.png){kind=icon}
.png){kind=icon}
Return
Deep Reinforced Segment Selection and Equalization for Task-Oriented Semantic Communication
.png){kind=icon}
PRE
AI
DOI：10.1109/LCOMM.2024.3418389.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this letter, we propose a novel deep reinforcement learning (DRL)-based segment selection and channel equalization strategy for a task-oriented semantic communication (TOSC) system. In non-linear channel conditions, the TOSC framework aims to coordinate computing complexity with task-oriented accuracy. The proposed method navigates this challenge by deploying a DRL agent at the transmitter to eliminate task-irrelevant data and reduce computational complexities while placing a paired DRL agent at the receiver to select an optimal channel equalizer to ensure high accuracy. The simulation results confirm that the proposed system can reduce computational complexity and improve accuracy by 16% over state-of-the-art methods.
Keywords:
Image segmentation
Semantics
Task analysis
Transmitters
Accuracy
Receivers
Equalizers
Semantic communication
task-oriented communication
reinforcement learning
variational autoencoder
Journal
IF:
4.4
Papers:
1.3W
Citations:
2.2W