## 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 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} } 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. |

## 2013 |

Salamanca, Luis; Murillo-Fuentes, Juan Jose; Olmos, Pablo M; Perez-Cruz, Fernando Improving the BP Estimate over the AWGN Channel Using Tree-Structured Expectation Propagation Inproceedings 2013 IEEE International Symposium on Information Theory, pp. 2990–2994, IEEE, Istanbul, 2013, ISSN: 2157-8095. Abstract | Links | BibTeX | Tags: Approximation algorithms, Approximation methods, AWGN channels, BEC, belief propagation decoding, BI-AWGN channel, binary additive white Gaussian noise channel, binary erasure channel, BP estimation, Channel Coding, Complexity theory, error rate reduction, error statistics, Expectation, finite-length codes, Iterative decoding, LDPC codes, LDPC decoding, low-density parity-check decoding, Maximum likelihood decoding, parity check codes, posterior distribution, Propagation, TEP algorithm, tree-structured expectation propagation algorithm, trees (mathematics) @inproceedings{Salamanca2013, title = {Improving the BP Estimate over the AWGN Channel Using Tree-Structured Expectation Propagation}, author = {Luis Salamanca and Juan Jose Murillo-Fuentes and Pablo M Olmos and Fernando Perez-Cruz}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6620774}, issn = {2157-8095}, year = {2013}, date = {2013-01-01}, booktitle = {2013 IEEE International Symposium on Information Theory}, pages = {2990--2994}, publisher = {IEEE}, address = {Istanbul}, abstract = {In this paper, we propose the tree-structured expectation propagation (TEP) algorithm for low-density parity-check (LDPC) decoding over the binary additive white Gaussian noise (BI-AWGN) channel. By approximating the posterior distribution by a tree-structure factorization, the TEP has been proven to improve belief propagation (BP) decoding over the binary erasure channel (BEC). We show for the AWGN channel how the TEP decoder is also able to capture additional information disregarded by the BP solution, which leads to a noticeable reduction of the error rate for finite-length codes. We show that for the range of codes of interest, the TEP gain is obtained with a slight increase in complexity over that of the BP algorithm. An efficient way of constructing the tree-like structure is also described.}, keywords = {Approximation algorithms, Approximation methods, AWGN channels, BEC, belief propagation decoding, BI-AWGN channel, binary additive white Gaussian noise channel, binary erasure channel, BP estimation, Channel Coding, Complexity theory, error rate reduction, error statistics, Expectation, finite-length codes, Iterative decoding, LDPC codes, LDPC decoding, low-density parity-check decoding, Maximum likelihood decoding, parity check codes, posterior distribution, Propagation, TEP algorithm, tree-structured expectation propagation algorithm, trees (mathematics)}, pubstate = {published}, tppubtype = {inproceedings} } In this paper, we propose the tree-structured expectation propagation (TEP) algorithm for low-density parity-check (LDPC) decoding over the binary additive white Gaussian noise (BI-AWGN) channel. By approximating the posterior distribution by a tree-structure factorization, the TEP has been proven to improve belief propagation (BP) decoding over the binary erasure channel (BEC). We show for the AWGN channel how the TEP decoder is also able to capture additional information disregarded by the BP solution, which leads to a noticeable reduction of the error rate for finite-length codes. We show that for the range of codes of interest, the TEP gain is obtained with a slight increase in complexity over that of the BP algorithm. An efficient way of constructing the tree-like structure is also described. |

Yang, Wei; Durisi, Giuseppe; Koch, Tobias; Polyanskiy, Yury Quasi-Static SIMO Fading Channels at Finite Blocklength Inproceedings 2013 IEEE International Symposium on Information Theory, pp. 1531–1535, IEEE, Istanbul, 2013, ISSN: 2157-8095. Abstract | Links | BibTeX | Tags: achievability bounds, AWGN channel, AWGN channels, channel capacity, channel dispersion, channel gains, Dispersion, error probability, error statistics, Fading, fading channels, fading realizations, fast convergence, finite blocklength, maximal achievable rate, numerical evaluation, outage capacity, quasistatic SIMO fading channels, Random variables, Receivers, SIMO Rician channel, single-input multiple-output, Transmitters, zero dispersion @inproceedings{Yang2013a, title = {Quasi-Static SIMO Fading Channels at Finite Blocklength}, author = {Wei Yang and Giuseppe Durisi and Tobias Koch and Yury Polyanskiy}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6620483}, issn = {2157-8095}, year = {2013}, date = {2013-01-01}, booktitle = {2013 IEEE International Symposium on Information Theory}, pages = {1531--1535}, publisher = {IEEE}, address = {Istanbul}, abstract = {We investigate the maximal achievable rate for a given blocklength and error probability over quasi-static single-input multiple-output (SIMO) fading channels. Under mild conditions on the channel gains, it is shown that the channel dispersion is zero regardless of whether the fading realizations are available at the transmitter and/or the receiver. The result follows from computationally and analytically tractable converse and achievability bounds. Through numerical evaluation, we verify that, in some scenarios, zero dispersion indeed entails fast convergence to outage capacity as the blocklength increases. In the example of a particular 1×2 SIMO Rician channel, the blocklength required to achieve 90% of capacity is about an order of magnitude smaller compared to the blocklength required for an AWGN channel with the same capacity.}, keywords = {achievability bounds, AWGN channel, AWGN channels, channel capacity, channel dispersion, channel gains, Dispersion, error probability, error statistics, Fading, fading channels, fading realizations, fast convergence, finite blocklength, maximal achievable rate, numerical evaluation, outage capacity, quasistatic SIMO fading channels, Random variables, Receivers, SIMO Rician channel, single-input multiple-output, Transmitters, zero dispersion}, pubstate = {published}, tppubtype = {inproceedings} } We investigate the maximal achievable rate for a given blocklength and error probability over quasi-static single-input multiple-output (SIMO) fading channels. Under mild conditions on the channel gains, it is shown that the channel dispersion is zero regardless of whether the fading realizations are available at the transmitter and/or the receiver. The result follows from computationally and analytically tractable converse and achievability bounds. Through numerical evaluation, we verify that, in some scenarios, zero dispersion indeed entails fast convergence to outage capacity as the blocklength increases. In the example of a particular 1×2 SIMO Rician channel, the blocklength required to achieve 90% of capacity is about an order of magnitude smaller compared to the blocklength required for an AWGN channel with the same capacity. |

## 2012 |

Durisi, Giuseppe; Koch, Tobias; Polyanskiy, Yury Diversity Versus Channel Knowledge at Finite Block-Length Inproceedings 2012 IEEE Information Theory Workshop, pp. 572–576, IEEE, Lausanne, 2012, ISBN: 978-1-4673-0223-4. Abstract | Links | BibTeX | Tags: Approximation methods, block error probability, channel coherence time, Channel estimation, channel knowledge, Coherence, diversity, diversity reception, error statistics, Fading, finite block-length, maximal achievable rate, noncoherent setting, Rayleigh block-fading channels, Rayleigh channels, Receivers, Signal to noise ratio, Upper bound @inproceedings{Durisi2012, title = {Diversity Versus Channel Knowledge at Finite Block-Length}, author = {Giuseppe Durisi and Tobias Koch and Yury Polyanskiy}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6404740}, isbn = {978-1-4673-0223-4}, year = {2012}, date = {2012-01-01}, booktitle = {2012 IEEE Information Theory Workshop}, pages = {572--576}, publisher = {IEEE}, address = {Lausanne}, abstract = {We study the maximal achievable rate R*(n, ∈) for a given block-length n and block error probability o over Rayleigh block-fading channels in the noncoherent setting and in the finite block-length regime. Our results show that for a given block-length and error probability, R*(n, ∈) is not monotonic in the channel's coherence time, but there exists a rate maximizing coherence time that optimally trades between diversity and cost of estimating the channel.}, keywords = {Approximation methods, block error probability, channel coherence time, Channel estimation, channel knowledge, Coherence, diversity, diversity reception, error statistics, Fading, finite block-length, maximal achievable rate, noncoherent setting, Rayleigh block-fading channels, Rayleigh channels, Receivers, Signal to noise ratio, Upper bound}, pubstate = {published}, tppubtype = {inproceedings} } We study the maximal achievable rate R*(n, ∈) for a given block-length n and block error probability o over Rayleigh block-fading channels in the noncoherent setting and in the finite block-length regime. Our results show that for a given block-length and error probability, R*(n, ∈) is not monotonic in the channel's coherence time, but there exists a rate maximizing coherence time that optimally trades between diversity and cost of estimating the channel. |

Campo, Adria Tauste; Vazquez-Vilar, Gonzalo; i Fàbregas, Albert Guillen; Koch, Tobias; Martinez, Alfonso Random Coding Bounds that Attain the Joint Source-Channel Exponent Inproceedings 2012 46th Annual Conference on Information Sciences and Systems (CISS), pp. 1–5, IEEE, Princeton, 2012, ISBN: 978-1-4673-3140-1. Abstract | Links | BibTeX | Tags: code construction, combined source-channel coding, Csiszár error exponent, Ducts, error probability, error statistics, Gallager exponent, joint source-channel coding, joint source-channel exponent, random codes, random-coding upper bound, Yttrium @inproceedings{Campo2012, title = {Random Coding Bounds that Attain the Joint Source-Channel Exponent}, author = {Adria Tauste Campo and Gonzalo Vazquez-Vilar and Albert Guillen i Fàbregas and Tobias Koch and Alfonso Martinez}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6310910}, isbn = {978-1-4673-3140-1}, year = {2012}, date = {2012-01-01}, booktitle = {2012 46th Annual Conference on Information Sciences and Systems (CISS)}, pages = {1--5}, publisher = {IEEE}, address = {Princeton}, abstract = {This paper presents a random-coding upper bound on the average error probability of joint source-channel coding that attains Csiszár's error exponent. The bound is based on a code construction for which source messages are assigned to disjoint subsets (classes), and codewords are generated according to a distribution that depends on the class of the source message. For a single class, the bound recovers Gallager's exponent; identifying the classes with source type classes, it recovers Csiszár's exponent. Moreover, it is shown that as a two appropriately designed classes are sufficient to attain Csiszár's exponent.}, keywords = {code construction, combined source-channel coding, Csiszár error exponent, Ducts, error probability, error statistics, Gallager exponent, joint source-channel coding, joint source-channel exponent, random codes, random-coding upper bound, Yttrium}, pubstate = {published}, tppubtype = {inproceedings} } This paper presents a random-coding upper bound on the average error probability of joint source-channel coding that attains Csiszár's error exponent. The bound is based on a code construction for which source messages are assigned to disjoint subsets (classes), and codewords are generated according to a distribution that depends on the class of the source message. For a single class, the bound recovers Gallager's exponent; identifying the classes with source type classes, it recovers Csiszár's exponent. Moreover, it is shown that as a two appropriately designed classes are sufficient to attain Csiszár's exponent. |

Campo, Adria Tauste; Vazquez-Vilar, Gonzalo; i Fabregas, Albert Guillen; Koch, Tobias; Martinez, Alfonso Achieving Csiszár's Source-Channel Coding Exponent with Product Distributions Inproceedings 2012 IEEE International Symposium on Information Theory Proceedings, pp. 1548–1552, IEEE, Cambridge, MA, 2012, ISSN: 2157-8095. Abstract | Links | BibTeX | Tags: average probability of error, Channel Coding, code construction, codewords, Csiszár's source-channel coding, Decoding, Encoding, error probability, error statistics, Joints, Manganese, product distributions, random codes, random-coding upper bound, source coding, source messages, Upper bound @inproceedings{Campo2012a, title = {Achieving Csiszár's Source-Channel Coding Exponent with Product Distributions}, author = {Adria Tauste Campo and Gonzalo Vazquez-Vilar and Albert Guillen i Fabregas and Tobias Koch and Alfonso Martinez}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6283524}, issn = {2157-8095}, year = {2012}, date = {2012-01-01}, booktitle = {2012 IEEE International Symposium on Information Theory Proceedings}, pages = {1548--1552}, publisher = {IEEE}, address = {Cambridge, MA}, abstract = {We derive a random-coding upper bound on the average probability of error of joint source-channel coding that recovers Csiszár's error exponent when used with product distributions over the channel inputs. Our proof technique for the error probability analysis employs a code construction for which source messages are assigned to subsets and codewords are generated with a distribution that depends on the subset.}, keywords = {average probability of error, Channel Coding, code construction, codewords, Csiszár's source-channel coding, Decoding, Encoding, error probability, error statistics, Joints, Manganese, product distributions, random codes, random-coding upper bound, source coding, source messages, Upper bound}, pubstate = {published}, tppubtype = {inproceedings} } We derive a random-coding upper bound on the average probability of error of joint source-channel coding that recovers Csiszár's error exponent when used with product distributions over the channel inputs. Our proof technique for the error probability analysis employs a code construction for which source messages are assigned to subsets and codewords are generated with a distribution that depends on the subset. |

## 2011 |

Goparaju, S; Calderbank, A R; Carson, W R; Rodrigues, Miguel R D; Perez-Cruz, Fernando When to Add Another Dimension when Communicating over MIMO Channels Inproceedings 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3100–3103, IEEE, Prague, 2011, ISSN: 1520-6149. Abstract | Links | BibTeX | Tags: divide and conquer approach, divide and conquer methods, error probability, error rate, error statistics, Gaussian channels, Lattices, Manganese, MIMO, MIMO channel, MIMO communication, multiple input multiple output Gaussian channel, Mutual information, optimal power allocation, power allocation, power constraint, receive filter, Resource management, Signal to noise ratio, signal-to-noise ratio, transmit filter, Upper bound @inproceedings{Goparaju2011, title = {When to Add Another Dimension when Communicating over MIMO Channels}, author = {S Goparaju and A R Calderbank and W R Carson and Miguel R D Rodrigues and Fernando Perez-Cruz}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5946351}, issn = {1520-6149}, year = {2011}, date = {2011-01-01}, booktitle = {2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages = {3100--3103}, publisher = {IEEE}, address = {Prague}, abstract = {This paper introduces a divide and conquer approach to the design of transmit and receive filters for communication over a Multiple Input Multiple Output (MIMO) Gaussian channel subject to an average power constraint. It involves conversion to a set of parallel scalar channels, possibly with very different gains, followed by coding per sub-channel (i.e. over time) rather than coding across sub-channels (i.e. over time and space). The loss in performance is negligible at high signal-to-noise ratio (SNR) and not significant at medium SNR. The advantages are reduction in signal processing complexity and greater insight into the SNR thresholds at which a channel is first allocated power. This insight is a consequence of formulating the optimal power allocation in terms of an upper bound on error rate that is determined by parameters of the input lattice such as the minimum distance and kissing number. The resulting thresholds are given explicitly in terms of these lattice parameters. By contrast, when the optimization problem is phrased in terms of maximizing mutual information, the solution is mercury waterfilling, and the thresholds are implicit.}, keywords = {divide and conquer approach, divide and conquer methods, error probability, error rate, error statistics, Gaussian channels, Lattices, Manganese, MIMO, MIMO channel, MIMO communication, multiple input multiple output Gaussian channel, Mutual information, optimal power allocation, power allocation, power constraint, receive filter, Resource management, Signal to noise ratio, signal-to-noise ratio, transmit filter, Upper bound}, pubstate = {published}, tppubtype = {inproceedings} } This paper introduces a divide and conquer approach to the design of transmit and receive filters for communication over a Multiple Input Multiple Output (MIMO) Gaussian channel subject to an average power constraint. It involves conversion to a set of parallel scalar channels, possibly with very different gains, followed by coding per sub-channel (i.e. over time) rather than coding across sub-channels (i.e. over time and space). The loss in performance is negligible at high signal-to-noise ratio (SNR) and not significant at medium SNR. The advantages are reduction in signal processing complexity and greater insight into the SNR thresholds at which a channel is first allocated power. This insight is a consequence of formulating the optimal power allocation in terms of an upper bound on error rate that is determined by parameters of the input lattice such as the minimum distance and kissing number. The resulting thresholds are given explicitly in terms of these lattice parameters. By contrast, when the optimization problem is phrased in terms of maximizing mutual information, the solution is mercury waterfilling, and the thresholds are implicit. |

## 2010 |

Salamanca, Luis; Murillo-Fuentes, Juan Jose; Perez-Cruz, Fernando Bayesian BCJR for Channel Equalization and Decoding Inproceedings 2010 IEEE International Workshop on Machine Learning for Signal Processing, pp. 53–58, IEEE, Kittila, 2010, ISSN: 1551-2541. Abstract | Links | BibTeX | Tags: a posteriori probability, Bayes methods, Bayesian BCJR, Bayesian methods, Bit error rate, channel decoding, channel estate information, Channel estimation, Decoding, digital communication, digital communications, equalisers, Equalizers, error statistics, Markov processes, Maximum likelihood decoding, maximum likelihood estimation, multipath channel, probabilistic channel equalization, Probability, single input single output model, SISO model, statistical information, Training @inproceedings{Salamanca2010, title = {Bayesian BCJR for Channel Equalization and Decoding}, author = {Luis Salamanca and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5589201}, issn = {1551-2541}, year = {2010}, date = {2010-01-01}, booktitle = {2010 IEEE International Workshop on Machine Learning for Signal Processing}, pages = {53--58}, publisher = {IEEE}, address = {Kittila}, abstract = {In this paper we focus on the probabilistic channel equalization in digital communications. We face the single input single output (SISO) model to show how the statistical information about the multipath channel can be exploited to further improve our estimation of the a posteriori probabilities (APP) during the equalization process. We consider not only the uncertainty due to the noise in the channel, but also in the estimate of the channel estate information (CSI). Thus, we resort to a Bayesian approach for the computation of the APP. This novel algorithm has the same complexity as the BCJR, exhibiting lower bit error rate at the output of the channel decoder than the standard BCJR that considers maximum likelihood (ML) to estimate the CSI.}, keywords = {a posteriori probability, Bayes methods, Bayesian BCJR, Bayesian methods, Bit error rate, channel decoding, channel estate information, Channel estimation, Decoding, digital communication, digital communications, equalisers, Equalizers, error statistics, Markov processes, Maximum likelihood decoding, maximum likelihood estimation, multipath channel, probabilistic channel equalization, Probability, single input single output model, SISO model, statistical information, Training}, pubstate = {published}, tppubtype = {inproceedings} } In this paper we focus on the probabilistic channel equalization in digital communications. We face the single input single output (SISO) model to show how the statistical information about the multipath channel can be exploited to further improve our estimation of the a posteriori probabilities (APP) during the equalization process. We consider not only the uncertainty due to the noise in the channel, but also in the estimate of the channel estate information (CSI). Thus, we resort to a Bayesian approach for the computation of the APP. This novel algorithm has the same complexity as the BCJR, exhibiting lower bit error rate at the output of the channel decoder than the standard BCJR that considers maximum likelihood (ML) to estimate the CSI. |

Salamanca, Luis; Murillo-Fuentes, Juan Jose; Perez-Cruz, Fernando Channel Decoding with a Bayesian Equalizer Inproceedings 2010 IEEE International Symposium on Information Theory, pp. 1998–2002, IEEE, Austin, TX, 2010, ISBN: 978-1-4244-7892-7. Abstract | Links | BibTeX | Tags: a posteriori probability, Bayesian equalizer, Bayesian methods, BER, Bit error rate, Channel Coding, channel decoding, channel estate information, Communication channels, Decoding, equalisers, Equalizers, error statistics, low-density parity-check decoders, LPDC decoders, Maximum likelihood decoding, maximum likelihood detection, maximum likelihood estimation, Noise reduction, parity check codes, Probability, Uncertainty @inproceedings{Salamanca2010a, title = {Channel Decoding with a Bayesian Equalizer}, author = {Luis Salamanca and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5513348}, isbn = {978-1-4244-7892-7}, year = {2010}, date = {2010-01-01}, booktitle = {2010 IEEE International Symposium on Information Theory}, pages = {1998--2002}, publisher = {IEEE}, address = {Austin, TX}, abstract = {Low-density parity-check (LPDC) decoders assume the channel estate information (CSI) is known and they have the true a posteriori probability (APP) for each transmitted bit. But in most cases of interest, the CSI needs to be estimated with the help of a short training sequence and the LDPC decoder has to decode the received word using faulty APP estimates. In this paper, we study the uncertainty in the CSI estimate and how it affects the bit error rate (BER) output by the LDPC decoder. To improve these APP estimates, we propose a Bayesian equalizer that takes into consideration not only the uncertainty due to the noise in the channel, but also the uncertainty in the CSI estimate, reducing the BER after the LDPC decoder.}, keywords = {a posteriori probability, Bayesian equalizer, Bayesian methods, BER, Bit error rate, Channel Coding, channel decoding, channel estate information, Communication channels, Decoding, equalisers, Equalizers, error statistics, low-density parity-check decoders, LPDC decoders, Maximum likelihood decoding, maximum likelihood detection, maximum likelihood estimation, Noise reduction, parity check codes, Probability, Uncertainty}, pubstate = {published}, tppubtype = {inproceedings} } Low-density parity-check (LPDC) decoders assume the channel estate information (CSI) is known and they have the true a posteriori probability (APP) for each transmitted bit. But in most cases of interest, the CSI needs to be estimated with the help of a short training sequence and the LDPC decoder has to decode the received word using faulty APP estimates. In this paper, we study the uncertainty in the CSI estimate and how it affects the bit error rate (BER) output by the LDPC decoder. To improve these APP estimates, we propose a Bayesian equalizer that takes into consideration not only the uncertainty due to the noise in the channel, but also the uncertainty in the CSI estimate, reducing the BER after the LDPC decoder. |

Olmos, Pablo M; Murillo-Fuentes, Juan Jose; Perez-Cruz, Fernando Joint Nonlinear Channel Equalization and Soft LDPC Decoding with Gaussian Processes Journal Article IEEE Transactions on Signal Processing, 58 (3), pp. 1183–1192, 2010, ISSN: 1053-587X. Abstract | Links | BibTeX | Tags: Bayesian nonlinear classification tool, Bit error rate, Channel Coding, channel equalizers, Channel estimation, Coding, equalisers, equalization, error statistics, Gaussian processes, GPC, joint nonlinear channel equalization, low-density parity-check (LDPC), low-density parity-check channel decoder, Machine learning, nonlinear channel, nonlinear codes, parity check codes, posterior probability estimates, soft LDPC decoding, soft-decoding, support vector machine (SVM) @article{Olmos2010a, title = {Joint Nonlinear Channel Equalization and Soft LDPC Decoding with Gaussian Processes}, author = {Pablo M Olmos and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5290078}, issn = {1053-587X}, year = {2010}, date = {2010-01-01}, journal = {IEEE Transactions on Signal Processing}, volume = {58}, number = {3}, pages = {1183--1192}, abstract = {In this paper, we introduce a new approach for nonlinear equalization based on Gaussian processes for classification (GPC). We propose to measure the performance of this equalizer after a low-density parity-check channel decoder has detected the received sequence. Typically, most channel equalizers concentrate on reducing the bit error rate, instead of providing accurate posterior probability estimates. We show that the accuracy of these estimates is essential for optimal performance of the channel decoder and that the error rate output by the equalizer might be irrelevant to understand the performance of the overall communication receiver. In this sense, GPC is a Bayesian nonlinear classification tool that provides accurate posterior probability estimates with short training sequences. In the experimental section, we compare the proposed GPC-based equalizer with state-of-the-art solutions to illustrate its improved performance.}, keywords = {Bayesian nonlinear classification tool, Bit error rate, Channel Coding, channel equalizers, Channel estimation, Coding, equalisers, equalization, error statistics, Gaussian processes, GPC, joint nonlinear channel equalization, low-density parity-check (LDPC), low-density parity-check channel decoder, Machine learning, nonlinear channel, nonlinear codes, parity check codes, posterior probability estimates, soft LDPC decoding, soft-decoding, support vector machine (SVM)}, pubstate = {published}, tppubtype = {article} } In this paper, we introduce a new approach for nonlinear equalization based on Gaussian processes for classification (GPC). We propose to measure the performance of this equalizer after a low-density parity-check channel decoder has detected the received sequence. Typically, most channel equalizers concentrate on reducing the bit error rate, instead of providing accurate posterior probability estimates. We show that the accuracy of these estimates is essential for optimal performance of the channel decoder and that the error rate output by the equalizer might be irrelevant to understand the performance of the overall communication receiver. In this sense, GPC is a Bayesian nonlinear classification tool that provides accurate posterior probability estimates with short training sequences. In the experimental section, we compare the proposed GPC-based equalizer with state-of-the-art solutions to illustrate its improved performance. |

## 2009 |

Bravo-Santos, Ángel M; Djuric, Petar M Cooperative Relay Communications in Mesh Networks Inproceedings 2009 IEEE 10th Workshop on Signal Processing Advances in Wireless Communications, pp. 499–503, IEEE, Perugia, 2009, ISBN: 978-1-4244-3695-8. Abstract | Links | BibTeX | Tags: binary transmission, bit error probability, Bit error rate, cooperative relay communications, decode-and-forward relays, Detectors, error statistics, Maximum likelihood decoding, maximum likelihood detection, Mesh networks, mesh wireless networks, multi-hop networks, Network topology, optimal node decision rules, Peer to peer computing, radio networks, Relays, spread spectrum communication, telecommunication network topology, Wireless Sensor Networks @inproceedings{Bravo-Santos2009, title = {Cooperative Relay Communications in Mesh Networks}, author = {Ángel M Bravo-Santos and Petar M Djuric}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5161835}, isbn = {978-1-4244-3695-8}, year = {2009}, date = {2009-01-01}, booktitle = {2009 IEEE 10th Workshop on Signal Processing Advances in Wireless Communications}, pages = {499--503}, publisher = {IEEE}, address = {Perugia}, abstract = {In previous literature on cooperative relay communications, the emphasis has been on the study of multi-hop networks. In this paper we address mesh wireless networks that use decode-and-forward relays for which we derive the optimal node decision rules in case of binary transmission. We also obtain the expression for the overall bit error probability. We compare the mesh networks with multi-hop networks and show the improvement in performance that can be achieved with them when both networks have the same number of nodes and equal number of hops.}, keywords = {binary transmission, bit error probability, Bit error rate, cooperative relay communications, decode-and-forward relays, Detectors, error statistics, Maximum likelihood decoding, maximum likelihood detection, Mesh networks, mesh wireless networks, multi-hop networks, Network topology, optimal node decision rules, Peer to peer computing, radio networks, Relays, spread spectrum communication, telecommunication network topology, Wireless Sensor Networks}, pubstate = {published}, tppubtype = {inproceedings} } In previous literature on cooperative relay communications, the emphasis has been on the study of multi-hop networks. In this paper we address mesh wireless networks that use decode-and-forward relays for which we derive the optimal node decision rules in case of binary transmission. We also obtain the expression for the overall bit error probability. We compare the mesh networks with multi-hop networks and show the improvement in performance that can be achieved with them when both networks have the same number of nodes and equal number of hops. |