Enabling Wireless Collective Intelligence via Knowledge Transfer and Reasoning
Connecting GenAI agents over a wireless network can potentially unleash the power of collective intelligence and pave the way for artificial general intelligence (AGI).
Improving MIMO channel estimation via receive power feedback
Estimating the channel state is known to be an important problem in wireless networks.
Joint Semantic-Native Communication and Inference via Minimal Simplicial Structures
Specifically, the teacher first maps its data into a k-order simplicial complex and learns its high-order correlations.
Large Generative AI Models for Telecom: The Next Big Thing?
To be specific, large GenAI models are envisioned to open up a new era of autonomous wireless networks, in which multi-modal GenAI models trained over various Telecom data, can be fine-tuned to perform several downstream tasks, eliminating the need for building and training dedicated AI models for each specific task and paving the way for the realization of artificial general intelligence (AGI)-empowered wireless networks.
Understanding Telecom Language Through Large Language Models
In particular, we fine-tune several LLMs including BERT, distilled BERT, RoBERTa and GPT-2, to the Telecom domain languages, and demonstrate a use case for identifying the 3rd Generation Partnership Project (3GPP) standard working groups.
Goal-Oriented Communications for the IoT and Application to Data Compression
Internet of Things (IoT) devices will play an important role in emerging applications, since their sensing, actuation, processing, and wireless communication capabilities stimulate data collection, transmission and decision processes of smart applications.
Goal-Oriented Quantization: Analysis, Design, and Application to Resource Allocation
The task is modeled by the minimization problem of a general goal function $f(x;g)$ for which the decision $x$ has to be taken from a quantized version of the parameters $g$.
A New Approach of Data Pre-processing for Data Compression in Smart Grids
The conventional approach to pre-process data for compression is to apply transforms such as the Fourier, the Karhunen-Lo\`{e}ve, or wavelet transforms.
Decision Set Optimization and Energy-Efficient MIMO Communications
We propose a framework to find a good (finite) decision set which induces a minimal performance loss w. r. t.
Decision-Oriented Communications: Application to Energy-Efficient Resource Allocation
In this paper, we introduce the problem of decision-oriented communications, that is, the goal of the source is to send the right amount of information in order for the intended destination to execute a task.
Task Oriented Channel State Information Quantization
The proposed point of view is fully relevant for a receiver which has to send a quantized version of the channel state to the transmitter.
