Generalized CMAC adaptive ensembles for concept-drifting data streams

In this paper we propose to use an adaptive ensemble learning framework with different levels of diversity to handle streams of data in non-stationary scenarios in which concept drifts are present. Our adaptive system consists of two ensembles, each one with a different level of diversity (from high to low), and, therefore, with different and complementary capabilities, that are adaptively combined to obtain an overall system of improved performance. In our approach, the ensemble members are generalized CMACs, a linear-in-the-parameters network. The ensemble of CMACs provides a reasonable trade-off between expressive power, simplicity, and fast learning speed. At the end of the paper, we provide a performance analysis of the proposed learning framework on benchmark datasets with concept drifts of different levels of severity and speed.This work is partially funded by grant CASI-CAM-CM (S2013/ICE-2845), DGUI-Comunidad de Madrid, and grants DAMA (TIN2015-70308-REDT), MINECO, and Macro-ADOBE (TEC 2015-67719-P), MINECO-FEDER-EU

Design and implementation of adaptive baseband predistorter for OFDM nonlinear transmitter. Simulation and measurement of OFDM transmitter in presence of RF high power amplifier nonlinear distortion and the development of adaptive digital predistorters based on Hammerstein approach.

The objective of this research work is to investigate, design and measurement of a digital predistortion linearizer that is able to compensate the dynamic nonlinear distortion of a High Power Amplifier (PA). The effectiveness of the proposed baseband predistorter (PD) on the performance of a WLAN OFDM transmitter utilizing a nonlinear PA with memory effect is observed and discussed. For this purpose, a 10W Class-A/B power amplifier with a gain of 22 dB, operated over the 3.5 GHz frequency band was designed and implemented. The proposed baseband PD is independent of the operating RF frequency and can be used in multiband applications. Its operation is based on the Hammerstein system, taking into account PA memory effect compensation, and demonstrates a noticeable improvement compared to memoryless predistorters. Different types of modelling procedures and linearizers were introduced and investigated, in which accurate behavioural models of Radio Frequency (RF) PAs exhibiting linear and nonlinear memory effects were presented and considered, based on the Wiener approach employing a linear parametric estimation technique. Three new linear methods of parameter estimation were investigated, with the aim of reducing the complexity of the required filtering process in linear memory compensation. Moreover, an improved wiener model is represented to include the nonlinear memory effect in the system. The validity of the PA modelling approaches and predistortion techniques for compensation of nonlinearities of a PA were verified by several tests and measurements. The approaches presented, based on the Wiener system, have the capacity to deal with the existing trade-off between accuracy and convergence speed compared to more computationally complex behavioural modelling algorithms considering memory effects, such as those based on Volterra series and Neural Networks. In addition, nonlinear and linear crosstalks introduced by the power amplifier nonlinear behaviour and antennas mutual coupling due to the compact size of a MIMO OFDM transmitter have been investigated

Digital communication receivers using Gaussian processes for machine learning

We propose Gaussian processes (GPs) as a novel nonlinear receiver for digital communication systems. The GPs framework can be used to solve both classification (GPC) and regression (GPR) problems. The minimum mean squared error solution is the expectation of the transmitted symbol given the information at the receiver, which is a nonlinear function of the received symbols for discrete inputs. GPR can be presented as a nonlinear MMSE estimator and thus capable of achieving optimal performance from MMSE viewpoint. Also, the design of digital communication receivers can be viewed as a detection problem, for which GPC is specially suited as it assigns posterior probabilities to each transmitted symbol. We explore the suitability of GPs as nonlinear digital communication receivers. GPs are Bayesian machine learning tools that formulates a likelihood function for its hyperparameters, which can then be set optimally. GPs outperform state-of-the-art nonlinear machine learning approaches that prespecify their hyperparameters or rely on cross validation. We illustrate the advantages of GPs as digital communication receivers for linear and nonlinear channel models for short training sequences and compare them to state-of-the-art nonlinear machine learning tools, such as support vector machines

Theory Based on Device Current Clipping to Explain and Predict Performance Including Distortion of Power Amplifiers for Wireless Communication Systems

Power amplifiers are critical components in wireless communication systems that need to have high efficiency, in order to conserve battery life and minimise heat generation, and at the same time low distortion, in order to prevent increase of bit error rate due to constellation errors and adjacent channel interference. This thesis is aimed at meeting a need for greater understanding of distortion generated by power amplifiers of any technology, in order to help designers manage better the trade-off between obtaining high efficiency and low distortion. The theory proposed in this thesis to explain and predict the performance of power amplifiers, including distortion, is based on analysis of clipping of the power amplifier device current, and it is a major extension of previous clipping analyses, that introduces many key definitions and concepts. Distortion and other power amplifier metrics are determined in the form of 3-D surfaces that are plotted against PA class, which is determined by bias voltage, and input signal power level. It is shown that the surface of distortion exhibits very high levels due to clipping in the region where efficiency is high. This area of high distortion is intersected by a valley that is ‘L’-shaped. The 'L'-shaped valley is subject to a rotation that depends on the softness of the cut-off of the power amplifier device transfer characteristic. The distortion surface with rotated 'L'-shaped valley leads to predicted curves for distortion versus input signal power that match published measured curves for power amplifiers even using very simple device models. The distortion versus input signal power curves have types that are independent of technology. In class C, there is a single deep null. In the class AB range, that is divided into three sub-ranges, there may be two deep nulls (sub-range AB(B)), a ledge (sub-range AB(A)) or a shallow null with varying depth (sub-range AB(AB))