Year: 2021
Type: preprint
Abstract: Semantic communications focus on the transmission of semantic features. In this letter, we consider a task-oriented multi-user semantic communication system for multimodal data transmission. Particularly, partial users transmit images while the others transmit texts to inquiry the information about the images. To exploit the correlation among the multimodal data from multiple users, we propose a deep neural network enabled semantic communication system, named MU-DeepSC, to execute the visual question answering (VQA) task as an example. Specifically, the transceiver for MU-DeepSC is designed and optimized jointly to capture the features from the correlated multimodal data for task-oriented transmission. Simulation results demonstrate that the proposed MU-DeepSC is more robust to channel variations than the traditional communication systems, especially in the low signal-to-noise (SNR) regime.
Source: arXiv (Cornell University)
Authors: Xie, Huiqiang, Qin, Zhijin, Li, Geoffrey Ye
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4206848268