Optimizing Communication Costs based on the Evolved Variational Message Passing Algorithm using an Enhanced Version of Junction Tree in Wireless Networks

2018 ◽  
Author(s):  
Kaouther Hedhly ◽  
Mohamed Laaraiedh ◽  
Fatma AbdelKefi ◽  
Mohamed Siala
Keyword(s):  
Wireless Networks ◽  
Message Passing ◽  
Communication Costs ◽  
Junction Tree ◽  
Message Passing Algorithm

A variational message passing algorithm for cooperative localization in wireless networks

2016 ◽  
Author(s):  
Kaouther Hedhly ◽  
Mohamed Laaraiedh ◽  
Fatma AbdelKefi ◽  
Mohamed Siala
Keyword(s):  
Wireless Networks ◽  
Message Passing ◽  
Cooperative Localization ◽  
Message Passing Algorithm

Hybrid Message-Passing Algorithm and Architecture for Decoding Cyclic Non-binary LDPC Codes

2013 ◽  
Vol E96.A (12) ◽  
pp. 2652-2659
Author(s):  
Yichao LU ◽  
Gang HE ◽  
Guifen TIAN ◽  
Satoshi GOTO
Keyword(s):  
Message Passing ◽  
Ldpc Codes ◽  
Message Passing Algorithm

scholarly journals Cooperative Localization for Mobile Networks: A Distributed Belief Propagation–Mean Field Message Passing Algorithm

2016 ◽  
Vol 23 (6) ◽  
pp. 828-832 ◽  
Author(s):  
Burak Cakmak ◽  
Daniel N. Urup ◽  
Florian Meyer ◽  
Troels Pedersen ◽  
Bernard H. Fleury ◽  
...  
Keyword(s):  
Mobile Networks ◽  
Message Passing ◽  
Belief Propagation ◽  
Mean Field ◽  
Cooperative Localization ◽  
Message Passing Algorithm

Vector approximate message passing algorithm for compressed sensing with structured matrix perturbation

2020 ◽  
Vol 166 ◽  
pp. 107248 ◽  
Author(s):  
Jiang Zhu ◽  
Qi Zhang ◽  
Xiangming Meng ◽  
Zhiwei Xu
Keyword(s):  
Compressed Sensing ◽  
Message Passing ◽  
Matrix Perturbation ◽  
Approximate Message Passing ◽  
Structured Matrix ◽  
Message Passing Algorithm

Matrix inference and estimation in multi-layer models*

2021 ◽  
Vol 2021 (12) ◽  
pp. 124004
Author(s):  
Parthe Pandit ◽  
Mojtaba Sahraee-Ardakan ◽  
Sundeep Rangan ◽  
Philip Schniter ◽  
Alyson K Fletcher
Keyword(s):  
Neural Network ◽  
Message Passing ◽  
Hidden Variables ◽  
Signal Recovery ◽  
Inference Problem ◽  
Neural Network Learning ◽  
Approximate Message Passing ◽  
Message Passing Algorithm ◽  
Training Samples ◽  
Mixed Regression

Abstract We consider the problem of estimating the input and hidden variables of a stochastic multi-layer neural network (NN) from an observation of the output. The hidden variables in each layer are represented as matrices with statistical interactions along both rows as well as columns. This problem applies to matrix imputation, signal recovery via deep generative prior models, multi-task and mixed regression, and learning certain classes of two-layer NNs. We extend a recently-developed algorithm—multi-layer vector approximate message passing, for this matrix-valued inference problem. It is shown that the performance of the proposed multi-layer matrix vector approximate message passing algorithm can be exactly predicted in a certain random large-system limit, where the dimensions N × d of the unknown quantities grow as N → ∞ with d fixed. In the two-layer neural-network learning problem, this scaling corresponds to the case where the number of input features as well as training samples grow to infinity but the number of hidden nodes stays fixed. The analysis enables a precise prediction of the parameter and test error of the learning.

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