129 research outputs found
Sparsity Enhanced Decision Feedback Equalization
For single-carrier systems with frequency domain equalization, decision
feedback equalization (DFE) performs better than linear equalization and has
much lower computational complexity than sequence maximum likelihood detection.
The main challenge in DFE is the feedback symbol selection rule. In this paper,
we give a theoretical framework for a simple, sparsity based thresholding
algorithm. We feed back multiple symbols in each iteration, so the algorithm
converges fast and has a low computational cost. We show how the initial
solution can be obtained via convex relaxation instead of linear equalization,
and illustrate the impact that the choice of the initial solution has on the
bit error rate performance of our algorithm. The algorithm is applicable in
several existing wireless communication systems (SC-FDMA, MC-CDMA, MIMO-OFDM).
Numerical results illustrate significant performance improvement in terms of
bit error rate compared to the MMSE solution
Reduced Complexity Sphere Decoding
In Multiple-Input Multiple-Output (MIMO) systems, Sphere Decoding (SD) can
achieve performance equivalent to full search Maximum Likelihood (ML) decoding,
with reduced complexity. Several researchers reported techniques that reduce
the complexity of SD further. In this paper, a new technique is introduced
which decreases the computational complexity of SD substantially, without
sacrificing performance. The reduction is accomplished by deconstructing the
decoding metric to decrease the number of computations and exploiting the
structure of a lattice representation. Furthermore, an application of SD,
employing a proposed smart implementation with very low computational
complexity is introduced. This application calculates the soft bit metrics of a
bit-interleaved convolutional-coded MIMO system in an efficient manner. Based
on the reduced complexity SD, the proposed smart implementation employs the
initial radius acquired by Zero-Forcing Decision Feedback Equalization (ZF-DFE)
which ensures no empty spheres. Other than that, a technique of a particular
data structure is also incorporated to efficiently reduce the number of
executions carried out by SD. Simulation results show that these approaches
achieve substantial gains in terms of the computational complexity for both
uncoded and coded MIMO systems.Comment: accepted to Journal. arXiv admin note: substantial text overlap with
arXiv:1009.351
Design and Implementation of Efficient Algorithms for Wireless MIMO Communication Systems
En la Ăşltima dĂ©cada, uno de los avances tecnolĂłgicos más importantes que han hecho culminar la nueva generaciĂłn de banda ancha inalámbrica es la comunicaciĂłn mediante sistemas de mĂşltiples entradas y mĂşltiples salidas (MIMO). Las tecnologĂas MIMO han sido adoptadas por muchos estándares inalámbricos tales como LTE, WiMAS y WLAN. Esto se debe principalmente a su capacidad de aumentar la máxima velocidad de transmisiĂłn , junto con la fiabilidad alcanzada y la cobertura de las comunicaciones inalámbricas actuales sin la necesidad de ancho de banda extra ni de potencia de transmisiĂłn adicional. Sin embargo, las ventajas proporcionadas por los sistemas MIMO se producen a expensas de un aumento sustancial del coste de implementaciĂłn de mĂşltiples antenas y de la complejidad del receptor, la cual tiene un gran impacto sobre el consumo de energĂa. Por esta razĂłn, el diseño de receptores de baja complejidad es un tema importante que se abordará a lo largo de esta tesis.
En primer lugar, se investiga el uso de tĂ©cnicas de preprocesado de la matriz de canal MIMO bien para disminuir el coste computacional de decodificadores Ăłptimos o bien para mejorar las prestaciones de detectores subĂłptimos lineales, SIC o de bĂşsqueda en árbol. Se presenta una descripciĂłn detallada de dos tĂ©cnicas de preprocesado ampliamente utilizadas: el mĂ©todo de Lenstra, Lenstra, Lovasz (LLL) para lattice reduction (LR) y el algorimo VBLAST ZF-DFE. Tanto la complejidad como las prestaciones de ambos mĂ©todos se han evaluado y comparado entre sĂ. Además, se propone una implementaciĂłn de bajo coste del algoritmo VBLAST ZF-DFE, la cual se incluye en la evaluaciĂłn.
En segundo lugar, se ha desarrollado un detector MIMO basado en búsqueda en árbol de baja complejidad, denominado detector K-Best de amplitud variable (VB K-Best). La idea principal de este método es aprovechar el impacto del número de condición de la matriz de canal sobre la detección de datos con el fin de disminuir la complejidad de los sistemasRoger Varea, S. (2012). Design and Implementation of Efficient Algorithms for Wireless MIMO Communication Systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/16562Palanci
Generalized feedback detection for spatial multiplexing multi-antenna systems
We present a unified detection framework for spatial multiplexing multiple-input multiple-output (MIMO) systems by generalizing Heller’s classical feedback decoding algorithm for convolutional codes. The resulting generalized feedback detector (GFD) is characterized by three parameters: window size, step size and branch factor. Many existing MIMO detectors are turned out to be special cases of the GFD. Moreover, different parameter choices can provide various performance-complexity tradeoffs. The connection between MIMO detectors and tree search algorithms is also established. To reduce redundant computations in the GFD, a shared computation technique is proposed by using a tree data structure. Using a union bound based analysis of the symbol error rates, the diversity order and signal-to-noise ratio (SNR) gain are derived analytically as functions of the three parameters; for example, the diversity order of the GFD varies between 1 and N. The complexity of the GFD varies between those of the maximum-likelihood (ML) detector and the zero-forcing decision feedback detector (ZFDFD). Extensive computer simulation results are also provided
Wireless receiver designs: from information theory to VLSI implementation
Receiver design, especially equalizer design, in communications is a major concern in both academia and industry. It is a problem with both theoretical challenges and severe implementation hurdles. While much research has been focused on reducing complexity for optimal or near-optimal schemes, it is still common practice in industry to use simple techniques (such as linear equalization) that are generally significantly inferior. Although digital signal processing (DSP) technologies have been applied to wireless communications to enhance the throughput, the users' demands for more data and higher rate have revealed new
challenges. For example, to collect the diversity and combat fading channels, in addition to the transmitter designs that enable the diversity, we also require the receiver to be able to collect the prepared diversity.
Most wireless transmissions can be modeled as a linear block transmission system. Given a linear block transmission model assumption, maximum likelihood equalizers (MLEs) or near-ML decoders have been adopted at the receiver to collect diversity which is an important metric for performance, but these decoders exhibit high complexity. To reduce the decoding complexity, low-complexity equalizers, such as linear equalizers (LEs) and
decision feedback equalizers (DFEs) are often adopted. These methods, however, may not utilize the diversity enabled by the transmitter and as a result have degraded performance compared to
MLEs.
In this dissertation, we will present efficient receiver designs that achieve low bit-error-rate (BER), high mutual information, and low decoding complexity. Our approach is
to first investigate the error performance and mutual information of existing low-complexity equalizers to reveal the fundamental condition to achieve full diversity with LEs. We show that the fundamental condition for LEs to collect the same (outage) diversity as MLE is that the channels need to be constrained within a certain distance from orthogonality. The orthogonality deficiency (od) is adopted to quantify the distance of channels to orthogonality while other existing metrics are also introduced and compared. To meet the fundamental condition and achieve full diversity, a hybrid equalizer framework is proposed. The performance-complexity trade-off of hybrid equalizers is quantified by deriving the distribution of od.
Another approach is to apply lattice reduction (LR) techniques to improve the ``quality' of channel matrices. We present two widely adopted LR methods in wireless communications, the Lenstra-Lenstra-Lovasz (LLL) algorithm [51] and Seysen's algorithm (SA), by providing detailed descriptions and pseudo codes. The properties of output matrices of the LLL algorithm and SA are also quantified. Furthermore, other LR algorithms are also briefly introduced.
After introducing LR algorithms, we show how to adopt them into the wireless communication decoding process by presenting LR-aided hard-output detectors and LR-aided soft-output detectors for coded systems, respectively. We also analyze the performance of proposed efficient receivers from the perspective of diversity, mutual information, and complexity. We prove that LR techniques help to restore the diversity of low-complexity equalizers without increasing the complexity significantly.
When it comes to practical systems and simulation tool, e.g., MATLAB, only finite bits are adopted to represent numbers. Therefore, we revisit the diversity analysis for finite-bit represented systems. We illustrate that the diversity of MLE for systems with finite-bit representation is determined by the number of non-vanishing eigenvalues. It is also shown that although theoretically LR-aided detectors collect the same diversity as MLE in the real/complex field, it may show different diversity orders when finite-bit representation exists. Finally, the VLSI implementation of the complex LLL algorithms is provided to verify the practicality of our proposed designs.Ph.D.Committee Chair: Ma, Xiaoli; Committee Member: Anderson, David; Committee Member: Barry, John; Committee Member: Chen, Xu-Yan; Committee Member: Kornegay, Kevi
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