Paper Info
Title
Multi-Agent Feedback Enabled Neural Networks for Intelligent Communications
Abstract
In the intelligent communication field, deep learning (DL) has attracted much attention due to its strong fitting ability and data-driven learning capability. Compared with the typical DL feedforward network structures, an enhancement structure with direct data feedback have been studied and proved to have better performance than the feedfoward networks. However, due to the above simple feedback methods lack sufficient analysis and learning ability on the feedback data, it is inadequate to deal with more complicated nonlinear systems and therefore the performance is limited for further improvement. In this paper, a novel multi-agent feedback enabled neural network (MAFENN) framework is proposed, consisting of three fully cooperative intelligent agents, which make the framework have stronger feedback learning capabilities and more intelligence on feature abstraction, denoising or generation, etc. Furthermore, the MAFENN frame work is theoretically formulated into a three-player Feedback Stackelberg game, and the game is proved to converge to the Feedback Stackelberg equilibrium. The design of MAFENN framework and algorithm are dedicated to enhance the learning capability of the feedfoward DL networks or their variations with the simple data feedback. To verify the MAFENN framework’s feasibility in wireless communications, a multi-agent MAFENN based equalizer (MAFENN-E) is developed for wireless fading channels with inter-symbol interference (ISI). Experimental results show that when the quadrature phase-shift keying (QPSK) modulation scheme is adopted, the SER performance of our proposed method outperforms that of the traditional equalizers by about 2 dB in linear channels. When in nonlinear channels, the SER performance of our proposed method outperforms that of either traditional or DL based equalizers more significantly, which shows the effectiveness and robustness of our proposal in the complex channel environment.
Year
DOI
Venue
2022
10.1109/TWC.2022.3147499
IEEE Transactions on Wireless Communications
Keywords
DocType
Volume
Multi-agent system,feedback neural network,feedback Stackelberg game,channel equalization
Journal
21
Issue
ISSN
Citations 
8
1536-1276
0
PageRank 
References 
Authors
0.34
14
7
Name
Order
Citations
PageRank
Fanglei Sun102.70
yang li2641.06
Ying Wen302.37
Jingchen Hu400.34
Jun Wang52514138.37
Yang Yang6612174.82
Kai Li702.03