### 2011

Salamanca, Luis; Olmos, Pablo M; Murillo-Fuentes, Juan Jose; Perez-Cruz, Fernando

MAP Decoding for LDPC Codes over the Binary Erasure Channel Artículo en actas

En: 2011 IEEE Information Theory Workshop, pp. 145–149, IEEE, Paraty, 2011, ISBN: 978-1-4577-0437-6.

Resumen | Enlaces | BibTeX | Etiquetas: binary erasure channel, Channel Coding, computational complexity, Decoding, generalized peeling decoder, generalized tree-structured expectation propagatio, graphical models, Iterative decoding, LDPC codes, MAP decoding, MAP decoding algorithm, Maximum likelihood decoding, parity check codes, TEP decoder, tree graph theory, Tree graphs, tree-structured expectation propagation, trees (mathematics)

@inproceedings{Salamanca2011a,

title = {MAP Decoding for LDPC Codes over the Binary Erasure Channel},

author = {Luis Salamanca and Pablo M Olmos and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6089364},

isbn = {978-1-4577-0437-6},

year = {2011},

date = {2011-01-01},

booktitle = {2011 IEEE Information Theory Workshop},

pages = {145--149},

publisher = {IEEE},

address = {Paraty},

abstract = {In this paper, we propose a decoding algorithm for LDPC codes that achieves the MAP solution over the BEC. This algorithm, denoted as generalized tree-structured expectation propagation (GTEP), extends the idea of our previous work, the TEP decoder. The GTEP modifies the graph by eliminating a check node of any degree and merging this information with the remaining graph. The GTEP decoder upon completion either provides the unique MAP solution or a tree graph in which the number of parent nodes indicates the multiplicity of the MAP solution. This algorithm can be easily described for the BEC, and it can be cast as a generalized peeling decoder. The GTEP naturally optimizes the complexity of the decoder, by looking for checks nodes of minimum degree to be eliminated first.},

keywords = {binary erasure channel, Channel Coding, computational complexity, Decoding, generalized peeling decoder, generalized tree-structured expectation propagatio, graphical models, Iterative decoding, LDPC codes, MAP decoding, MAP decoding algorithm, Maximum likelihood decoding, parity check codes, TEP decoder, tree graph theory, Tree graphs, tree-structured expectation propagation, trees (mathematics)},

pubstate = {published},

tppubtype = {inproceedings}

}

### 2009

Perez-Cruz, Fernando; Kulkarni, S R

Distributed Least Square for Consensus Building in Sensor Networks Artículo en actas

En: 2009 IEEE International Symposium on Information Theory, pp. 2877–2881, IEEE, Seoul, 2009, ISBN: 978-1-4244-4312-3.

Resumen | Enlaces | BibTeX | Etiquetas: Change detection algorithms, Channel Coding, Distributed computing, distributed least square method, graphical models, Inference algorithms, Kernel, Least squares methods, nonparametric statistics, Parametric statistics, robustness, sensor-network learning, statistical analysis, Telecommunication network reliability, Wireless sensor network, Wireless Sensor Networks

@inproceedings{Perez-Cruz2009,

title = {Distributed Least Square for Consensus Building in Sensor Networks},

author = {Fernando Perez-Cruz and S R Kulkarni},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5205336},

isbn = {978-1-4244-4312-3},

year = {2009},

date = {2009-01-01},

booktitle = {2009 IEEE International Symposium on Information Theory},

pages = {2877--2881},

publisher = {IEEE},

address = {Seoul},

abstract = {We present a novel mechanism for consensus building in sensor networks. The proposed algorithm has three main properties that make it suitable for general sensor-network learning. First, the proposed algorithm is based on robust nonparametric statistics and thereby needs little prior knowledge about the network and the function that needs to be estimated. Second, the algorithm uses only local information about the network and it communicates only with nearby sensors. Third, the algorithm is completely asynchronous and robust. It does not need to coordinate the sensors to estimate the underlying function and it is not affected if other sensors in the network stop working. Therefore, the proposed algorithm is an ideal candidate for sensor networks deployed in remote and inaccessible areas, which might need to change their objective once they have been set up.},

keywords = {Change detection algorithms, Channel Coding, Distributed computing, distributed least square method, graphical models, Inference algorithms, Kernel, Least squares methods, nonparametric statistics, Parametric statistics, robustness, sensor-network learning, statistical analysis, Telecommunication network reliability, Wireless sensor network, Wireless Sensor Networks},

pubstate = {published},

tppubtype = {inproceedings}

}