14 citations · 48 across the 28 of their papers we have counts for
40 papers
From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity
Yu Ma, Zhen Gao, Li Qiao +11
The ambitious requirements of sixth-generation (6G) networks are driving communication systems from reliable bit delivery toward meaning-aware and task-oriented connectivity. Large…
Ada-TokenCom: Rate-Adaptive Token Communications via Large-Model-Driven Token Compression and Generation
Zijun Zhang, Li Qiao, Mahdi Boloursaz Mashhadi +3
Token Communications (TokenCom) has recently emerged as a new paradigm in which tokens serve as unified units for communication and computation, enabling efficient multimodal seman…
Demo: Real-time Generative Multicasting with On-Device Intent-aware Semantic Decomposition
Xinkai Liu, Mahdi Boloursaz Mashhadi, Yi Ma +1
We present a demonstration for generative multicasting with on-device, intent-aware semantic decomposition. At the transmitter, DNN-based segmentation extracts a semantic map from…
PipeSD: An Efficient Cloud-Edge Collaborative Pipeline Inference Framework with Speculative Decoding
Yunhe Han, Yunqi Gao, Bing Hu +4
Speculative decoding can significantly accelerate LLM inference, especially given that its cloud-edge collaborative deployment offers cloud workload offloading, offline robustness,…
Video TokenCom: Textual Intent-Guided Multi-Rate Video Token Communications with UEP-Based Adaptive Source-Channel Coding
Jingxuan Men, Mahdi Boloursaz Mashhadi, Ning Wang +3
Token Communication (TokenCom) is a new paradigm, motivated by the recent success of Large AI Models (LAMs) and Multimodal Large Language Models (MLLMs), where tokens serve as unif…
Talk Like a Packet: Rethinking Network Traffic Analysis with Transformer Foundation Models
Samara Mayhoub, Chuan Heng Foh, Mahdi Boloursaz Mashhadi +2
Inspired by the success of Transformer-based models in natural language processing, this paper investigates their potential as foundation models for network traffic analysis. We pr…