492 research outputs found
Digital Signal Processing Research Program
Contains table of contents for Section 2, an introduction, reports on twenty-two research projects and a list of publications.Sanders, a Lockheed-Martin Corporation Contract BZ4962U.S. Army Research Laboratory Contract DAAL01-96-2-0001U.S. Navy - Office of Naval Research Grant N00014-93-1-0686National Science Foundation Grant MIP 95-02885U.S. Navy - Office of Naval Research Grant N00014-96-1-0930National Defense Science and Engineering FellowshipU.S. Air Force - Office of Scientific Research Grant F49620-96-1-0072U.S. Navy - Office of Naval Research Grant N00014-95-1-0362National Science Foundation Graduate Research FellowshipAT&T Bell Laboratories Graduate Research FellowshipU.S. Army Research Laboratory Contract DAAL01-96-2-0002National Science Foundation Graduate FellowshipU.S. Army Research Laboratory/Advanced Sensors Federated Lab Program Contract DAAL01-96-2-000
Transmit Diversity Code Filter Sets (TDCFSs), an MISO Antenna Frequency Predistortion Scheme for ATSC 3.0
"(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.")Transmit diversity code filter sets (TDCFSs) are a method of predistorting the common waveforms from multiple transmitters in the same frequency channel, as in a single frequency network, in order to minimize the possibility of cross-interference among the transmitted signals over the entire reception area. This processing is achieved using all-pass linear filters, allowing the resulting combination of predistortion and multipath to be properly compensated as part of the equalization process in the receiver. The filter design utilizes an iterative computational approach, which minimizes cross-correlation peak side lobe under the constraints of number of transmitters and delay spread, allowing customization for specific network configurations. This paper provides an overview of the TDCFS multiple-input single output antenna scheme adopted in ATSC 3.0, together with experimental analysis of capacity and specific worst-case conditions that illustrate the benefits of using the TDCFS approach.Lopresto, S.; Citta, R.; Vargas, D.; Gómez Barquero, D. (2016). Transmit Diversity Code Filter Sets (TDCFSs), an MISO Antenna Frequency Predistortion Scheme for ATSC 3.0. IEEE Transactions on Broadcasting. 62(1):271-280. doi:10.1109/TBC.2015.2505400S27128062
Sparse-DFT and WHT Precoding with Iterative Detection for Highly Frequency-Selective Channels
Various precoders have been recently studied by the wireless community to
combat the channel fading effects. Two prominent precoders are implemented with
the discrete Fourier transform (DFT) and Walsh-Hadamard transform (WHT). The
WHT precoder is implemented with less complexity since it does not need complex
multiplications. Also, spreading can be applied sparsely to decrease the
transceiver complexity, leading to sparse DFT (SDFT) and sparse Walsh-Hadamard
(SWH). Another relevant topic is the design of iterative receivers that deal
with inter-symbol-interference (ISI). In particular, many detectors based on
expectation propagation (EP) have been proposed recently for channels with high
levels of ISI. An alternative is the maximum a-posterior (MAP) detector,
although it leads to unfeasible high complexity in many cases. In this paper,
we provide a relatively low-complexity \textcolor{black}{computation} of the
MAP detector for the SWH. We also propose two \textcolor{black}{feasible
methods} based on the Log-MAP and Max-Log-MAP. Additionally, the DFT, SDFT and
SWH precoders are compared using an EP-based receiver with one-tap FD
equalization. Lastly, SWH-Max-Log-MAP is compared to the (S)DFT with EP-based
receiver in terms of performance and complexity. The results show that the
proposed SWH-Max-Log-MAP has a better performance and complexity trade-off for
QPSK and 16-QAM under highly selective channels, but has unfeasible complexity
for higher QAM orders
Hybrid solutions to instantaneous MIMO blind separation and decoding: narrowband, QAM and square cases
Future wireless communication systems are desired to support high data rates and high quality transmission when considering the growing multimedia applications. Increasing the channel throughput leads to the multiple input and multiple output and blind equalization techniques in recent years. Thereby blind MIMO equalization has attracted a great interest.Both system performance and computational complexities play important roles in real time communications. Reducing the computational load and providing accurate performances are the main challenges in present systems. In this thesis, a hybrid method which can provide an affordable complexity with good performance for Blind Equalization in large constellation MIMO systems is proposed first. Saving computational cost happens both in the signal sep- aration part and in signal detection part. First, based on Quadrature amplitude modulation signal characteristics, an efficient and simple nonlinear function for the Independent Compo- nent Analysis is introduced. Second, using the idea of the sphere decoding, we choose the soft information of channels in a sphere, and overcome the so- called curse of dimensionality of the Expectation Maximization (EM) algorithm and enhance the final results simultaneously. Mathematically, we demonstrate in the digital communication cases, the EM algorithm shows Newton -like convergence.Despite the widespread use of forward -error coding (FEC), most multiple input multiple output (MIMO) blind channel estimation techniques ignore its presence, and instead make the sim- plifying assumption that the transmitted symbols are uncoded. However, FEC induces code structure in the transmitted sequence that can be exploited to improve blind MIMO channel estimates. In final part of this work, we exploit the iterative channel estimation and decoding performance for blind MIMO equalization. Experiments show the improvements achievable by exploiting the existence of coding structures and that it can access the performance of a BCJR equalizer with perfect channel information in a reasonable SNR range. All results are confirmed experimentally for the example of blind equalization in block fading MIMO systems
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
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
An efficient multi-resolution framework for high quality interactive rendering of massive point clouds using multi-way kd-trees
We present an efficient technique for out-of-core multi-resolution construction and high quality interactive visualization of massive point clouds. Our approach introduces a novel hierarchical level of detail (LOD) organization based on multi-way kd-trees, which simplifies memory management and allows control over the LOD-tree height. The LOD tree, constructed bottom up using a fast high-quality point simplification method, is fully balanced and contains all uniformly sized nodes. To this end, we introduce and analyze three efficient point simplification approaches that yield a desired number of high-quality output points. For constant rendering performance, we propose an efficient rendering-on-a-budget method with asynchronous data loading, which delivers fully continuous high quality rendering through LOD geo-morphing and deferred blending. Our algorithm is incorporated in a full end-to-end rendering system, which supports both local rendering and cluster-parallel distributed rendering. The method is evaluated on complex models made of hundreds of millions of point sample
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