Low-Complexity Algorithms for Channel Estimation in Optimised Pilot-Assisted Wireless OFDM Systems

Orthogonal frequency division multiplexing (OFDM) has recently become a dominant transmission technology considered for the next generation fixed and mobile broadband wireless communication systems. OFDM has an advantage of lessening the severe effects of the frequency-selective (multipath) fading due to the band splitting into relatively flat fading subchannels, and allows for low-complexity transceiver implementation based on the fast Fourier transform algorithms. Combining OFDM modulation with multilevel frequency-domain symbol mapping (e.g., QAM) and spatial multiplexing (SM) over the multiple-input multiple-output (MIMO) channels, can theoretically achieve near Shannon capacity of the communication link. However, the high-rate and spectrumefficient system implementation requires coherent detection at the receiving end that is possible only when accurate channel state information (CSI) is available. Since in practice, the response of the wireless channel is unknown and is subject to random variation with time, the receiver typically employs a channel estimator for CSI acquisition. The channel response information retrieved by the estimator is then used by the data detector and can also be fed back to the transmitter by means of in-band or out-of-band signalling, so the latter could adapt power loading, modulation and coding parameters according to the channel conditions. Thus, design of an accurate and robust channel estimator is a crucial requirement for reliable communication through the channel, which is selective in time and frequency. In a MIMO configuration, a separate channel estimator has to be associated with each transmit/receive antenna pair, making the estimation algorithm complexity a primary concern. Pilot-assisted methods, relying on the insertion of reference symbols in certain frequencies and time slots, have been found attractive for identification of the doubly-selective radio channels from both the complexity and performance standpoint. In this dissertation, a family of the reduced-complexity estimators for the single and multiple-antenna OFDM systems is developed. The estimators are based on the transform-domain processing and have the same order of computational complexity, irrespective of the number of pilot subcarriers and their positioning. The common estimator structure represents a cascade of successive small-dimension filtering modules. The number of modules, as well as their order inside the cascade, is determined by the class of the estimator (one or two-dimensional) and availability of the channel statistics (correlation and signal-to-noise power ratio). For fine precision estimation in the multipath channels with statistics not known a priori, we propose recursive design of the filtering modules. Simulation results show that in the steady state, performance of the recursive estimators approaches that of their theoretical counterparts, which are optimal in the minimum mean square error (MMSE) sense. In contrast to the majority of the channel estimators developed so far, our modular-type architectures are suitable for the reconfigurable OFDM transceivers where the actual channel conditions influence the decision of what class of filtering algorithm to use, and how to allot pilot subcarrier positions in the band. In the pilot-assisted transmissions, channel estimation and detection are performed separately from each other over the distinct subcarrier sets. The estimator output is used only to construct the detector transform, but not as the detector input. Since performance of both channel estimation and detection depends on the signal-to-noise power vi ratio (SNR) at the corresponding subcarriers, there is a dilemma of the optimal power allocation between the data and the pilot symbols as these are conflicting requirements under the total transmit power constraint. The problem is exacerbated by the variety of channel estimators. Each kind of estimation algorithm is characterised by its own SNR gain, which in general can vary depending on the channel correlation. In this dissertation, we optimise pilot-data power allocation for the case of developed low-complexity one and two-dimensional MMSE channel estimators. The resultant contribution is manifested by the closed-form analytical expressions of the upper bound (suboptimal approximate value) on the optimal pilot-to-data power ratio (PDR) as a function of a number of design parameters (number of subcarriers, number of pilots, number of transmit antennas, effective order of the channel model, maximum Doppler shift, SNR, etc.). The resultant PDR equations can be applied to the MIMO-OFDM systems with arbitrary arrangement of the pilot subcarriers, operating in an arbitrary multipath fading channel. These properties and relatively simple functional representation of the derived analytical PDR expressions are designated to alleviate the challenging task of on-the-fly optimisation of the adaptive SM-MIMO-OFDM system, which is capable of adjusting transmit signal configuration (e.g., block length, number of pilot subcarriers or antennas) according to the established channel conditions

Review of Recent Trends

This work was partially supported by the European Regional Development Fund (FEDER), through the Regional Operational Programme of Centre (CENTRO 2020) of the Portugal 2020 framework, through projects SOCA (CENTRO-01-0145-FEDER-000010) and ORCIP (CENTRO-01-0145-FEDER-022141). Fernando P. Guiomar acknowledges a fellowship from “la Caixa” Foundation (ID100010434), code LCF/BQ/PR20/11770015. Houda Harkat acknowledges the financial support of the Programmatic Financing of the CTS R&D Unit (UIDP/00066/2020).MIMO-OFDM is a key technology and a strong candidate for 5G telecommunication systems. In the literature, there is no convenient survey study that rounds up all the necessary points to be investigated concerning such systems. The current deeper review paper inspects and interprets the state of the art and addresses several research axes related to MIMO-OFDM systems. Two topics have received special attention: MIMO waveforms and MIMO-OFDM channel estimation. The existing MIMO hardware and software innovations, in addition to the MIMO-OFDM equalization techniques, are discussed concisely. In the literature, only a few authors have discussed the MIMO channel estimation and modeling problems for a variety of MIMO systems. However, to the best of our knowledge, there has been until now no review paper specifically discussing the recent works concerning channel estimation and the equalization process for MIMO-OFDM systems. Hence, the current work focuses on analyzing the recently used algorithms in the field, which could be a rich reference for researchers. Moreover, some research perspectives are identified.publishersversionpublishe

REAL-TIME ADAPTIVE PULSE COMPRESSION ON RECONFIGURABLE, SYSTEM-ON-CHIP (SOC) PLATFORMS

New radar applications need to perform complex algorithms and process a large quantity of data to generate useful information for the users. This situation has motivated the search for better processing solutions that include low-power high-performance processors, efficient algorithms, and high-speed interfaces. In this work, hardware implementation of adaptive pulse compression algorithms for real-time transceiver optimization is presented, and is based on a System-on-Chip architecture for reconfigurable hardware devices. This study also evaluates the performance of dedicated coprocessors as hardware accelerator units to speed up and improve the computation of computing-intensive tasks such matrix multiplication and matrix inversion, which are essential units to solve the covariance matrix. The tradeoffs between latency and hardware utilization are also presented. Moreover, the system architecture takes advantage of the embedded processor, which is interconnected with the logic resources through high-performance buses, to perform floating-point operations, control the processing blocks, and communicate with an external PC through a customized software interface. The overall system functionality is demonstrated and tested for real-time operations using a Ku-band testbed together with a low-cost channel emulator for different types of waveforms

Contribution to dimensionality reduction of digital predistorter behavioral models for RF power amplifier linearization

The power efficiency and linearity of radio frequency (RF) power amplifiers (PAs) are critical in wireless communication systems. The main scope of PA designers is to build the RF PAs capable to maintain high efficiency and linearity figures simultaneously. However, these figures are inherently conflicted to each other and system-level solutions based on linearization techniques are required. Digital predistortion (DPD) linearization has become the most widely used solution to mitigate the efficiency versus linearity trade-off. The dimensionality of the DPD model depends on the complexity of the system. It increases significantly in high efficient amplification architectures when considering current wideband and spectrally efficient technologies. Overparametrization may lead to an ill-conditioned least squares (LS) estimation of the DPD coefficients, which is usually solved by employing regularization techniques. However, in order to both reduce the computational complexity and avoid ill-conditioning problems derived from overparametrization, several efforts have been dedicated to investigate dimensionality reduction techniques to reduce the order of the DPD model. This dissertation contributes to the dimensionality reduction of DPD linearizers for RF PAs with emphasis on the identification and adaptation subsystem. In particular, several dynamic model order reduction approaches based on feature extraction techniques are proposed. Thus, the minimum number of relevant DPD coefficients are dynamically selected and estimated in the DPD adaptation subsystem. The number of DPD coefficients is reduced, ensuring a well-conditioned LS estimation while demanding minimum hardware resources. The presented dynamic linearization approaches are evaluated and compared through experimental validation with an envelope tracking PA and a class-J PA The experimental results show similar linearization performance than the conventional LS solution but at lower computational cost.La eficiencia energetica y la linealidad de los amplificadores de potencia (PA) de radiofrecuencia (RF) son fundamentales en los sistemas de comunicacion inalambrica. El principal objetivo a alcanzar en el diserio de amplificadores de radiofrecuencia es lograr simultaneamente elevadas cifras de eficiencia y de linealidad. Sin embargo, estas cifras estan inherentemente en conflicto entre si, y se requieren soluciones a nivel de sistema basadas en tecnicas de linealizacion. La linealizacion mediante predistorsion digital (DPD) se ha convertido en la solucion mas utilizada para mitigar el compromise entre eficiencia y linealidad. La dimension del modelo del predistorsionador DPD depende de la complejidad del sistema, y aumenta significativamente en las arquitecturas de amplificacion de alta eficiencia cuando se consideran los actuales anchos de banda y las tecnologfas espectralmente eficientes. El exceso de parametrizacion puede conducir a una estimacion de los coeficientes DPD, mediante minimos cuadrados (LS), mal condicionada, lo cual generalmente se resuelve empleando tecnicas de regularizacion. Sin embargo, con el fin de reducir la complejidad computacional y evitar dichos problemas de mal acondicionamiento derivados de la sobreparametrizacion, se han dedicado varies esfuerzos para investigar tecnicas de reduccion de dimensionalidad que permitan reducir el orden del modelo del DPD. Esta tesis doctoral contribuye a aportar soluciones para la reduccion de la dimension de los linealizadores DPD para RF PA, centrandose en el subsistema de identificacion y adaptacion. En concrete, se proponen varies enfoques de reduccion de orden del modelo dinamico, basados en tecnicas de extraccion de caracteristicas. El numero minimo de coeficientes DPD relevantes se seleccionan y estiman dinamicamente en el subsistema de adaptacion del DPD, y de este modo la cantidad de coeficientes DPD se reduce, lo cual ademas garantiza una estimacion de LS bien condicionada al tiempo que exige menos recursos de hardware. Las propuestas de linealizacion dinamica presentados en esta tesis se evaluan y comparan mediante validacion experimental con un PA de seguimiento de envolvente y un PA tipo clase J. Los resultados experimentales muestran unos resultados de linealizacion de los PA similares a los obtenidos cuando se em plea la solucion LS convencional, pero con un coste computacional mas reducido.Postprint (published version

Underwater acoustic communications

The underwater acoustic medium poses unique challenges to the design of robust, high throughput digital communications. The aim of this work is to identify modulation and receiver processing techniques to enable the reliable transfer of data at high rate, at range between two, potentially mobile parties using acoustics. More generally, this work seeks to investigate techniques to effectively communicate between two or more parties over a wide range of channel conditions where data rate is a key but not always the absolute performance requirement. Understanding the intrinsic ocean mechanisms that influence signal coherence, the relationship between signal coherence and optimum signal design, and the development of robust modulation and receiver processing techniques are the main areas of study within this work. New and established signal design, modulation, synchronisation, equalisation and spatial processing techniques are investigated. Several new, innovative techniques are presented which seek to improve the robustness of ‘classical’ solutions to the underwater acoustic communications problem. The performance of these techniques to mitigate the severe temporal dispersion of the underwater channel and its unique temporal variability are assessed. A candidate modulation, synchronisation and equalisation architecture is proposed based on a spatial-temporal adaptive signal processing (STAP) receiver. Comprehensive simulation results are presented to demonstrate the performance of the candidate receiver to time selective, frequency selective and spatially selective channel behaviour. Several innovative techniques are presented which maximise system performance over a wider range of operational and environmental conditions. Field trials results are presented based on system evaluation over a wide range of geographically distinct environments demonstrating system performance over a diverse range of ocean bathymetry, topography and background noise conditions. A real time implementation of the system is reported and field trials results presented demonstrating the capability of the system to support a wide range of data formats including video at useful frame rates. Within this work, several novel techniques have been developed which have extended the state of the art in high data rate underwater communications:- • Robust, high fidelity open loop synchronisation techniques capable of operating at marginal signal-to-noise ratios over a wide range of severely time spread environments. These high probability of synchronisation, low probability of false alarm techniques, provide the means for ‘burst’ open loop synchronisation in time, Doppler and space (bearing). The techniques have been demonstrated in communication and position fixing/navigation systems to provide repeatable range accuracy’s to centimetric order. • Novel closed loop synchronisation compensation for STAP receiver architectures. Specifically, this work has demonstrated the performance benefits of including both delay lock loop (DLL) and phase lock loop (PLL) support for acoustic adaptive receivers to offload tracking effort from the fractional feedforward equaliser section. It has been shown that the addition of a DLL/PLL outperforms the PLL only case for Doppler errors exceeding a few fractions of a knot. • Recycling of training data has been demonstrated as a potentially useful means to improve equaliser convergence in difficult acoustic channels. With suitable processing power, training data recycling introduces no additional transmission time overhead, which may be a limiting factor in battery powered applications. • Forward and time reverse decoding of packet data has been demonstrated as an effective means to overcome some non-minimum phase channel conditions. It has also been shown that there may be further benefits in terms of improved bit error performance, by exploiting concurrent forward and backward symbol data under modest channel conditions. • Several wideband techniques have been developed and demonstrated to be effective at resolving and coherently tracking difficult doubly spread acoustic channels. In particular, wideband spread spectrum techniques have been shown to be effective at resolving acoustic multipath, and with the aid of independent delay lock loops, track individual path arrivals. Techniques have been developed which can effect coherent or non-coherent recombination of these paths with a view to improving the robustness of an acoustic link operating at very low signal-to-noise levels. • Demonstrated throughputs of up to 41kbps in a difficult, tropical environment, featuring significant biological noise levels for mobile platforms at range up to 1.5km. • Demonstrated throughputs of between 300bps and 1600bps in a shallow, reverberant environment, at a range up to 21km at LF. • Implemented and demonstrated all algorithms in real time systems

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

FPGA-Based Adaptive Digital Beamforming Using Machine Learning for MIMO Systems

In modern Multiple-Input and Multiple-Output (MIMO) systems, such as cellular and Wi-Fi technology, an array of antenna elements is used to spatially steer RF signals with the goal of changing the overall antenna gain pattern to achieve a higher Signal-to-interference-plus-noise ratio (SINR). Digital Beamforming (DBF) achieves this steering effect by applying weighted coefficients to antenna elements- similar to digital filtering- which adjust the phase and gain of the received, or transmitted, signals. Since real world MIMO systems are often used in dynamic environments, Adaptive Beamforming techniques have been used to overcome variable challenges to system SINR- such as dispersive channels or inter-device interference- by applying statistically-based algorithms to calculate weights adaptively. However, large element count array systems, with their high degrees of freedom (DOF), can face many challenges in real application of these adaptive algorithms. These statistical matrix methods can be either computationally prohibitive, or utilize non-optimal simplifications, in order to provide adaptive weights in time for an application, especially given a certain system's computational capability; for instance, MIMO communication devices with strict size, weight and power (SWaP) constraints often have processing limitations due to use of low-power processors or Field-Programmable Gate Arrays (FPGAs). Thus, this thesis research investigation will show novel progress in these adaptive MIMO challenges in a twofold approach. First, it will be shown that advances in Machine Learning (ML) and Deep Neural Networks (DNNs) can be directly applied to the computationally complex problem of calculating optimal adaptive beamforming weights via a custom Convolutional Neural Net (CNN). Secondly, the derived adaptive beamforming CNN will be shown to efficiently map to programmable logic FPGA resources which can update adaptive coefficients in real-time. This machine learning implementation is contrasted against the current state-of-the-art FPGA architecture for adaptive beamforming- which uses traditional, Recursive Least Squares (RLS) computation- and is shown to provide adaptive beamforming weights faster, and with fewer FPGA logic resources. The reduction in both processing latency and FPGA fabric utilization enables SWaP constrained MIMO processors to perform adaptive beamforming for higher channel count systems than currently possible with traditional computation methods

MIMO Systems

In recent years, it was realized that the MIMO communication systems seems to be inevitable in accelerated evolution of high data rates applications due to their potential to dramatically increase the spectral efficiency and simultaneously sending individual information to the corresponding users in wireless systems. This book, intends to provide highlights of the current research topics in the field of MIMO system, to offer a snapshot of the recent advances and major issues faced today by the researchers in the MIMO related areas. The book is written by specialists working in universities and research centers all over the world to cover the fundamental principles and main advanced topics on high data rates wireless communications systems over MIMO channels. Moreover, the book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Estudo de técnicas de formatação de feixe para transmissão OFDM

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-graduação em Engenharia ElétricaEsta dissertação apresenta um estudo do comportamento de diversos algoritmos adaptativos capazes de realizar a formatação de feixe em uma transmissão OFDM. A técnica de transmissão/recepção OFDM envolve sinais tanto no domínio do tempo quanto no domínio da freqüência, o que possibilita o emprego de dois métodos de estimação do vetor de coeficientes responsável pela formatação de feixe. O primeiro método realiza a estimação no domínio temporal, necessitando de apenas uma FFT e uma IFFT no processo de recepção. O segundo método realiza a estimação no domínio da freqüência e exige uma FFT para cada antena do arranjo. Cada um dos métodos apresenta seus prós e contras em diferentes situações de canal. Portanto, para cada método, situação e algoritmo utilizado, é interessante obter o comportamento do algoritmo, permitindo assim fazer uma comparação entre eles. Outro importante resultado obtido das simulações é a comparação entre um sistema de único usuário, utilizando uma antena, e um sistema multiusuário empregando múltiplas antenas. São apresentadas ainda duas novas proposições para a melhoria de formatadores de feixe no domínio temporal. A primeira proposta melhora o comportamento desse tipo de formatação em situações em que o canal é seletivo em freqüência, fazendo uso da informação do equalizador de canal que geralmente existe em uma recepção OFDM. A segunda proposição reduz o ruído gerado pelas subportadoras de dados em um sistema que emprega somente algumas subportadoras piloto na estimação do formatador de feixe, e que utilize os algoritmos RLS ou DMI

Survey of FPGA applications in the period 2000 – 2015 (Technical Report)

Romoth J, Porrmann M, Rückert U. Survey of FPGA applications in the period 2000 – 2015 (Technical Report).; 2017.Since their introduction, FPGAs can be seen in more and more different fields of applications. The key advantage is the combination of software-like flexibility with the performance otherwise common to hardware. Nevertheless, every application field introduces special requirements to the used computational architecture. This paper provides an overview of the different topics FPGAs have been used for in the last 15 years of research and why they have been chosen over other processing units like e.g. CPUs