An Adaptive Fuzzy Logic System for the Compensation of Nonlinear Distortion in Wireless Power Amplifiers

Computational intelligent systems are becoming an increasingly attractive solution for power amplifier (PA) behavioural modelling, due to their excellent approximation capability. This paper utilizes an adaptive fuzzy logic system (AFLS) for the modelling of the highly nonlinear MIMIX CFH2162-P3 PA. Moreover, PA’s inverse model based also on AFLS has been developed in order to act as a pre-distorter unit. Driving an LTE 1.4 MHz 64 QAM signal at 880 MHz as centre frequency at PA’s input, very good modelling performance was achieved, for both PA’s forward and inverse dynamics. A comparative study of AFLS and neural networks (NN) has been carried out to establish AFLS as an effective, robust, and easy-to-implement baseband model, which is suitable for inverse modelling of PAs and capable to be used as an effective digital pre-distorter. Pre-distortion system based on AFLS, achieved distortion suppression of 84.2%, compared to the 48.4% gained using the NN-based equivalent schem

Compensation of nonlinear distortion in RF amplifiers for mobile communications

Compensation of nonlinear distortion of power amplifiers in mobile communications is an important requirement for improving power consumption performance while maintaining efficiency, since mobile phone became an essential accessory for everyone nowadays. This problem demands a good power amplifier model, in order to develop an effective predistortion system. Current researches are focused on modelling and predistortion of power amplifiers with memory, as well as memoryless ones. Different methods for modelling are used, as the Volterra series, polynomial models, look-up tables, the Hammerstein models, the Wiener models, and artificial intelligence systems. For predistortion feedback, feedforward and digital predistortion techniques are used. Among digital predistortion methods there are artificial intelligence systems, used in this thesis for linearization of power amplifier. This thesis presents developed robust method for modelling power amplifiers without memory effects and gives a comparison of proposed method with least squares method. Also, this research presents two novel techniques based on artificial intelligence systems for modelling and predistortion of highly nonlinear power amplifier with memory. The first approach is based on artificial neural networks, while the second one uses adaptive fuzzy logic systems. Forward and inverse models of power amplifier are created with both proposed methods. Superiority of artificial intelligence systems over partial least squares method is presented. Developed models are employed in a cascade to make a linearized system. Verification of proposed methods is carried out through the signal performance parameters and spectra of measured signal and signal from predistortion system. The feasibility and performances of the proposed digital predistortions are examined by simulations and experiments. The comparison of proposed methods is given to present advantages/disadvantages of both methods. The achieved distortion suppression from 72.2% to 93.6% and spectral regrowth improvement from 11.4 dB to 16.2 dB prove that the proposed methods have great ability to compensate the nonlinear distortion in power amplifier

High power amplifier pre-distorter based on neural-fuzzy systems for OFDM signals

In this paper, a novel High Power Amplifier (HPA) pre-distorter based on Adaptive Networks - Fuzzy Inference Systems (ANFIS) for Orthogonal Frequency Division Multiplexing (OFDM) signals is proposed and analyzed. Models of Traveling Wave Tube Amplifiers (TWTA) and Solid State Power Amplifiers (SSPA), both memoryless and with memory, have been used for evaluation of the proposed technique. After training, the ANFIS linearizes the HPA response and thus, the obtained signal is extremely similar to the original. An average Error Vector Magnitude (EVM) of 10-6 can be easily obtained with our proposal. As a consequence, the Bit Error Rate (BER) degradation is negligible showing a better performance than what can be achieved with other methods available in the literature. Moreover, the complexity of the proposed scheme is reducedThis work was supported in part by projectsMULTIADAPTIVE (TEC2008-06327-C03-02) and AECI Program of Research Cooperation with MoroccoPublicad

Proposal of a health care network based on big data analytics for PDs

Health care networks for Parkinson's disease (PD) already exist and have been already proposed in the literature, but most of them are not able to analyse the vast volume of data generated from medical examinations and collected and organised in a pre-defined manner. In this work, the authors propose a novel health care network based on big data analytics for PD. The main goal of the proposed architecture is to support clinicians in the objective assessment of the typical PD motor issues and alterations. The proposed health care network has the ability to retrieve a vast volume of acquired heterogeneous data from a Data warehouse and train an ensemble SVM to classify and rate the motor severity of a PD patient. Once the network is trained, it will be able to analyse the data collected during motor examinations of a PD patient and generate a diagnostic report on the basis of the previously acquired knowledge. Such a diagnostic report represents a tool both to monitor the follow up of the disease for each patient and give robust advice about the severity of the disease to clinicians

Advances in Solid State Circuit Technologies

This book brings together contributions from experts in the fields to describe the current status of important topics in solid-state circuit technologies. It consists of 20 chapters which are grouped under the following categories: general information, circuits and devices, materials, and characterization techniques. These chapters have been written by renowned experts in the respective fields making this book valuable to the integrated circuits and materials science communities. It is intended for a diverse readership including electrical engineers and material scientists in the industry and academic institutions. Readers will be able to familiarize themselves with the latest technologies in the various fields

Hybrid Dy-NFIS & RLS equalization for ZCC code in optical-CDMA over multi-mode optical fiber

For long haul coherent optical fiber communication systems, it is significant to precisely monitor the quality of transmission links and optical signals. The channel capacity beyond Shannon limit of Single-mode optical fiber (SMOF) is achieved with the help of Multi-mode optical fiber (MMOF), where the signal is multiplexed in different spatial modes. To increase single-mode transmission capacity and to avoid a foreseen “capacity crunch”, researchers have been motivated to employ MMOF as an alternative. Furthermore, different multiplexing techniques could be applied in MMOF to improve the communication system. One of these techniques is the Optical Code Division Multiple Access (Optical-CDMA), which simplifies and decentralizes network controls to improve spectral efficiency and information security increasing flexibility in bandwidth granularity. This technique also allows synchronous and simultaneous transmission medium to be shared by many users. However, during the propagation of the data over the MMOF based on Optical-CDMA, an inevitable encountered issue is pulse dispersion, nonlinearity and MAI due to mode coupling. Moreover, pulse dispersion, nonlinearity and MAI are significant aspects for the evaluation of the performance of high-speed MMOF communication systems based on Optical-CDMA. This work suggests a hybrid algorithm based on nonlinear algorithm (Dynamic evolving neural fuzzy inference (Dy-NFIS)) and linear algorithm (Recursive least squares (RLS)) equalization for ZCC code in Optical-CDMA over MMOF. Root mean squared error (RMSE), mean squared error (MSE) and Structural Similarity index (SSIM) are used to measure performance results

Wideband active envelope load-pull for robust power amplifier and transistor characterisation

The advent of fourth generation (4G) wireless communication with available modulation bandwidth ranging from 1 MHz to 20 MHz is starting to emerge. The linear modulation technique being employed means that the power amplifiers that support the standards need to have high degree of linearity. By nature, however, all power amplifiers are non-linear. Load-pull measurement system provides anindispensable non-linear tool for the characterization of power amplifier and transistor for linearity enhancement. Conventional passive or active load-pull has delay problem that get worse as the modulation frequency is increased beyond few MHz. Furthermore in order to provide robust non-linear measurement, load-pull system needs to provide bandwidth at least five times the modulation bandwidth by including the fifth-order inter-modulation (IMD5). This thesis presents, for the first time, delay compensation on the unique active envelope load-pull architecture providing constant impedance for bandwidth up to 20 MHz. In doing so, it provides a superior load-pull measurement and also the ability to directly control in-band impedances. Artificial variations imposed on the in-band impedances offer further insight on power amplifier and transistor behaviours under wideband multi-tone stimulus.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Analogue implementation of a neural network controller for UPS inverter applications

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