188 research outputs found
A Scalable VLSI Architecture for Soft-Input Soft-Output Depth-First Sphere Decoding
Multiple-input multiple-output (MIMO) wireless transmission imposes huge
challenges on the design of efficient hardware architectures for iterative
receivers. A major challenge is soft-input soft-output (SISO) MIMO demapping,
often approached by sphere decoding (SD). In this paper, we introduce the - to
our best knowledge - first VLSI architecture for SISO SD applying a single
tree-search approach. Compared with a soft-output-only base architecture
similar to the one proposed by Studer et al. in IEEE J-SAC 2008, the
architectural modifications for soft input still allow a one-node-per-cycle
execution. For a 4x4 16-QAM system, the area increases by 57% and the operating
frequency degrades by 34% only.Comment: Accepted for IEEE Transactions on Circuits and Systems II Express
Briefs, May 2010. This draft from April 2010 will not be updated any more.
Please refer to IEEE Xplore for the final version. *) The final publication
will appear with the modified title "A Scalable VLSI Architecture for
Soft-Input Soft-Output Single Tree-Search Sphere Decoding
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
Architecture and Algorithm for a Stochastic Soft-output MIMO Detector
In this paper, we propose a novel architecture for a
soft-output stochastic detector in multiple-input, multiple-output
(MIMO) systems. The stochastic properties of this detector
are studied and derived in this work, and several complexity
reduction techniques are proposed to significantly reduce its
cost from an architecture-implementation perspective. We also
propose an efficient architecture to implement this detector.
Finally, this detector is incorporated into an iterative detectiondecoding
structure, and through simulations, it is shown that
the overall frame error rate (FER) performance and complexity
is of the same order as that of the conventional K-best sphere
detector.Nokia CorporationXilinx Inc.National Science Foundatio
A Robust Low-Complexity MIMO Detector for Rank 4 LTE/LTE-A Systems
This paper deals with MIMO detection for rank 4 LTE systems. The paper
revolves around a previously known detector [1, by Inkyu Lee, TCOM'2010] which
we shall refer to as RCSMLD
(Reduced-Constellation-Size-Maximum-Likelihood-Detector). However, a direct
application of the scheme in [1, by Inkyu Lee, TCOM'2010] to LTE/LTE-A rank 4
test cases results in unsatisfactory performance. The first contribution of the
paper is to introduce several modifications that can jointly be applied to the
basic RCSMLD scheme which, taken together, result in excellent performance. Our
second contribution is the development of a highly efficient hardware structure
for RCSMLD that allows for an implementation with very few multiplications.Comment: Accepted for publication in PIMRC-2014, Washington DC, US
Cooperative Partial Detection for MIMO Relay Networks
This paper was submitted by the author prior to final official version. For official version please see http://hdl.handle.net/1911/64372Cooperative communication has recently re-emerged as a possible paradigm shift to realize the promises of the ever increasing wireless communication market; how- ever, there have been few, if any, studies to translate theoretical results into feasi- ble schemes with their particular practical challenges. The multiple-input multiple- output (MIMO) technique is another method that has been recently employed in different standards and protocols, often as an optional scenario, to further improve the reliability and data rate of different wireless communication applications. In this work, we look into possible methods and algorithms for combining these two tech- niques to take advantage of the benefits of both.
In this thesis, we will consider methods that consider the limitations of practical solutions, which, to the best of our knowledge, are the first time to be considered in this context. We will present complexity reduction techniques for MIMO systems in cooperative systems. Furthermore, we will present architectures for flexible and configurable MIMO detectors. These architectures could support a range of data rates, modulation orders and numbers of antennas, and therefore, are crucial in the different nodes of cooperative systems. The breadth-first search employed in our realization presents a large opportunity to exploit the parallelism of the FPGA in order to achieve high data rates. Algorithmic modifications to address potential sequential bottlenecks in the traditional bread-first search-based SD are highlighted in the thesis.
We will present a novel Cooperative Partial Detection (CPD) approach in MIMO relay channels, where instead of applying the conventional full detection in the relay, the relay performs a partial detection and forwards the detected parts of the message to the destination. We will demonstrate how this approach leads to controlling the complexity in the relay and helping it choose how much it is willing to cooperate based on its available resources. We will discuss the complexity implications of this method, and more importantly, present hardware verification and over-the-air experimentation of CPD using the Wireless Open-access Research Platform (WARP).NSF grants EIA-0321266, CCF-0541363, CNS-0551692, CNS-0619767, EECS-0925942, and CNS-0923479, Nokia, Xilinx, Nokia Siemens Networks, Texas Instruments, and Azimuth Systems
Approximate inference in massive MIMO scenarios with moment matching techniques
Mención Internacional en el título 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 probabilística 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 desafíos tecnológicos que deben ser considerados. Uno de
ellos es la detección de los símbolos 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 líneas 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 serían
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 símbolos 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 símbolo 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 regímenes 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 típica 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 Capítulo 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 capítulo. En los Capítulos II y III el estado del arte y
nuestra propuesta para detección dura son presentados, mientras que en los
Capítulos IV y V se presentan para detección suave. Finalmente las conclusiones
que pueden obtenerse de esta Tesis y futuras líneas de investigación
son expuestas en el Capítulo VI.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Juan José Murillo Fuentes.- Secretario: Gonzalo Vázquez Vilar.- Vocal: María Isabel Valera Martíne
Low-Power and Error-Resilient VLSI Circuits and Systems.
Efficient low-power operation is critically important for the success of the next-generation signal processing applications. Device and supply voltage have been continuously scaled to meet a more constrained power envelope, but scaling has created resiliency challenges, including increasing timing faults and soft errors. Our research aims at designing low-power and robust circuits and systems for signal processing by drawing circuit, architecture, and algorithm approaches.
To gain an insight into the system faults due to supply voltage reduction, we researched the two primary effects that determine the minimum supply voltage (VMIN) in Intel’s tri-gate CMOS technology, namely process variations and gate-dielectric soft breakdown. We determined that voltage scaling increases the timing window that sequential circuits are vulnerable. Thus, we proposed a new hold-time violation metric to define hold-time VMIN, which has been adopted as a new design standard.
Device scaling increases soft errors which affect circuit reliability. Through extensive soft error characterization using two 65nm CMOS test chips, we studied the soft error mechanisms and its dependence on supply voltage and clock frequency. This study laid the foundation of the first 65nm DSP chip design for a NASA spaceflight project. To mitigate such random errors, we proposed a new confidence-driven architecture that effectively enhances the error resiliency of deeply scaled CMOS and post-CMOS circuits.
Designing low-power resilient systems can effectively leverage application-specific algorithmic approaches. To explore design opportunities in the algorithmic domain, we demonstrate an application-specific detection and decoding processor for multiple-input multiple-output (MIMO) wireless communication. To enhance the receive error rate for a robust wireless communication, we designed a joint detection and decoding technique by enclosing detection and decoding in an iterative loop to enhance both interference cancellation and error reduction. A proof-of-concept chip design was fabricated for the next-generation 4x4 256QAM MIMO systems. Through algorithm-architecture optimizations and low-power circuit techniques, our design achieves significant improvements in throughput, energy efficiency and error rate, paving the way for future developments in this area.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/110323/1/uchchen_1.pd
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