154 research outputs found
Turbo EP-based Equalization: a Filter-Type Implementation
This manuscript has been submitted to Transactions on Communications on
September 7, 2017; revised on January 10, 2018 and March 27, 2018; and accepted
on April 25, 2018
We propose a novel filter-type equalizer to improve the solution of the
linear minimum-mean squared-error (LMMSE) turbo equalizer, with computational
complexity constrained to be quadratic in the filter length. When high-order
modulations and/or large memory channels are used the optimal BCJR equalizer is
unavailable, due to its computational complexity. In this scenario, the
filter-type LMMSE turbo equalization exhibits a good performance compared to
other approximations. In this paper, we show that this solution can be
significantly improved by using expectation propagation (EP) in the estimation
of the a posteriori probabilities. First, it yields a more accurate estimation
of the extrinsic distribution to be sent to the channel decoder. Second,
compared to other solutions based on EP the computational complexity of the
proposed solution is constrained to be quadratic in the length of the finite
impulse response (FIR). In addition, we review previous EP-based turbo
equalization implementations. Instead of considering default uniform priors we
exploit the outputs of the decoder. Some simulation results are included to
show that this new EP-based filter remarkably outperforms the turbo approach of
previous versions of the EP algorithm and also improves the LMMSE solution,
with and without turbo equalization
Receiver Architectures for MIMO-OFDM Based on a Combined VMP-SP Algorithm
Iterative information processing, either based on heuristics or analytical
frameworks, has been shown to be a very powerful tool for the design of
efficient, yet feasible, wireless receiver architectures. Within this context,
algorithms performing message-passing on a probabilistic graph, such as the
sum-product (SP) and variational message passing (VMP) algorithms, have become
increasingly popular.
In this contribution, we apply a combined VMP-SP message-passing technique to
the design of receivers for MIMO-ODFM systems. The message-passing equations of
the combined scheme can be obtained from the equations of the stationary points
of a constrained region-based free energy approximation. When applied to a
MIMO-OFDM probabilistic model, we obtain a generic receiver architecture
performing iterative channel weight and noise precision estimation,
equalization and data decoding. We show that this generic scheme can be
particularized to a variety of different receiver structures, ranging from
high-performance iterative structures to low complexity receivers. This allows
for a flexible design of the signal processing specially tailored for the
requirements of each specific application. The numerical assessment of our
solutions, based on Monte Carlo simulations, corroborates the high performance
of the proposed algorithms and their superiority to heuristic approaches
Soft-Decision-Driven Channel Estimation for Pipelined Turbo Receivers
We consider channel estimation specific to turbo equalization for
multiple-input multiple-output (MIMO) wireless communication. We develop a
soft-decision-driven sequential algorithm geared to the pipelined turbo
equalizer architecture operating on orthogonal frequency division multiplexing
(OFDM) symbols. One interesting feature of the pipelined turbo equalizer is
that multiple soft-decisions become available at various processing stages. A
tricky issue is that these multiple decisions from different pipeline stages
have varying levels of reliability. This paper establishes an effective
strategy for the channel estimator to track the target channel, while dealing
with observation sets with different qualities. The resulting algorithm is
basically a linear sequential estimation algorithm and, as such, is
Kalman-based in nature. The main difference here, however, is that the proposed
algorithm employs puncturing on observation samples to effectively deal with
the inherent correlation among the multiple demapper/decoder module outputs
that cannot easily be removed by the traditional innovations approach. The
proposed algorithm continuously monitors the quality of the feedback decisions
and incorporates it in the channel estimation process. The proposed channel
estimation scheme shows clear performance advantages relative to existing
channel estimation techniques.Comment: 11 pages; IEEE Transactions on Communications 201
An Iterative Receiver for OFDM With Sparsity-Based Parametric Channel Estimation
In this work we design a receiver that iteratively passes soft information
between the channel estimation and data decoding stages. The receiver
incorporates sparsity-based parametric channel estimation. State-of-the-art
sparsity-based iterative receivers simplify the channel estimation problem by
restricting the multipath delays to a grid. Our receiver does not impose such a
restriction. As a result it does not suffer from the leakage effect, which
destroys sparsity. Communication at near capacity rates in high SNR requires a
large modulation order. Due to the close proximity of modulation symbols in
such systems, the grid-based approximation is of insufficient accuracy. We show
numerically that a state-of-the-art iterative receiver with grid-based sparse
channel estimation exhibits a bit-error-rate floor in the high SNR regime. On
the contrary, our receiver performs very close to the perfect channel state
information bound for all SNR values. We also demonstrate both theoretically
and numerically that parametric channel estimation works well in dense
channels, i.e., when the number of multipath components is large and each
individual component cannot be resolved.Comment: Major revision, accepted for IEEE Transactions on Signal Processin
Expectation propagation as a solution for digital communication systems.
In the context of digital communications, a digital receiver is required to provide an estimation of the transmitted symbols. Nowadays channel decoders highly benefit from soft (probabilistic) estimates for each transmitted symbol rather than from hard decisions. For this reason, digital receivers must be designed to provide the probability that each possible symbol was transmitted based on the received corrupted signal. Since exact inference might be unfeasible in terms of complexity for high-order scenarios, it is necessary to resort to approximate inference, such as the linear minimum mean square error (LMMSE) criterion. The LMMSE approximates the discrete prior information of the transmitted symbols with a Gaussian distribution, which causes a degradation in its performance. In this thesis, an alternative approximate statistical technique is applied to the design of a digital
probabilistic receiver in digital communications. Specifically, the expectation
propagation (EP) algorithm is investigated to find the Gaussian posterior probability density function (pdf) that minimizes the Kullback-Leibler (KL) divergence with respect to the true posterior pdf. Two different communication system scenarios are studied: a single-input singleoutput (SISO) digital communication system with memory channel and a multipleinput multiple-output (MIMO) system with memoryless channel. In the SISO scenario, three different designs of a soft standalone and turbo equalizer based on the EP algorithm are developed: the block or batch approach, the filter-type
version that emulates theWiener filter behavior and the smoothing equalizer which proceeds similarly to a Kalman smoother. Finally, the block EP implementation is also adapted to MIMO scenarios with feedback from the decoder. In both scenarios, the EP is applied iteratively, including a damping mechanism and a control to avoid negative values of variances, which would lead to instabilities (specially for high-order constellations). Experimental results included through the thesis show that the EP algorithm applied to communication systems greatly improves the performance of previous approaches found in the literature with a complexity slightly increased but still proportional to that of the LMMSE. These results also show the robustness of the algorithm even for high-order modulations, large memory channels and high number of antennas. Major contributions of this dissertation have been published in four journal (one of them is still under review) and two conference papers. One more paper will be submitted to a journal soon. All these papers are listed below:
• Irene Santos, Juan José Murillo-Fuentes, Rafael Boloix-Tortosa, Eva Arias
de Reyna and Pablo M. Olmos, "Expectation Propagation as Turbo Equalizer
in ISI Channels," IEEE Transactions on Communications, vol. 65, no.1, pp.
360-370, Jan 2017.
• Irene Santos, Juan José Murillo-Fuentes, Eva Arias de Reyna and Pablo M.
Olmos, "Turbo EP-based Equalization: a Filter-Type Implementation," IEEE
Transactions on Communications, Sep 2017, Accepted. [Online] Available:
https://ieeexplore.ieee.org/document/8353388/
• Irene Santos, Juan José Murillo-Fuentes, Eva Arias-de-Reyna and Pablo M.
Olmos, "Probabilistic Equalization With a Smoothing Expectation Propagation
Approach," IEEE Transactions on Wireless Communications, vol. 16,
no. 5, pp. 2950-2962, May 2017.
• Irene Santos, Juan José Murillo-Fuentes and Eva Arias-de-Reyna, "Equalization
with Expectation Propagation at Smoothing Level," To be submitted.
[Online] Available: https://arxiv.org/abs/1809.00806
• Irene Santos and Juan José Murillo-Fuentes, "EP-based turbo detection for
MIMO receivers and large-scale systems," IEEE Transactions on Vehicular
Technology, May 2018, Under review. [Online] Available:
https://arxiv.org/abs/1805.05065
• Irene Santos, Juan José Murillo-Fuentes, and Pablo M. Olmos, "Block
expectation propagation equalization for ISI channels," 23rd European Signal
Processing Conference (EUSIPCO 2015), Nice, 2015, pp. 379-383.
• Irene Santos, and Juan José Murillo-Fuentes, "Improved probabilistic EPbased receiver for MIMO systems and high-order modulations," XXXIII
Simposium Nacional de la Unión CientÃfica Internacional de Radio (URSI
2018), Granada, 2018.En el ámbito de las comunicaciones digitales, es necesario un receptor digital que proporcione una estimación de los sÃmbolos transmitidos. Los decodificadores de canal actuales se benefician enormemente de estimaciones suaves (probabilÃsticas) de cada sÃmbolo transmitido, en vez de estimaciones duras. Por este motivo, los receptores digitales deben diseñarse para proporcionar la probabilidad de cada posible sÃmbolo que fue transmitido en base a la señal recibida y corrupta. Dado que la inferencia exacta puede no ser posible en términos de complejidad para escenarios de alto orden, es necesario recurrir a inferencia aproximada, como por
ejemplo el criterio de linear minimum-mean-square-error (LMMSE). El LMMSE aproxima la información a priori discreta de los sÃmbolos transmitidos con una distribución Gaussiana, lo cual provoca una degradación en su resultado. En esta tesis, se aplica una técnica alternativa de inferencia estadÃstica para diseñar un receptor digital probabilÃstico de comunicaciones digitales. En concreto, se investiga el algoritmo expectation propagation (EP) con el objetivo de encontrar la función densidad de probabilidad (pdf) a posteriori Gaussiana que minimiza la divergencia de Kullback-Leibler (KL) con respecto a la pdf a posteriori verdadera. Se estudian dos escenarios de comunicaciones digitales diferentes: un sistema de comunicaciones single-input single-output (SISO) con canales con memoria y un sistema multiple-input multiple-output (MIMO) con canales sin memoria. Para el
escenario SISO se proponen tres diseños diferentes para un igualador probabilÃstico, tanto simple como turbo, que está basado en el algoritmo EP: una versión bloque, una versión filtrada que emula el comportamiento de un filtroWiener y una versión smoothing que funciona de forma similar a un Kalman smoother. Finalmente, la implementación del EP en bloque se adapta también para escenarios MIMO con realimentación desde el decodificador. En ambos escenarios, el EP se aplica de forma iterativa, incluyendo un mecanismo de damping y un control para evitar valores de varianzas negativas, que darÃan lugar a inestabilidades (especialmente, en
constelaciones de alto orden). Los resultados experimentales que se incluyen en la tesis muestran que, cuando el algoritmo EP se aplica a sistemas de comunicaciones, se mejora notablemente el resultado de otras propuestas anteriores que existen en la literatura, con un pequeño incremento de la complejidad que es proporcional a la carga del LMMSE. Estos resultados también demuestran la robustez del algoritmo incluso para modulaciones de alto orden, canales con bastante memoria y un gran número de antenas.
Las principales contribuciones de esta tesis se han publicado en cuatro artÃculos de revista (uno de ellos todavÃa bajo revisión) y dos artÃculos de conferencia. Otro artÃculo adicional se encuentra en preparación y se enviarÃa próximamente a una revista. Estos se citan a continuación:
• Irene Santos, Juan José Murillo-Fuentes, Rafael Boloix-Tortosa, Eva Arias
de Reyna and Pablo M. Olmos, "Expectation Propagation as Turbo Equalizer
in ISI Channels," IEEE Transactions on Communications, vol. 65, no.1, pp.
360-370, Jan 2017.
• Irene Santos, Juan José Murillo-Fuentes, Eva Arias de Reyna and Pablo
M. Olmos, "Turbo EP-based Equalization: a Filter-Type Implementation,"
IEEE Transactions on Communications, Sep 2017, Aceptado. [Online]
Disponible: https://ieeexplore.ieee.org/document/8353388/
• Irene Santos, Juan José Murillo-Fuentes, Eva Arias-de-Reyna and Pablo M.
Olmos, "Probabilistic Equalization With a Smoothing Expectation Propagation
Approach," IEEE Transactions on Wireless Communications, vol. 16,
no. 5, pp. 2950-2962, May 2017.
• Irene Santos, Juan José Murillo-Fuentes and Eva Arias-de-Reyna, "Equalization with Expectation Propagation at Smoothing Level," En preparación. [Online] Disponible: https://arxiv.org/abs/1809.00806
• Irene Santos and Juan José Murillo-Fuentes, "EP-based turbo detection for
MIMO receivers and large-scale systems," IEEE Transactions on Vehicular
Technology, May 2018, En revisión. [Online] Disponible: https://arxiv.org/abs/1805.05065
• Irene Santos, Juan José Murillo-Fuentes, and Pablo M. Olmos, "Block
expectation propagation equalization for ISI channels," 23rd European Signal
Processing Conference (EUSIPCO 2015), Nice, 2015, pp. 379-383.
• Irene Santos, and Juan José Murillo-Fuentes, "Improved probabilistic EPbased receiver for MIMO systems and high-order modulations," XXXIII Simposium Nacional de la Unión CientÃfica Internacional de Radio (URSI
2018), Granada, 2018
Frequency-Domain Turbo Equalization for MIMO Underwater Acoustic Communications
This paper investigates a low-complexity frequency-domain turbo equalization (FDTE) based on linear minimum mean square error (LMMSE) criterion for single-carrier (SC) multiple-input multiple-output (MIMO) underwater acoustic communications (UAC). The receiver incorporates both the equalizer and the decoder which exchange the extrinsic information on the coded bits for each other to implement the iterative detection. The channel impulse responses (CIRs) required in the equalization are estimated in the frequency domain (FD) by inserting the well-designed pilot blocks which are frequency-orthogonal Chu sequences. The proposed SC-MIMO-FDTE architecture is applied to the fixed-to-fixed underwater data gathered during SPACE08 ocean experiments in October 2008, where multiple transducers and hydrophones are deployed in communication ranges of 200m and 1000m, and the channel bandwidth is 9.765625 kHz. The phase shift keying (PSK) signals are transmitted from multiple transducers in various block sizes. The proposed transceiver has been demonstrated to improve the bit-error-rate (BER) performance significantly by processing the QPSK data blocks with block length of 1024 in 200m and 1000m ranges. The average BERs obtained by turbo detection with 3 iterations can achieve approximately 1.4 × 10-4 for the 200m system and 4.4 × 10-5 for the 1000m system
IST-2000-30148 I-METRA: D3.2 Implementation of relevant algorithms
This deliverable provides a high level description of the software developed within the I-METRA project following the selection reported in D3.1 "Design, Analysis and Selection of Suitable Algorithms".Preprin
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