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Found 5 papers, 2 papers with code
Blind Channel Estimation and Joint Symbol Detection with Data-Driven Factor Graphs
We investigate the application of the factor graph framework for blind joint channel estimation and symbol detection on time-variant linear inter-symbol interference channels.
Local Message Passing on Frustrated Systems
Message passing on factor graphs is a powerful framework for probabilistic inference, which finds important applications in various scientific domains.
Structural Optimization of Factor Graphs for Symbol Detection via Continuous Clustering and Machine Learning
We propose a novel method to optimize the structure of factor graphs for graph-based inference.
Low-complexity Near-optimum Symbol Detection Based on Neural Enhancement of Factor Graphs
In this paper, we develop and evaluate efficient strategies to improve the performance of the factor graph-based symbol detection by means of neural enhancement.
Neural Enhancement of Factor Graph-based Symbol Detection
We study the application of the factor graph framework for symbol detection on linear inter-symbol interference channels.
