Adaptive Baseband Pro cessing and Configurable Hardware for Wireless Communication

The world of information is literally at one’s fingertips, allowing access to previously unimaginable amounts of data, thanks to advances in wireless communication. The growing demand for high speed data has necessitated theuse of wider bandwidths, and wireless technologies such as Multiple-InputMultiple-Output (MIMO) have been adopted to increase spectral efficiency.These advanced communication technologies require sophisticated signal processing, often leading to higher power consumption and reduced battery life.Therefore, increasing energy efficiency of baseband hardware for MIMO signal processing has become extremely vital. High Quality of Service (QoS)requirements invariably lead to a larger number of computations and a higherpower dissipation. However, recognizing the dynamic nature of the wirelesscommunication medium in which only some channel scenarios require complexsignal processing, and that not all situations call for high data rates, allowsthe use of an adaptive channel aware signal processing strategy to provide adesired QoS. Information such as interference conditions, coherence bandwidthand Signal to Noise Ratio (SNR) can be used to reduce algorithmic computations in favorable channels. Hardware circuits which run these algorithmsneed flexibility and easy reconfigurability to switch between multiple designsfor different parameters. These parameters can be used to tune the operations of different components in a receiver based on feedback from the digitalbaseband. This dissertation focuses on the optimization of digital basebandcircuitry of receivers which use feedback to trade power and performance. Aco-optimization approach, where designs are optimized starting from the algorithmic stage through the hardware architectural stage to the final circuitimplementation is adopted to realize energy efficient digital baseband hardwarefor mobile 4G devices. These concepts are also extended to the next generation5G systems where the energy efficiency of the base station is improved.This work includes six papers that examine digital circuits in MIMO wireless receivers. Several key blocks in these receiver include analog circuits thathave residual non-linearities, leading to signal intermodulation and distortion.Paper-I introduces a digital technique to detect such non-linearities and calibrate analog circuits to improve signal quality. The concept of a digital nonlinearity tuning system developed in Paper-I is implemented and demonstratedin hardware. The performance of this implementation is tested with an analogchannel select filter, and results are presented in Paper-II. MIMO systems suchas the ones used in 4G, may employ QR Decomposition (QRD) processors tosimplify the implementation of tree search based signal detectors. However,the small form factor of the mobile device increases spatial correlation, whichis detrimental to signal multiplexing. Consequently, a QRD processor capableof handling high spatial correlation is presented in Paper-III. The algorithm and hardware implementation are optimized for carrier aggregation, which increases requirements on signal processing throughput, leading to higher powerdissipation. Paper-IV presents a method to perform channel-aware processingwith a simple interpolation strategy to adaptively reduce QRD computationcount. Channel properties such as coherence bandwidth and SNR are used toreduce multiplications by 40% to 80%. These concepts are extended to usetime domain correlation properties, and a full QRD processor for 4G systemsfabricated in 28 nm FD-SOI technology is presented in Paper-V. The designis implemented with a configurable architecture and measurements show thatcircuit tuning results in a highly energy efficient processor, requiring 0.2 nJ to1.3 nJ for each QRD. Finally, these adaptive channel-aware signal processingconcepts are examined in the scope of the next generation of communicationsystems. Massive MIMO systems increase spectral efficiency by using a largenumber of antennas at the base station. Consequently, the signal processingat the base station has a high computational count. Paper-VI presents a configurable detection scheme which reduces this complexity by using techniquessuch as selective user detection and interpolation based signal processing. Hardware is optimized for resource sharing, resulting in a highly reconfigurable andenergy efficient uplink signal detector

Deep learning-based space-time coding wireless MIMO receiver optimization.

Doctoral Degree. University of KwaZulu-Natal, Durban.With the high demand for high data throughput and reliable wireless links to cater for real-time or low latency mobile application services, the wireless research community has developed wireless multiple-input multiple-output (MIMO) architectures that cater to these stringent quality of service (QoS) requirements. For the case of wireless link reliability, spatial diversity in wireless MIMO architectures is used to increase the link reliability. Besides increasing link reliability using spatial diversity, space-time block coding schemes may be used to further increase the wireless link reliability by adding time diversity to the wireless link. Our research is centered around the optimization of resources used in decoding space-time block coded wireless signals. There are two categories of space-time block coding schemes namely the orthogonal and non-orthogonal space-time block codes (STBC). In our research, we concentrate on two non-orthogonal STBC schemes namely the uncoded space-time labeling diversity (USTLD) and the Golden code. These two non-orthogonal STBC schemes exhibit some advantages over the orthogonal STBC called Alamouti despite their non-linear optimal detection. Orthogonal STBC schemes have the advantage of simple linear optimal detection relative to the more complex non-linear optimal detection of non-orthogonal STBC schemes. Since our research concentrates on wireless MIMO STBC transmission, for detection to occur optimally at the receiver side of a space-time block coded wireless MIMO link, we need to optimally perform channel estimation and decoding. USTLD has a coding gain advantage over the Alamouti STBC scheme. This implies that the USTLD can deliver higher wireless link reliability relative to the Alamouti STBC for the same spectral efficiency. Despite this advantage of the USTLD, to the best of our knowledge, the literature has concentrated on USTLD wireless transmission under the assumption that the wireless receiver has full knowledge of the wireless channel without estimation errors. We thus perform research of the USTLD wireless MIMO transmission with imperfect channel estimation. The traditional least-squares (LS) and minimum mean squared error (MMSE) used in literature, for imperfect pilot-assisted channel estimation, require the full knowledge of the transmitted pilot symbols and/or wireless channel second order statistics which may not always be fully known. We, therefore, propose blind channel estimation facilitated by a deep learning model that makes it unnecessary to have prior knowledge of the wireless channel second order statistics, transmitted pilot symbols and/or average noise power. We also derive an optimal number of pilot symbols that maybe used for USTLD wireless MIMO channel estimation without compromising the wireless link reliability. It is shown from the Monte Carlo simulations that the error rate performance of the USTLD transmission is not compromised despite using only 20% of the required number of Zadoff-Chu sequence pilot symbols used by the traditional LS and MMSE channel estimators for both 16-QAM and 16-PSK baseband modulation. The Golden code is a STBC scheme with spatial multiplexing gain over the Alamouti scheme. This implies that the Golden code can deliver higher spectral efficiencies for the same link reliability with the Alamouti scheme. The Alamouti scheme has been implemented in the modern wireless standards because it adds time diversity, with low decoding complexity, to wireless MIMO links. The Golden code adds time diversity and improves wireless MIMO spectral efficiency but at the cost of much higher decoding complexity relative to the Alamouti scheme. Because of the high decoding complexity, the Golden code is not widely adopted in the modern wireless standards. We, therefore, propose analytical and deep learning-based sphere-decoding algorithms to lower the number of detection floating-point operations (FLOPS) and decoding latency of the Golden code under low- and high-density M-ary quadrature amplitude modulation (M-QAM) baseband transmissions whilst maintaining the near-optimal error rate performance. The proposed sphere-decoding algorithms achieve at most 99% reduction in Golden code detection FLOPS, at low SNR, relative to the sphere-decoder with sorted detection subsets (SD-SDS) whilst maintaining the error rate performance. For the case of high-density M-QAM Golden code transmission, the proposed analytical and deep learning sphere-decoders reduce decoding latency by at most 70%, relative to the SD-SDS decoder, without diminishing the error rate performance

Low-Rank Channel Estimation for Millimeter Wave and Terahertz Hybrid MIMO Systems

Massive multiple-input multiple-output (MIMO) is one of the fundamental technologies for 5G and beyond. The increased number of antenna elements at both the transmitter and the receiver translates into a large-dimension channel matrix. In addition, the power requirements for the massive MIMO systems are high, especially when fully digital transceivers are deployed. To address this challenge, hybrid analog-digital transceivers are considered a viable alternative. However, for hybrid systems, the number of observations during each channel use is reduced. The high dimensions of the channel matrix and the reduced number of observations make the channel estimation task challenging. Thus, channel estimation may require increased training overhead and higher computational complexity. The need for high data rates is increasing rapidly, forcing a shift of wireless communication towards higher frequency bands such as millimeter Wave (mmWave) and terahertz (THz). The wireless channel at these bands is comprised of only a few dominant paths. This makes the channel sparse in the angular domain and the resulting channel matrix has a low rank. This thesis aims to provide channel estimation solutions benefiting from the low rankness and sparse nature of the channel. The motivation behind this thesis is to offer a desirable trade-off between training overhead and computational complexity while providing a desirable estimate of the channel

FlexCore: Massively Parallel and Flexible Processing for Large MIMO Access Points

Large MIMO base stations remain among wireless network designers’ best tools for increasing wireless throughput while serving many clients, but current system designs, sacrifice throughput with simple linear MIMO detection algorithms. Higher-performance detection techniques are known, but remain off the table because these systems parallelize their computation at the level of a whole OFDM subcarrier, sufficing only for the less demanding linear detection approaches they opt for. This paper presents FlexCore, the first computational architecture capable of parallelizing the detection of large numbers of mutually-interfering information streams at a granularity below individual OFDM subcarriers, in a nearly-embarrassingly parallel manner while utilizing any number of available processing elements. For 12 clients sending 64-QAM symbols to a 12-antenna base station, our WARP testbed evaluation shows similar network throughput to the state-of-the-art while using an order of magnitude fewer processing elements. For the same scenario, our combined WARP-GPU testbed evaluation demonstrates a 19x computational speedup, with 97% increased energy efficiency when compared with the state of the art. Finally, for the same scenario, an FPGA-based comparison between FlexCore and the state of the art shows that FlexCore can achieve up to 96% better energy efficiency, and can offer up to 32x the processing throughput

Scalable System Design for Covert MIMO Communications

In modern communication systems, bandwidth is a limited commodity. Bandwidth efficient systems are needed to meet the demands of the ever-increasing amount of data that users share. Of particular interest is the U.S. Military, where high-resolution pictures and video are used and shared. In these environments, covert communications are necessary while still providing high data rates. The promise of multi-antenna systems providing higher data rates has been shown on a small scale, but limitations in hardware prevent large systems from being implemented

Estimation and detection techniques for doubly-selective channels in wireless communications

A fundamental problem in communications is the estimation of the channel. The signal transmitted through a communications channel undergoes distortions so that it is often received in an unrecognizable form at the receiver. The receiver must expend significant signal processing effort in order to be able to decode the transmit signal from this received signal. This signal processing requires knowledge of how the channel distorts the transmit signal, i.e. channel knowledge. To maintain a reliable link, the channel must be estimated and tracked by the receiver. The estimation of the channel at the receiver often proceeds by transmission of a signal called the 'pilot' which is known a priori to the receiver. The receiver forms its estimate of the transmitted signal based on how this known signal is distorted by the channel, i.e. it estimates the channel from the received signal and the pilot. This design of the pilot is a function of the modulation, the type of training and the channel. [Continues.

Multiple Signal Classification Based Joint Communication and Sensing System

Joint communication and sensing (JCS) has become a promising technology for mobile networks because of its higher spectrum and energy efficiency. Up to now, the prevalent fast Fourier transform (FFT)-based sensing method for mobile JCS networks is on-grid based, and the grid interval determines the resolution. Because the mobile network usually has limited consecutive OFDM symbols in a downlink (DL) time slot, the sensing accuracy is restricted by the limited resolution, especially for velocity estimation. In this paper, we propose a multiple signal classification (MUSIC)-based JCS system that can achieve higher sensing accuracy for the angle of arrival, range, and velocity estimation, compared with the traditional FFT-based JCS method. We further propose a JCS channel state information (CSI) enhancement method by leveraging the JCS sensing results. Finally, we derive a theoretical lower bound for sensing mean square error (MSE) by using perturbation analysis. Simulation results show that in terms of the sensing MSE performance, the proposed MUSIC-based JCS outperforms the FFT-based one by more than 20 dB. Moreover, the bit error rate (BER) of communication demodulation using the proposed JCS CSI enhancement method is significantly reduced compared with communication using the originally estimated CSI.Comment: 30 pages, 10 figures, major revision to IEEE Transactions on Wireless Communication

MIMOPack: A High Performance Computing Library for MIMO Communication Systems

[EN] Nowadays, several communication standards are emerging and evolving, searching higher transmission rates, reliability and coverage. This expansion is primarily driven by the continued increase in consumption of mobile multimedia services due to the emergence of new handheld devices such as smartphones and tablets. One of the most significant techniques employed to meet these demands is the use of multiple transmit and receive antennas, known as MIMO systems. The use of this technology allows to increase the transmission rate and the quality of the transmission through the use of multiple antennas at the transmitter and receiver sides. MIMO technologies have become an essential key in several wireless standards such as WLAN, WiMAX and LTE. These technologies will be incorporated also in future standards, therefore is expected in the coming years a great deal of research in this field. Clearly, the study of MIMO systems is critical in the current investigation, however the problems that arise from this technology are very complex. High Performance Computing (HPC) systems, and specifically, modern hardware architectures as multi-core and many-cores (e.g Graphics Processing Units (GPU)) are playing a key role in the development of efficient and low-complexity algorithms for MIMO transmissions. Proof of this is that the number of scientific contributions and research projects related to its use has increased in the last years. Also, some high performance libraries have been implemented as tools for researchers involved in the development of future communication standards. Two of the most popular libraries are: IT++ that is a library based on the use of some optimized libraries for multi-core processors and the Communications System Toolbox designed for use with MATLAB, which uses GPU computing. However, there is not a library able to run on a heterogeneous platform using all the available resources. In view of the high computational requirements in MIMO application research and the shortage of tools able to satisfy them, we have made a special effort to develop a library to ease the development of adaptable parallel applications in accordance with the different architectures of the executing platform. The library, called MIMOPack, aims to implement efficiently using parallel computing, a set of functions to perform some of the critical stages of MIMO communication systems simulation. The main contribution of the thesis is the implementation of efficient Hard and Soft output detectors, since the detection stage is considered the most complex part of the communication process. These detectors are highly configurable and many of them include preprocessing techniques that reduce the computational cost and increase the performance. The proposed library shows three important features: portability, efficiency and easy of use. Current realease allows GPUs and multi-core computation, or even simultaneously, since it is designed to use on heterogeneous machines. The interface of the functions are common to all environments in order to simplify the use of the library. Moreover, some of the functions are callable from MATLAB increasing the portability of developed codes between different computing environments. According to the library design and the performance assessment, we consider that MIMOPack may facilitate industrial and academic researchers the implementation of scientific codes without having to know different programming languages and machine architectures. This will allow to include more complex algorithms in their simulations and obtain their results faster. This is particularly important in the industry, since the manufacturers work to analyze and to propose their own technologies with the aim that it will be approved as a standard. Thus allowing to enforce their intellectual property rights over their competitors, who should obtain the corresponding licenses to include these technologies into their products.[ES] En la actualidad varios estándares de comunicación están surgiendo buscando velocidades de transmisión más altas y mayor fiabilidad. Esta expansión está impulsada por el aumento en el consumo de servicios multimedia debido a la aparición de nuevos dispositivos como los smartphones y las tabletas. Una de las técnicas empleadas más importantes es el uso de múltiples antenas de transmisión y recepción, conocida como sistemas MIMO, que permite aumentar la velocidad y la calidad de la transmisión. Las tecnologías MIMO se han convertido en una parte esencial en diferentes estándares tales como WLAN, WiMAX y LTE. Estas tecnologías se incorporarán también en futuros estándares, por lo tanto, se espera en los próximos años una gran cantidad de investigación en este campo. Está claro que el estudio de los sistemas MIMO es crítico en la investigación actual, sin embargo los problemas que surgen de esta tecnología son muy complejos. La sistemas de computación de alto rendimiento, y en concreto, las arquitecturas hardware actuales como multi-core y many-core (p. ej. GPUs) están jugando un papel clave en el desarrollo de algoritmos eficientes y de baja complejidad en las transmisiones MIMO. Prueba de ello es que el número de contribuciones científicas y proyectos de investigación relacionados con su uso se han incrementado en el últimos años. Algunas librerías de alto rendimiento se están utilizando como herramientas por investigadores en el desarrollo de futuros estándares. Dos de las librerías más destacadas son: IT++ que se basa en el uso de distintas librerías optimizadas para procesadores multi-core y el paquete Communications System Toolbox diseñada para su uso con MATLAB, que utiliza computación con GPU. Sin embargo, no hay una biblioteca capaz de ejecutarse en una plataforma heterogénea. En vista de los altos requisitos computacionales en la investigación MIMO y la escasez de herramientas capaces de satisfacerlos, hemos implementado una librería que facilita el desarrollo de aplicaciones paralelas adaptables de acuerdo con las diferentes arquitecturas de la plataforma de ejecución. La librería, llamada MIMOPack, implementa de manera eficiente un conjunto de funciones para llevar a cabo algunas de las etapas críticas en la simulación de un sistema de comunicación MIMO. La principal aportación de la tesis es la implementación de detectores eficientes de salida Hard y Soft, ya que la etapa de detección es considerada la parte más compleja en el proceso de comunicación. Estos detectores son altamente configurables y muchos de ellos incluyen técnicas de preprocesamiento que reducen el coste computacional y aumentan el rendimiento. La librería propuesta tiene tres características importantes: la portabilidad, la eficiencia y facilidad de uso. La versión actual permite computación en GPU y multi-core, incluso simultáneamente, ya que está diseñada para ser utilizada sobre plataformas heterogéneas que explotan toda la capacidad computacional. Para facilitar el uso de la biblioteca, las interfaces de las funciones son comunes para todas las arquitecturas. Algunas de las funciones se pueden llamar desde MATLAB aumentando la portabilidad de códigos desarrollados entre los diferentes entornos. De acuerdo con el diseño de la biblioteca y la evaluación del rendimiento, consideramos que MIMOPack puede facilitar la implementación de códigos sin tener que saber programar con diferentes lenguajes y arquitecturas. MIMOPack permitirá incluir algoritmos más complejos en las simulaciones y obtener los resultados más rápidamente. Esto es particularmente importante en la industria, ya que los fabricantes trabajan para proponer sus propias tecnologías lo antes posible con el objetivo de que sean aprobadas como un estándar. De este modo, los fabricantes pueden hacer valer sus derechos de propiedad intelectual frente a sus competidores, quienes luego deben obtener las correspon[CA] En l'actualitat diversos estàndards de comunicació estan sorgint i evolucionant cercant velocitats de transmissió més altes i major fiabilitat. Aquesta expansió, està impulsada pel continu augment en el consum de serveis multimèdia a causa de l'aparició de nous dispositius portàtils com els smartphones i les tablets. Una de les tècniques més importants és l'ús de múltiples antenes de transmissió i recepció (MIMO) que permet augmentar la velocitat de transmissió i la qualitat de transmissió. Les tecnologies MIMO s'han convertit en una part essencial en diferents estàndards inalàmbrics, tals com WLAN, WiMAX i LTE. Aquestes tecnologies s'incorporaran també en futurs estàndards, per tant, s'espera en els pròxims anys una gran quantitat d'investigació en aquest camp. L'estudi dels sistemes MIMO és crític en la recerca actual, no obstant açó, els problemes que sorgeixen d'aquesta tecnologia són molt complexos. Els sistemes de computació d'alt rendiment com els multi-core i many-core (p. ej. GPUs)), estan jugant un paper clau en el desenvolupament d'algoritmes eficients i de baixa complexitat en les transmissions MIMO. Prova d'açò és que el nombre de contribucions científiques i projectes d'investigació relacionats amb el seu ús s'han incrementat en els últims anys. Algunes llibreries d'alt rendiment estan utilitzant-se com a eines per investigadors involucrats en el desenvolupament de futurs estàndards. Dos de les llibreries més destacades són: IT++ que és una llibreria basada en lús de diferents llibreries optimitzades per a processadors multi-core i el paquet Communications System Toolbox dissenyat per al seu ús amb MATLAB, que utilitza computació amb GPU. No obstant açò, no hi ha una biblioteca capaç d'executar-se en una plataforma heterogènia. Degut als alts requisits computacionals en la investigació MIMO i l'escacès d'eines capaces de satisfer-los, hem implementat una llibreria que facilita el desenvolupament d'aplicacions paral·leles adaptables d'acord amb les diferentes arquitectures de la plataforma d'ejecució. La llibreria, anomenada MIMOPack, implementa de manera eficient, un conjunt de funcions per dur a terme algunes de les etapes crítiques en la simulació d'un sistema de comunicació MIMO. La principal aportació de la tesi és la implementació de detectors eficients d'exida Hard i Soft, ja que l'etapa de detecció és considerada la part més complexa en el procés de comunicació. Estos detectors són altament configurables i molts d'ells inclouen tècniques de preprocessament que redueixen el cost computacional i augmenten el rendiment. La llibreria proposta té tres característiques importants: la portabilitat, l'eficiència i la facilitat d'ús. La versió actual permet computació en GPU i multi-core, fins i tot simultàniament, ja que està dissenyada per a ser utilitzada sobre plataformes heterogènies que exploten tota la capacitat computacional. Amb el fi de simplificar l'ús de la biblioteca, les interfaces de les funcions són comunes per a totes les arquitectures. Algunes de les funcions poden ser utilitzades des de MATLAB augmentant la portabilitat de còdics desenvolupats entre els diferentes entorns. D'acord amb el disseny de la biblioteca i l'evaluació del rendiment, considerem que MIMOPack pot facilitar la implementació de còdics a investigadors sense haver de saber programar amb diferents llenguatges i arquitectures. MIMOPack permetrà incloure algoritmes més complexos en les seues simulacions i obtindre els seus resultats més ràpid. Açò és particularment important en la industria, ja que els fabricants treballen per a proposar les seues pròpies tecnologies el més prompte possible amb l'objectiu que siguen aprovades com un estàndard. D'aquesta menera, els fabricants podran fer valdre els seus drets de propietat intel·lectual enfront dels seus competidors, els qui després han d'obtenir les corresponents llicències si voleRamiro Sánchez, C. (2015). MIMOPack: A High Performance Computing Library for MIMO Communication Systems [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/53930TESISPremios Extraordinarios de tesis doctorale

Low complexity MIMO detection algorithms and implementations

University of Minnesota Ph.D. dissertation. December 2014. Major: Electrical Engineering. Advisor: Gerald E. Sobelman. 1 computer file (PDF); ix, 111 pages.MIMO techniques use multiple antennas at both the transmitter and receiver sides to achieve diversity gain, multiplexing gain, or both. One of the key challenges in exploiting the potential of MIMO systems is to design high-throughput, low-complexity detection algorithms while achieving near-optimal performance. In this thesis, we design and optimize algorithms for MIMO detection and investigate the associated performance and FPGA implementation aspects.First, we study and optimize a detection algorithm developed by Shabany and Gulak for a K-Best based high throughput and low energy hard output MIMO detection and expand it to the complex domain. The new method uses simple lookup tables, and it is fully scalable for a wide range of K-values and constellation sizes. This technique reduces the computational complexity, without sacrificing performance and the complexity scales only sub-linearly with the constellation size. Second, we apply the bidirectional technique to trellis search and propose a high performance soft output bidirectional path preserving trellis search (PPTS) detector for MIMO systems. The comparative error analysis between single direction and bidirectional PPTS detectors is given. We demonstrate that the bidirectional PPTS detector can minimize the detection error. Next, we design a novel bidirectional processing algorithm for soft-output MIMO systems. It combines features from several types of fixed complexity tree search procedures. The proposed approach achieves a higher performance than previously proposed algorithms and has a comparable computational cost. Moreover, its parallel nature and fixed throughput characteristics make it attractive for very large scale integration (VLSI) implementation.Following that, we present a novel low-complexity hard output MIMO detection algorithm for LTE and WiFi applications. We provide a well-defined tradeoff between computational complexity and performance. The proposed algorithm uses a much smaller number of Euclidean distance (ED) calculations while attaining only a 0.5dB loss compared to maximum likelihood detection (MLD). A 3x3 MIMO system with a 16QAM detector architecture is designed, and the latency and hardware costs are estimated.Finally, we present a stochastic computing implementation of trigonometric and hyperbolic functions which can be used for QR decomposition and other wireless communications and signal processing applications