Continuous-time adaptive control applied to rf amplifier linearization

A new approach to the RF power amplifier linearization problem is presented. The proposed solution applies non-linear theories (Lyapunov direct method) to adaptive filtering in order to improve the linearity of the RF amplifiers. The obtained design requires lower circuit complexity than the LINC amplifier, and is not based on iterative algorithms nor sub-system identification. Up to 100 MHz these functions could be implemented, at present, with operational amplifiers and integrated analog multipliers (four quadrants). The adjusting algorithm convergence or the interruption of the communication are not problems in the proposed adaptive solution. The canceller structure design is based on model reference adaptive systems (MRAS): to cancel the error between the plant output (distortion output of the RF amplifier) and reference model (the desired signal obtained from a linear and low-power amplifier) by using continuous-time techniques. The proposed structure is studied by computer simulation (SPICE program) in a class-A RF power amplifier, The behaviour of the adapted amplifier is studied when power transistors approach nonlinear operating zones (saturation state).Peer ReviewedPostprint (published version

Caracterização de moduladores RSOA em ligações de radio sobre fibra

Doutoramento em Engenharia ElectrotécnicaIn this work physical and behavioral models for a bulk Reflective Semiconductor Optical Amplifier (RSOA) modulator in Radio over Fiber (RoF) links are proposed. The transmission performance of the RSOA modulator is predicted under broadband signal drive. At first, the simplified physical model for the RSOA modulator in RoF links is proposed, which is based on the rate equation and traveling-wave equations with several assumptions. The model is implemented with the Symbolically Defined Devices (SDD) in Advanced Design System (ADS) and validated with experimental results. Detailed analysis regarding optical gain, harmonic and intermodulation distortions, and transmission performance is performed. The distribution of the carrier and Amplified Spontaneous Emission (ASE) is also demonstrated. Behavioral modeling of the RSOA modulator is to enable us to investigate the nonlinear distortion of the RSOA modulator from another perspective in system level. The Amplitude-to-Amplitude Conversion (AM-AM) and Amplitude-to-Phase Conversion (AM-PM) distortions of the RSOA modulator are demonstrated based on an Artificial Neural Network (ANN) and a generalized polynomial model. Another behavioral model based on Xparameters was obtained from the physical model. Compensation of the nonlinearity of the RSOA modulator is carried out based on a memory polynomial model. The nonlinear distortion of the RSOA modulator is reduced successfully. The improvement of the 3rd order intermodulation distortion is up to 17 dB. The Error Vector Magnitude (EVM) is improved from 6.1% to 2.0%. In the last part of this work, the performance of Fibre Optic Networks for Distributed and Extendible Heterogeneous Radio Architectures and Service Provisioning (FUTON) systems, which is the four-channel virtual Multiple Input Multiple Output (MIMO), is predicted by using the developed physical model. Based on Subcarrier Multiplexing (SCM) techniques, four-channel signals with 100 MHz bandwidth per channel are generated and used to drive the RSOA modulator. The transmission performance of the RSOA modulator under the broadband multi channels is depicted with the figure of merit, EVM under di erent adrature Amplitude Modulation (QAM) level of 64 and 254 for various number of Orthogonal Frequency Division Multiplexing (OFDM) subcarriers of 64, 512, 1024 and 2048.Nesta tese são propostos modelos físicos e comportamentais para o amplificador óptico semicondutor reflectivo (RSOA), tendo como objectivo a avaliação do seu desempenho quando utilizado como modulador em ligações de rádio sobre fibra (RoF). Os modelos propostos são capazes de prever o comportamento do dispositivo quando utilizado com sinais de banda larga bem como quando estimulado por sinais de elevada potência. Inicialmente propõe-se um modelo físico simplificado para o RSOA baseado nas equações de taxa e nas equações de propagação electromagnética. A implementação do modelo utiliza o ADS (Advanced Design Systems) e o bloco designado por dispositivo definido simbolicamente (SDD) para descrever as equações de taxa, assim como a propagação de fotões ao longo da cavidade. O modelo permite uma análise detalhada do ganho óptico, distorções harmônicas, intermodulação e seu desempenho de transmissão com portadoras RF modeladas. Foram também considerados modelos comportamentais. Um modelo baseado em rede neural artificial (ANN) e um modelo polinomial generalizado para banda base foram considerados tendo os parâmetros respectivos sido extraídos utilizando, para o efeito, dados obtidos experimentalmente. São demonstradas a característica da distorção resultante da conversão amplitude - amplitude (AM-AM) e conversão da fase - amplitude (AM-PM) no modulador RSOA. Um modelo baseado em parametros X, obtidos a partir do modelo físico, foi também analisado. Compensação da não-linearidade do modulador RSOA é realizada com base num modelo polinomial com memória. Demonstra-se que a distorção não linear do modulador RSOA pode ser compensada com sucesso. Com a compensação obtem-se uma redução de 17 dB da distorção introduzida pelos produtos de intermodulação de terceira ordem. O EVM (Error Vector Magnitude) apresenta uma melhoria de 6,1% para 2,0%. Na última parte deste trabalho considera-se uma configuração que representa a ligação ascendente por fibra de um sistema de antenas remoto a uma estação central de processamento. Com esta configuração pretendese demonstrar a possibilidade de implementação de uma tecnologia MIMO, suportada num sistema RoF. Baseado numa técnica de multiplexação subportadora (SCM), os sinais de quatro canais com largura de banda de 100 MHz por canal são multiplexados e utilizados para modelar o ganho do RSOA. O desempenho deste link óptico é caracterizado para modulações OFDM considerando diferentes números de sub-portadoras por símbolo (64, 512 , 1024 e 2048) assim como o formato QAM imposto sobre cada sub-portadora

On Design and Optimization of Convolutional Neural Network for Embedded Systems

This work presents the research on optimizing neural networks and deploying them for real-time practical applications. We analyze different optimization methods, namely binarization, separable convolution and pruning. We implement each method for the application of vehicle classification and we empirically evaluate and analyze the results. The objective is to make large neural networks suitable for real-time applications by reducing the computation requirements through these optimization approaches. The data set is of vehicles from 4 classes of vehicle types, and a convolutional model was used to solve the problem initially. Our results show that these optimization methods offer many performance benefits in this application in terms of reduced execution time (by up to 5 ×), reduced model storage requirements, with out largely impacting accuracy, making them a suitable tool for use in streamlining heavy neural networks to be used on resource-constrained envrionments. The platforms used in the research are a desktop platform, and two embedded platforms

Machine Learning Meets Communication Networks: Current Trends and Future Challenges

The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction

Algorithmic lateral inhibition formal model for real-time motion detection.

Recently, the use of the algorithmic lateral inhibition (ALI) method in motion detection has shown to be very effective. The promising results in terms of the goodness of the silhouettes detected and tracked along video sequences lead us to accept the challenge of searching for a real-time implementation of the algorithms. This paper introduces two steps towards that direction: (a) A simplification of the general ALI method is performed by formally transforming it into a finite state machine. (b) A hardware implementation of such a designed ALI module, as well as an 8x8 ALI module, has been tested on several video sequences, providing promising performance results

Home energy forecast performance tool for smart living services suppliers under an energy 4.0 and CPS framework

Industry 4.0 is a paradigm consisting of cyber-physical systems based on the interconnection between all sorts of machines, sensors, and actuators, generally known as things. The combination of energy technology and information and technology communication (ICT) enables measure ment, control, and automation to be performed across the distributed grid with high time resolution. Through digital revolution in the energy sector, the term Energy 4.0 emerges in the future electric sector. The growth outlook for appliance usage is increasing and the appearance of renewable energy sources on the electric grid requires strategies to control demand and peak loads. Potential feedback for energy performance is the use of smart meters in conjunction with smart energy man agement; well-designed applications will successfully inform, engage, empower, and motivate con sumers. This paper presents several hands-on tools for load forecasting, comparing previous works and verifying which show the best energy forecasting performance in a smart monitoring system. Simulations were performed based on forecasting of the hours ahead of the load for several households. Special attention was given to the accuracy of the forecasting model for weekdays and weekends. The development of the proposed methods, based on artificial neural networks (ANN), pro vides more reliable forecasting for a few hours ahead and peak loads.info:eu-repo/semantics/publishedVersio

Circuit paradigm in the 21

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Real-time motion detection by lateral inhibition in accumulative computation.

Many researchers have explored the relationship between recurrent neural networks and finite state machines. Finite state machines constitute the best characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving. In the few last years, the neurally inspired lateral inhibition in accumulative computation (LIAC) method and its application to the motion detection task have been introduced. The article shows how to implement the tasks directly related to LIAC in motion detection by means of a formal model described as finite state machines. This paper introduces two steps towards that direction: (a) A simplification of the general LIAC method is performed by formally transforming it into a finite state machine. (b) A hardware implementation of such a designed LIAC module, as well as an 8×8 LIAC module, has been tested on several video sequences, providing promising performance results