183 research outputs found
Superposition Signal Input Decoding for Lattice Reduction-Aided MIMO Receivers
This paper proposes a novel approach to low complexity soft input decoding for lattice reduction-aided MIMO receivers. The proposed approach feeds a soft input decoder with soft signals made from hard decision signals generated by using a lattice reduction-aided linear detector. The soft signal is a weighted-sum of some candidate vectors that are near by the hard decision signal coming out from the lattice reduction-aided linear detector. This paper proposes a technique to adjust the weight adapt to the channel for the higher transmission performance. Furthermore, we propose to introduce a coefficient that is used for the weights in order to enhance the transmission performance. The transmission performance is evaluated in a 4×4 MIMO channel. When a linear MMSE filter or a serial interference canceller is used as the linear detector, the proposed technique achieves about 1.0dB better transmission performance at the BER of 10-5 than the decoder fed with the hard decision signals. In addition, the low computational complexity of the proposed technique is quantitatively evaluated
Lattice sampling algorithms for communications
In this thesis, we investigate the problem of decoding for wireless communications from the perspective of lattice sampling. In particular, computationally efficient lattice sampling algorithms are exploited to enhance the system performance, which enjoys the system tradeoff between performance and complexity through the sample size. Based on this idea, several novel lattice sampling algorithms are presented in this thesis. First of all, in order to address the inherent issues in the random sampling, derandomized sampling algorithm is proposed. Specifically, by setting a probability threshold to sample candidates, the whole sampling procedure becomes deterministic, leading to considerable performance improvement and complexity reduction over to the randomized sampling. According to the analysis and optimization, the correct decoding radius is given with the optimized parameter setting. Moreover, the upper bound on the sample size, which corresponds to near-maximum likelihood (ML) performance, is also derived. After that, the proposed derandomized sampling algorithm is introduced into the soft-output decoding of MIMO bit-interleaved coded
modulation (BICM) systems to further improve the decoding performance. According to the demonstration, we show that the derandomized sampling algorithm is able to achieve the near-maximum a posteriori (MAP) performance in the soft-output decoding. We then extend the well-known Markov Chain Monte Carlo methods into the
samplings from lattice Gaussian distribution, which has emerged as a common theme in lattice coding and decoding, cryptography, mathematics. We firstly show that the statistical Gibbs sampling is capable to perform the lattice Gaussian sampling. Then, a more efficient algorithm referred to as Gibbs-Klein sampling is proposed, which samples multiple variables block by block using Klein’s algorithm. After that, for the sake of convergence rate, we introduce the conventional statistical Metropolis-Hastings (MH) sampling into lattice Gaussian distributions and three MH-based sampling algorithms are then proposed. The first one, named as MH multivariate sampling algorithm, is demonstrated to have a faster convergence rate than Gibbs-Klein sampling. Next, the symmetrical distribution generated by Klein’s algorithm is taken as the proposal distribution, which offers an efficient way to perform the Metropolis sampling over high-dimensional models. Finally, the independent
Metropolis-Hastings-Klein (MHK) algorithm is proposed, where the Markov chain arising from it is proved to converge to the stationary distribution exponentially fast. Furthermore, its convergence rate can be explicitly calculated in terms of the theta series, making it possible to predict the exact mixing time of the underlying Markov chain.Open Acces
Approximate inference in massive MIMO scenarios with moment matching techniques
Mención Internacional en el tÃtulo de doctorThis Thesis explores low-complexity inference probabilistic algorithms in
high-dimensional Multiple-Input Multiple-Output (MIMO) systems and high order
M-Quadrature Amplitude Modulation (QAM) constellations. Several
modern communications systems are using more and more antennas to maximize
spectral efficiency, in a new phenomena call Massive MIMO. However,
as the number of antennas and/or the order of the constellation grow several
technical issues have to be tackled, one of them is that the symbol
detection complexity grows fast exponentially with the system dimension.
Nowadays the design of massive MIMO low-complexity receivers is one important
research line in MIMO because symbol detection can no longer rely
on conventional approaches such as Maximum a Posteriori (MAP) due to
its exponential computation complexity. This Thesis proposes two main results.
On one hand a hard decision low-complexity MIMO detector based on
Expectation Propagation (EP) algorithm which allows to iteratively approximate
within polynomial cost the posterior distribution of the transmitted
symbols. The receiver is named Expectation Propagation Detector (EPD)
and its solution evolves from Minimum Mean Square Error (MMSE) solution
and keeps per iteration the MMSE complexity which is dominated by
a matrix inversion. Hard decision Symbol Error Rate (SER) performance is
shown to remarkably improve state-of-the-art solutions of similar complexity.
On the other hand, a soft-inference algorithm, more suitable to modern
communication systems with channel codification techniques such as Low-
Density Parity-Check (LDPC) codes, is also presented. Modern channel
decoding techniques need as input Log-Likehood Ratio (LLR) information
for each coded bit. In order to obtain that information, firstly a soft bit
inference procedure must be performed. In low-dimensional scenarios, this
can be done by marginalization over the symbol posterior distribution. However,
this is not feasible at high-dimension. While EPD could provide this
probabilistic information, it is shown that its probabilistic estimates are in
general poor in the low Signal-to-Noise Ratio (SNR) regime. In order to
solve this inconvenience a new algorithm based on the Expectation Consistency
(EC) algorithm, which generalizes several algorithms such as Belief.
Propagation (BP) and EP itself, was proposed. The proposed algorithm
called Expectation Consistency Detector (ECD) maps the inference problem
as an optimization over a non convex function. This new approach
allows to find stationary points and tradeoffs between accuracy and convergence,
which leads to robust update rules. At the same complexity cost than
EPD, the new proposal achieves a performance closer to channel capacity at
moderate SNR. The result reveals that the probabilistic detection accuracy
has a relevant impact in the achievable rate of the overall system. Finally,
a modified ECD algorithm is presented, with a Turbo receiver structure
where the output of the decoder is fed back to ECD, achieving performance
gains in all block lengths simulated.
The document is structured as follows. In Chapter I an introduction
to the MIMO scenario is presented, the advantages and challenges are exposed
and the two main scenarios of this Thesis are set forth. Finally, the
motivation behind this work, and the contributions are revealed. In Chapters
II and III the state of the art and our proposal are presented for Hard
Detection, whereas in Chapters IV and V are exposed for Soft Inference Detection.
Eventually, a conclusion and future lines can be found in Chapter
VI.Esta Tesis aborda algoritmos de baja complejidad para la estimación probabilÃstica en sistemas de Multiple-Input Multiple-Output (MIMO) de grandes
dimensiones con constelaciones M-Quadrature Amplitude Modulation (QAM)
de alta dimensionalidad. Son diversos los sistemas de comunicaciones que en
la actualidad están utilizando más y más antenas para maximizar la eficiencia
espectral, en un nuevo fenómeno denominado Massive MIMO. Sin embargo
los incrementos en el número de antenas y/o orden de la constelación
presentan ciertos desafÃos tecnológicos que deben ser considerados. Uno de
ellos es la detección de los sÃmbolos transmitidos en el sistema debido a que
la complejidad aumenta más rápido que las dimensiones del sistema. Por
tanto el diseño receptores para sistemas Massive MIMO de baja complejidad
es una de las importantes lÃneas de investigación en la actualidad en
MIMO, debido principalmente a que los métodos tradicionales no se pueden
implementar en sistemas con decenas de antenas, cuando lo deseable serÃan
centenas, debido a que su coste es exponencial.
Los principales resultados en esta Tesis pueden clasificarse en dos. En
primer lugar un receptor MIMO para decisión dura de baja complejidad
basado en el algoritmo Expectation Propagation (EP) que permite de manera
iterativa, con un coste computacional polinómico por iteración, aproximar
la distribución a posteriori de los sÃmbolos transmitidos. El algoritmo,
denominado Expectation Propagation Detector (EPD), es inicializado con
la solución del algoritmo Minimum Mean Square Error (MMSE) y mantiene
el coste de este para todas las iteraciones, dominado por una inversión de
matriz. El rendimiento del decisor en probabilidad de error de sÃmbolo muestra
ganancias remarcables con respecto a otros métodos en la literatura con
una complejidad similar. En segundo lugar, un algoritmo que provee una
estimación blanda, información que es más apropiada para los actuales sistemas
de comunicaciones que utilizan codificación de canal, como pueden
ser códigos Low-Density Parity-Check (LDPC). La información necesaria
para estos decodificadores de canal es Log-Likehood Ratio (LLR) para cada
uno de los bits codificados.
En escenarios de bajas dimensiones se pueden calcular las marginales de la distribución a posteriori, pero en escenarios de grandes dimensiones
no es viable, aunque EPD puede proporcionar este tipo de información a la
entrada del decodificador, dicha información no es la mejor al estar el algoritmo
pensado para detección dura, sobre todo se observa este fenómeno en
el rango de baja Signal-to-Noise Ratio (SNR). Para solucionar este problema
se propone un nuevo algoritmo basado en Expectation Consistency
(EC) que engloba diversos algoritmos como pueden ser Belief Propagation
(BP) y el algoritmo EP propuesto con anterioridad. El nuevo algoritmo
llamado Expectation Consistency Detector (ECD), trata el problema como
una optimización de una función no convexa. Esta aproximación permite
encontrar los puntos estacionarios y la relación entre precisión y convergencia,
que permitirán reglas de actualización más robustas y eficaces. Con
la misma compleja que el algoritmo propuesto inicialmente, ECD permite
rendimientos más próximos a la capacidad del canal en regÃmenes moderados
de SNR. Los resultados muestran que la precisión tiene un gran efecto
en la tasa que alcanza el sistema. Finalmente una versión modificada de
ECD es propuesta en una arquitectura tÃpica de los Turbo receptores, en
la que la salida del decodificador es la entrada del receptor, y que permite
ganancias en el rendimiento en todas las longitudes de código simuladas.
El presente documento está estructurado de la siguiente manera. En el
primer CapÃtulo I, se realiza una introducción a los sistemas MIMO, presentando
sus ventajas, desventajas, problemas abiertos. Los modelos que se
utilizaran en la tesis y la motivación con la que se inició esta tesis son expuestos
en este primer capÃtulo. En los CapÃtulos II y III el estado del arte y
nuestra propuesta para detección dura son presentados, mientras que en los
CapÃtulos IV y V se presentan para detección suave. Finalmente las conclusiones
que pueden obtenerse de esta Tesis y futuras lÃneas de investigación
son expuestas en el CapÃtulo VI.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Juan José Murillo Fuentes.- Secretario: Gonzalo Vázquez Vilar.- Vocal: MarÃa Isabel Valera MartÃne
Doctor of Philosophy
dissertationMultiple-input and multiple-output (MIMO) technique has emerged as a key feature for future generations of wireless communication systems. It increases the channel capacity proportionate to the minimum number of transmit and receive antennas. This dissertation addresses the receiver design for high-rate MIMO communications in at fading environments. The emphasis of the thesis is on the cases where channel state information (CSI) is not available and thus, clever channel estimation algorithms have to be developed to bene t from the maximum available channel capacity. The thesis makes four distinct novel contributions. First, we note that the conventional MCMC-MIMO detector presented in the prior work may deteriorate as SNR increases. We suggest and show through computer simulations that this problem to a great extent can be solved by initializing the MCMC detector with regulated states which are found through linear detectors. We also introduce the novel concept of staged-MCMC in a turbo receiver, where we start the detection process at a lower complexity and increase complexity only if the data could not be correctly detected in the present stage of data detection. Second, we note that in high-rate MIMO communications, joint data detection and channel estimation poses new challenges when a turbo loop is used to improve the quality of the estimated channel and the detected data. Erroneous detected data may propagate in the turbo loop and, thus, degrade the performance of the receiver signi cantly. This is referred to as error propagation. We propose a novel receiver that decorrelates channel estimation and the detected data to avoid the detrimental e ect of error propagation. Third, the dissertation studies joint channel estimation and MIMO detection over a continuously time-varying channel and proposes a new dual-layer channel estimator to overcome the complexity of optimal channel estimators. The proposed dual-layer channel estimator reduces the complexity of the MIMO detector with optimal channel estimator by an order of magnitude at a cost of a negligible performance degradation, on the order of 0.1 to 0.2 dB. The fourth contribution of this dissertation is to note that the Wiener ltering techniques that are discussed in this dissertation and elsewhere in the literature assume that channel (time-varying) statistics are available. We propose a new method that estimates such statistics using the coarse channel estimates obtained through pilot symbols. The dissertation also makes an additional contribution revealing di erences between the MCMC-MIMO and LMMSE-MIMO detectors. We nd that under the realistic condition where CSI has to be estimated, hence the available channel estimate will be noisy, the MCMC-MIMO detector outperforms the LMMSE-MIMO detector with a signi cant margin
Doctor of Philosophy
dissertationThe continuous growth of wireless communication use has largely exhausted the limited spectrum available. Methods to improve spectral efficiency are in high demand and will continue to be for the foreseeable future. Several technologies have the potential to make large improvements to spectral efficiency and the total capacity of networks including massive multiple-input multiple-output (MIMO), cognitive radio, and spatial-multiplexing MIMO. Of these, spatial-multiplexing MIMO has the largest near-term potential as it has already been adopted in the WiFi, WiMAX, and LTE standards. Although transmitting independent MIMO streams is cheap and easy, with a mere linear increase in cost with streams, receiving MIMO is difficult since the optimal methods have exponentially increasing cost and power consumption. Suboptimal MIMO detectors such as K-Best have a drastically reduced complexity compared to optimal methods but still have an undesirable exponentially increasing cost with data-rate. The Markov Chain Monte Carlo (MCMC) detector has been proposed as a near-optimal method with polynomial cost, but it has a history of unusual performance issues which have hindered its adoption. In this dissertation, we introduce a revised derivation of the bitwise MCMC MIMO detector. The new approach resolves the previously reported high SNR stalling problem of MCMC without the need for hybridization with another detector method or adding heuristic temperature scaling terms. Another common problem with MCMC algorithms is an unknown convergence time making predictable fixed-length implementations problematic. When an insufficient number of iterations is used on a slowly converging example, the output LLRs can be unstable and overconfident, therefore, we develop a method to identify rare, slowly converging runs and mitigate their degrading effects on the soft-output information. This improves forward-error-correcting code performance and removes a symptomatic error floor in bit-error-rates. Next, pseudo-convergence is identified with a novel way to visualize the internal behavior of the Gibbs sampler. An effective and efficient pseudo-convergence detection and escape strategy is suggested. Finally, the new excited MCMC (X-MCMC) detector is shown to have near maximum-a-posteriori (MAP) performance even with challenging, realistic, highly-correlated channels at the maximum MIMO sizes and modulation rates supported by the 802.11ac WiFi specification, 8x8 256 QAM. Further, the new excited MCMC (X-MCMC) detector is demonstrated on an 8-antenna MIMO testbed with the 802.11ac WiFi protocol, confirming its high performance. Finally, a VLSI implementation of the X-MCMC detector is presented which retains the near-optimal performance of the floating-point algorithm while having one of the lowest complexities found in the near-optimal MIMO detector literature
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