Paper Info
Title
Retrospective Thinking based Multi-Agent System for Wireless Video Transmissions
Abstract
Benefiting from the breakthrough development of the fifth generation (5G), beyond 5G (B5G) wireless communication networks and Artificial Intelligence (AI) in recent years, the artificial intelligence of things (AIoT) is a new trend in the future. AIoT devices often have high-quality wireless video transmission requirements. However, the propagating signals at millimeter wave suffer from high propagation loss and sensitivity to blockage, resulting in the received video is vulnerable to be interfered. Due to the ability of Deep Learning (DL) to discover and learn good representations, some DL methods have achieved breakthrough performance in video recovery. However, most of these methods cannot exploit information from the higher to lower level to refine themselves. In this paper, we propose a novel retrospective thinking based multi-agent (ReTMA) system to solve the interference problem experienced on wireless channels. Compared with other plain feedback models, we add a retrospective agent on the feedback loop, which makes the entire system have stronger capabilities to learn good representative features. We further formulate it as a Stackelberg game to analyze the dependency relationship between the agents and facilitate the complex training issue of the multiple agents. To verify the feasibility of ReTMA system, we randomly add masks to simulate the severe interference received by the video frames in wireless transmissions. Experimental results show that the performances of similarity index measure (SSIM), peak signal-to-noise ratio (PSNR) and classification accuracy all achieve significant gains compared with those of other plain feedback models at different mask ratios.
Year
DOI
Venue
2021
10.1109/ICC42927.2021.9500274
IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2021)
Keywords
DocType
ISSN
Feedback mechanism, retrospective thinking, multi-agent, video transmission
Conference
1550-3607
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Yang Li100.34
Fanglei Sun202.70
Wenbin Song300.68
Ying Wen402.37
Kai Li502.03
Jun Wang6164.99
Yang Yang7612174.82