### 2014

Cespedes, Javier; Olmos, Pablo M; Sanchez-Fernandez, Matilde; Perez-Cruz, Fernando

Improved Performance of LDPC-Coded MIMO Systems with EP-based Soft-Decisions Inproceedings

In: 2014 IEEE International Symposium on Information Theory, pp. 1997–2001, IEEE, Honolulu, 2014, ISBN: 978-1-4799-5186-4.

Abstract | Links | BibTeX | Tags: Approximation algorithms, Approximation methods, approximation theory, Channel Coding, channel decoder, communication complexity, complexity, Complexity theory, Detectors, encoding scheme, EP soft bit probability, EP-based soft decision, error statistics, expectation propagation, expectation-maximisation algorithm, expectation-propagation algorithm, Gaussian approximation, Gaussian channels, LDPC, LDPC coded MIMO system, Low Complexity receiver, MIMO, MIMO communication, MIMO communication systems, MIMO receiver, modern communication system, multiple input multiple output, parity check codes, per-antenna soft bit probability, posterior marginalization problem, posterior probability computation, QAM constellation, Quadrature amplitude modulation, radio receivers, signaling, spectral analysis, spectral efficiency maximization, symbol detection, telecommunication signalling, Vectors

@inproceedings{Cespedes2014b,

title = {Improved Performance of LDPC-Coded MIMO Systems with EP-based Soft-Decisions},

author = {Javier Cespedes and Pablo M Olmos and Matilde Sanchez-Fernandez and Fernando Perez-Cruz},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6875183},

isbn = {978-1-4799-5186-4},

year = {2014},

date = {2014-01-01},

booktitle = {2014 IEEE International Symposium on Information Theory},

pages = {1997--2001},

publisher = {IEEE},

address = {Honolulu},

abstract = {Modern communications systems use efficient encoding schemes, multiple-input multiple-output (MIMO) and high-order QAM constellations for maximizing spectral efficiency. However, as the dimensions of the system grow, the design of efficient and low-complexity MIMO receivers possesses technical challenges. Symbol detection can no longer rely on conventional approaches for posterior probability computation due to complexity. Marginalization of this posterior to obtain per-antenna soft-bit probabilities to be fed to a channel decoder is computationally challenging when realistic signaling is used. In this work, we propose to use Expectation Propagation (EP) algorithm to provide an accurate low-complexity Gaussian approximation to the posterior, easily solving the posterior marginalization problem. EP soft-bit probabilities are used in an LDPC-coded MIMO system, achieving outstanding performance improvement compared to similar approaches in the literature for low-complexity LDPC MIMO decoding.},

keywords = {Approximation algorithms, Approximation methods, approximation theory, Channel Coding, channel decoder, communication complexity, complexity, Complexity theory, Detectors, encoding scheme, EP soft bit probability, EP-based soft decision, error statistics, expectation propagation, expectation-maximisation algorithm, expectation-propagation algorithm, Gaussian approximation, Gaussian channels, LDPC, LDPC coded MIMO system, Low Complexity receiver, MIMO, MIMO communication, MIMO communication systems, MIMO receiver, modern communication system, multiple input multiple output, parity check codes, per-antenna soft bit probability, posterior marginalization problem, posterior probability computation, QAM constellation, Quadrature amplitude modulation, radio receivers, signaling, spectral analysis, spectral efficiency maximization, symbol detection, telecommunication signalling, Vectors},

pubstate = {published},

tppubtype = {inproceedings}

}

### 2011

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

Tree-Structured Expectation Propagation for Decoding Finite-Length LDPC Codes Journal Article

In: IEEE Communications Letters, 15 (2), pp. 235–237, 2011, ISSN: 1089-7798.

Abstract | Links | BibTeX | Tags: belief propagation decoder, BP algorithm, BP decoder, code graph, communication complexity, computational complexity, Decoding, finite-length analysis, finite-length low-density parity-check code, LDPC code, LDPC decoding, parity check codes, radiowave propagation, stopping set, TEP algorithm, TEP decoder, tree-structured expectation propagation

@article{Olmos2011c,

title = {Tree-Structured Expectation Propagation for Decoding Finite-Length LDPC Codes},

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

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5682215},

issn = {1089-7798},

year = {2011},

date = {2011-01-01},

journal = {IEEE Communications Letters},

volume = {15},

number = {2},

pages = {235--237},

abstract = {In this paper, we propose Tree-structured Expectation Propagation (TEP) algorithm to decode finite-length Low-Density Parity-Check (LDPC) codes. The TEP decoder is able to continue decoding once the standard Belief Propagation (BP) decoder fails, presenting the same computational complexity as the BP decoder. The BP algorithm is dominated by the presence of stopping sets (SSs) in the code graph. We show that the TEP decoder, without previous knowledge of the graph, naturally avoids some fairly common SSs. This results in a significant improvement in the system performance.},

keywords = {belief propagation decoder, BP algorithm, BP decoder, code graph, communication complexity, computational complexity, Decoding, finite-length analysis, finite-length low-density parity-check code, LDPC code, LDPC decoding, parity check codes, radiowave propagation, stopping set, TEP algorithm, TEP decoder, tree-structured expectation propagation},

pubstate = {published},

tppubtype = {article}

}

### 2010

Perez-Cruz, Fernando; Kulkarni, S R

Robust and Low Complexity Distributed Kernel Least Squares Learning in Sensor Networks Journal Article

In: IEEE Signal Processing Letters, 17 (4), pp. 355–358, 2010, ISSN: 1070-9908.

Abstract | Links | BibTeX | Tags: communication complexity, Consensus, distributed learning, kernel methods, learning (artificial intelligence), low complexity distributed kernel least squares le, message passing, message-passing algorithms, robust nonparametric statistics, sensor network learning, sensor networks, telecommunication computing, Wireless Sensor Networks

@article{Perez-Cruz2010,

title = {Robust and Low Complexity Distributed Kernel Least Squares Learning in Sensor Networks},

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

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5395679},

issn = {1070-9908},

year = {2010},

date = {2010-01-01},

journal = {IEEE Signal Processing Letters},

volume = {17},

number = {4},

pages = {355--358},

abstract = {We present a novel mechanism for consensus building in sensor networks. The proposed algorithm has three main properties that make it suitable for 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 = {communication complexity, Consensus, distributed learning, kernel methods, learning (artificial intelligence), low complexity distributed kernel least squares le, message passing, message-passing algorithms, robust nonparametric statistics, sensor network learning, sensor networks, telecommunication computing, Wireless Sensor Networks},

pubstate = {published},

tppubtype = {article}

}