Events - Event View
This is the "Event Detail" view, showing all available information for this event.
If the event has passed, click the "Event Report" icon to read a report and view photos that were uploaded.
IEEE Future Networks Artificial Intelligence and Machine Learning (AIML)
If you are a member, please log in to access additional, potentially lower registration fee options.
Category
Affiliate Group Event
Registration Info
Registration is required
About this event
IEEE Future Networks Artificial Intelligence & Machine Learning (AIML)
Working Group
Date: February 20, 2025
Time: 12:00 PM to 01:00 PM
Location: Virtual
Goal-Oriented Generative Semantic Communications with Multimodal LLMs
The integration of Generative Artificial Intelligence (GenAI) models with wireless networks provides ample opportunities to develop innovative technologies with transformative potential. One such technologies is Generative Semantic Communications (Gen SemCom), which leverages the capabilities of state-of-the-art GenAI models to develop ultra-low bitrate semantic communication systems aiming to transmit only the semantic message of interest with high fidelity. GenAI models such as Diffusion, Flow-based, and GAN models, can learn the general distribution of natural signals through training and generate new samples at the inference time. This generative process can be guided or conditioned to synthesize outputs with a desired semantic content. In Gen SemCom, the semantics of interest are extracted at the transmitter, communicated over the channel, and then used at the receiver to guide a generative model to locally synthesize a semantically consistent signal. The emerging generative foundation AI models and Multi-modal Large Language Models (MLLMs) can be leveraged in the SemCom framework to convey the most important semantics of the source signal to the receiver through textual prompts in a super compact form. These models possess a vast general knowledge through intensive pre-training on huge amount of data. This alleviates the need for a shared knowledge base/graph between the semantic transmitter and receiver, obviating the need for corresponding knowledge sharing overheads imposed in current SemCom frameworks. Despite the above benefits, deployment of such large models in the SemCom framework is challenging due to their high computational complexity, energy consumption, and latency. This talk focuses on novel generative approaches to semantic communications, the fundamental bounds on Gen SemCom, and its emerging applications in wireless networks. It investigates the challenges and opportunities of deploying Gen SemCom at various layers in future wireless networks and provides the corresponding future research directions.
Register here:
https://events.vtools.ieee.org/m/463737