Paper Info
Title
MAFENN: Multi-Agent Feedback Enabled Neural Network for Wireless Channel Equalization
Abstract
Feedback mechanism has been widely used in wireless communication such as channel equalization and resource allocation. In recent years, deep learning (DL) has made great progress in the field of wireless communication. There is now some work that attempts to introduce plain feedback mechanisms into DL algorithm to solve wireless communication problems. However, the improvement of plain feedback DL methods is limited in complex situations due to those methods lack sufficient learning ability on feedback information. In this paper, we propose a Multi-Agent Feedback Enabled Neural Network (MAFENN) equalizer, which consists of a specific learnable feedback agent and two feed-forward agents. Three fully cooperative intelligent agents help the system improve the ability to remove wireless inter-symbol interference (ISI) in receiving ends. We further formulate it into a three-player Stackelberg Game, which helps us to optimize and train this model more efficiently. To verify the feasibility of our proposed MAFENN system and the Stackelberg Game optimization, we conduct a series of experiments to compare the symbol error rate (SER) performance of the MAFENN equalizer and the other methods which utilizes quadrature phase-shift keying (QPSK) modulation scheme. Our performance outperforms that of the other equalizers at different signal-to-noise ratio (SNR) settings for both linear and nonlinear channels.
Year
DOI
Venue
2021
10.1109/GLOBECOM46510.2021.9685522
2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)
Keywords
DocType
ISSN
Feedback mechanism, Stackelberg game, multi-agent system, wireless channel equalization
Conference
2334-0983
Citations 
PageRank 
References 
0
0.34
0
Authors
9
Name
Order
Citations
PageRank
Yangyang Li1255.92
Fanglei Sun202.70
Weiqin Zu300.34
Wenbin Song400.68
Ying Wen502.37
Jun Wang6164.99
Yang Yang7612174.82
Kai Li802.03
Liantao Wu901.69