Showing papers on "Volterra series published in 2016"
TL;DR: In this article, a linear and nonlinear digital pre-distortion (DPD) tailored to the components of an optical transmitter is proposed, which uses nonlinear models of the transmitter devices which are obtained from direct component measurements.
Abstract: We present a linear and nonlinear digital pre-distortion (DPD) tailored to the components of an optical transmitter. The DPD concept uses nonlinear models of the transmitter devices, which are obtained from direct component measurements. While the digital-to-analog converter and driver amplifier are modeled jointly by a Volterra series, the modulator is modeled independently as a Wiener system. This allows for a block-wise compensation of the modulator by a Hammerstein system and a pre-distortion of the electrical components by a second Volterra series. In simulations and extensive experiments, the performance of our approach for nonlinear DPD is compared to an equivalent linear solution as well as to a configuration without any DPD. The experiments were performed using M -ary quadrature-amplitude modulation ( M -QAM) formats ranging from 16- to 128-QAM at a symbol rate of 32 GBd. It is shown that the DPD improves the required optical signal-to-noise ratio at a bit error ratio of 2·10 −2 by at least 1.2 dB. Nonlinear DPD outperforms linear DPD by an additional 0.9 and 2.7 dB for higher-order modulation formats such as 64-QAM and 128-QAM, respectively.
92 citations
TL;DR: The Loewner framework for model reduction is extended to the class of bilinear systems and one can derive state-space models directly from input-output data without requiring initial system matrices.
Abstract: The Loewner framework for model reduction is extended to the class of bilinear systems The main advantage of this framework over existing ones is that the Loewner pencil introduces a trade-off between accuracy and complexity Furthermore, through this framework, one can derive state-space models directly from input-output data without requiring initial system matrices The recently introduced methodology of Volterra series interpolation is also addressed Several numerical experiments illustrate the main features of this approach
85 citations
TL;DR: In this article, the authors proposed a key kernels-PSO method for rotor-bearing series identification, where the key kernels of the Volterra model were found out by the PSO method.
37 citations
TL;DR: A novel versatile digital signal processing (DSP)-based equalizer using support vector machine regression (SVR) is proposed for 16-quadrature amplitude modulated (16-QAM) coherent optical orthogonal frequency-division multiplexing and experimentally compared to traditional DSP-based deterministic fiber-induced nonlinearity equalizers (NLEs).
Abstract: A novel versatile digital signal processing (DSP)-based equalizer using support vector machine regression (SVR) is proposed for 16-quadrature amplitude modulated (16-QAM) coherent optical orthogonal frequency-division multiplexing (CO-OFDM) and experimentally compared to traditional DSP-based deterministic fiber-induced nonlinearity equalizers (NLEs), namely the full-field digital back-propagation (DBP) and the inverse Volterra series transfer function-based NLE (V-NLE) For a 40 Gb/s 16-QAM CO-OFDM at 2000 km, SVR-NLE extends the optimum launched optical power (LOP) by 4 dB compared to V-NLE by means of reduction of fiber nonlinearity In comparison to full-field DBP at a LOP of 6 dBm, SVR-NLE outperforms by ∼1 dB in Q-factor In addition, SVR-NLE is the most computational efficient DSP-NLE
36 citations
TL;DR: Experimental results show that the proposed modified canonical piecewise-linear function (CPWL)-based model nearly gives the same normalized mean square error performance as the DVR model when a wideband Doherty power amplifier is excited by a 5-carrier long-term evolution advanced signal of 100 MHz bandwidth.
Abstract: A band-limited canonical piecewise-linear function (CPWL)-based model is proposed for wideband power amplifiers (PAs). The model has a similar structure of the band-limited dynamic deviation reduction (DDR) Volterra series model but without high-order terms and long finite impulse response filters, which are replaced by CPWL. The model has lower complexity and more flexibility than the band-limited DDR Volterra series model and the model parameters can be estimated by the least-square method. Experimental results show that the proposed model nearly gives the same normalized mean square error and digital predistortion performance as the band-limited Volterra series when a wideband PA is excited by a 5-carrier long-term evolution advanced signal of 100-MHz bandwidth.
29 citations
TL;DR: In this article, an empirical model for field effect transistor (FET) based power detectors is presented, which constitutes a Volterra analysis based on a Taylor series expansion of the drain current together with a linear embedding small-signal circuit.
Abstract: An empirical model for field-effect transistor (FET) based power detectors is presented. The electrical model constitutes a Volterra analysis based on a Taylor series expansion of the drain current together with a linear embedding small-signal circuit. It is fully extracted from S-parameters and IV curves. The final result are closed-form expressions for the frequency dependence of the noise equivalent power (NEP) in terms of the FET intrinsic capacitances and parasitic resistances. Excellent model agreement to measured NEP of coplanar access graphene FETs with varying channel dimensions up to 67 GHz is obtained. The influence of gate length on responsivity and NEP is theoretically and experimentally studied.
24 citations
TL;DR: The results show that the proposed mixer without the π-network, in comparison with the conventional mixer, exhibits up to 15 and 14 dB improvements in IIP2 and IIP3, respectively; the gain, bandwidth, and noise figure are also improved.
Abstract: This paper presents the distortion analysis of a linearized CMOS subharmonic mixer (SHM) based on a Volterra series analysis. The second- and third-order intermodulation distortions are reduced by a modified second-harmonic reinjection in the RF transconductance stage. An injected IM2 is mixed with an RF input signal and generates an IM3 signal with the same amplitude and opposite phase of a main path for the cancellation of the intrinsic IM3 signal. In order to cancel the second-order intermodulation component, a signal with the same IM2 amplitude and opposite phase of the main path is generated. Closed-form expressions of the conversion gain along with the second-and third-order distortions are derived using a Volterra series analysis to facilitate an optimal design and provide an insight into the nonlinearity of SHM. An inductive connection between RF and local oscillator stages implementing a $\pi $ -network is employed to improve the linearity and enhance the gain and bandwidth of the mixer. Simulation results performed in Taiwan Semiconductor Manufacturing Company 0.18- $\mu \text{m}$ CMOS process at 2.4-GHz RF frequency and 1.6 V supply voltage. The results show that the proposed mixer without the $\pi $ -network, in comparison with the conventional mixer, exhibits up to 15 and 14 dB improvements in IIP2 and IIP3, respectively. The improvements obtained over 1–20 MHz two-tone spacing range without gain reduction or noise penalty. Moreover, in the presence of the $\pi $ -network, the mixer exhibits up to 25 and 7 dB improvements in IIP2 and IIP3, respectively; the gain, bandwidth, and noise figure are also improved. The simulation results demonstrate good accuracy in comparison with the analytical results.
19 citations
01 Jun 2016
TL;DR: A stabilization scheme for nonlinear control systems whose vector fields satisfy Hormander's condition with the second-order Lie brackets is proposed, based on the use of trigonometric controls with bounded frequencies and a modification of Lyapunov's direct method.
Abstract: In this paper, we propose a stabilization scheme for nonlinear control systems whose vector fields satisfy Hormander's condition with the second-order Lie brackets. This scheme is based on the use of trigonometric controls with bounded frequencies. By using the Volterra series and a modification of Lyapunov's direct method, we reduce the stabilization problem to a system of cubic equations and prove its local solvability. Our approach ensures exponential stability of the equilibrium and gives explicit formulas for the coefficients of the control functions. The proposed methodology is illustrated by a rolling disc example.
18 citations
TL;DR: In this article, an approach for nonlinear system identification using output-only data is proposed, where the outputs of the system are computed by multiple convolutions between the excitation force and the Volterra kernels.
Abstract: The operational modal analysis methods based on output-only measurements are well-known and applied in linear systems. However, they are not so well developed for nonlinear systems. Thus, this paper proposes an approach for nonlinear system identification using output-only data. In the conventional Volterra series, the outputs of the system are computed by multiple convolutions between the excitation force and the Volterra kernels. However, in this paper at least two time series measured in different placements are used to compute the multiple convolutions and the excitation signals are not required. The novel kernels identified can be used to characterize nonlinear behavior through a model using only output data. A numerical example based on a Duffing oscillator with two degrees-of-freedom (2DOF) and experimental vibration data from a buckled beam with hardening nonlinearities are used to illustrate the proposed method. The prediction results using output-only data are similar to the conventional Volterra kernels based on input and output data.
16 citations
TL;DR: In this article, the time-average performance of a chemical reactor subjected to single input modulations of general waveforms was evaluated using the nonlinear frequency response (NFR) method.
Abstract: The nonlinear frequency response (NFR) method is used for evaluating the time-average performance of a chemical reactor subjected to single input modulations of general waveforms, by using Fourier series for representing the input and Volterra series for representing the output. Both the input and the output are approximated by finite sums. The obtained results are applied for the case of a square-wave input modulation. As a case study, the improvement of an isothermal continuous stirred-tank reactor with simple reaction mechanism with modulation of the inlet reactant concentration is used and the results are tested on a numerical example.
15 citations
TL;DR: The interpolation based first-order necessary conditions for H 2 optimality to bilinear descriptor systems are extended and an iterative scheme to obtain an H 2 optimal reduced-order system is proposed.
TL;DR: Compared to the conventional VNLE using training symbols before demodulation, the blind Volterra series based nonlinear equalization is performed after matched filtering and downsampling, so shorter memory length is required but similar performance improvement is observed.
01 Nov 2016
TL;DR: A new derivation is presented which shows that by assuming modified basis functionals in the passband, one obtains a baseband Volterra series which also includes even-order terms and decreases the condition number of the regression matrix.
Abstract: The baseband Volterra series is a general approach to model nonlinear passband systems like radio frequency power amplifiers in equivalent baseband. In the present paper, we review the derivation of the baseband Volterra series using a compact vector notation and show that it only includes odd-order terms. After that, we present a new derivation which shows that by assuming modified basis functionals in the passband, one obtains a baseband Volterra series which also includes even-order terms. By simulations, we demonstrate that the inclusion of the proposed even-order basis functionals improves the performance of behavioral modeling and digital predistortion and decreases the condition number of the regression matrix.
TL;DR: In this paper, an inter-subcarrier nonlinear and linear interference canceler (INIC) is proposed for long-haul Nyquist-wavelength division multiplexing superchannel transmission, which consists in detecting the adjacent subcarriers, regenerating them thanks to the Volterra series model of optical fiber, and removing them from the subcarrier of interest.
Abstract: For long-haul Nyquist-wavelength division multiplexing superchannel transmission, an inter-subcarrier nonlinear and linear interference canceler (INIC) is proposed. This approach consists in detecting the adjacent subcarriers, regenerating them thanks to the Volterra series model of optical fiber, and removing them from the subcarrier of interest. Different ways to implement the INIC are described and compared with the well-known techniques, such as digital backpropagation (DBP) and Volterra-based nonlinear equalizer (VNLE) implemented in a subcarrierwise manner. Significant performance gain (on either the $Q$ factor or transmission distance) is observed. In the context of 400 Gbps scheme, the transmission distance gain is up to 500 km compared with the DBP and VNLE.
TL;DR: In this article, a new pre-distortion technique for power amplifiers in wideband applications is proposed based on Nonlinear Autoregressive with Exogenous inputs (NARX).
Abstract: In this letter, a new pre-distortion technique for power amplifiers in wideband applications is proposed. The proposed pre-distortion technique is based on Nonlinear Autoregressive with Exogenous inputs (NARX). The forward path of the proposed predictive method is based on the memory polynomial. Experimental validation is carried out with 4 carrier WCDMA signal with 20MHz bandwidth and $\text{PAPR}=9.8\ \text{dB}$ . The results show significant reduction in the number of coefficients with comparable performance in terms of adjacent channel leakage ratio (ACLR) and error vector magnitude (EVM) to Volterra series techniques.
07 Nov 2016
TL;DR: This paper presents a non-linear model identification and simulation framework built on top of Volterra series and its seamless integration with tensor arithmetic, exploiting partially-symmetric polyadic decompositions of sparse Toeplitz tensors to allow computationally fast modeling and simulation beyond weakly non- linear systems.
Abstract: Tensors are a multi-linear generalization of matrices to their d-way counterparts, and are receiving intense interest recently due to their natural representation of high-dimensional data and the availability of fast tensor decomposition algorithms. Given the input-output data of a nonlinear system/circuit, this paper presents a non-linear model identification and simulation framework built on top of Volterra series and its seamless integration with tensor arithmetic. By exploiting partially-symmetric polyadic decompositions of sparse Toeplitz tensors, the proposed framework permits a pleasantly scalable way to incorporate high-order Volterra kernels. Such an approach largely eludes the curse of dimensionality and allows computationally fast modeling and simulation beyond weakly non-linear systems. The black-box nature of the model also hides structural information of the system/circuit and encapsulates it in terms of compact tensors. Numerical examples are given to verify the efficacy, efficiency and generality of this tensor-based modeling and simulation framework.
07 Mar 2016
TL;DR: In this article, a model-free predictive control method for nonlinear systems on the basis of polynomial regression is proposed, which is a generalization of the so-called Volterra series expansion of nonlinear functions.
Abstract: This paper proposes a model-free predictive control method for nonlinear systems on the basis of polynomial regression. In contrast to conventional model predictive control, model-free predictive control does not require mathematical models. Instead, it uses the previous recorded input/output datasets of the controlled system to predict an optimal control input so as to achieve the desired output. The novel point in this paper is the improvement of existing model-free predictive control by adopting polynomial regression, which is a generalization of the so-called Volterra series expansion of nonlinear functions.
TL;DR: An improved algorithm of wavelet packet-chaos model for life prediction of space relays based on volterra series that can predict the life curve of the relay accurately and reflect the characteristics of the relays performance with sufficient accuracy is proposed.
Abstract: In this paper, an improved algorithm of wavelet packet-chaos model for life prediction of space relays based on volterra series is proposed. In the proposed method, the high and low frequency time sequence components of performance parameters are obtained by employing the improved wavelet packet transform to decompose the performance parameters of the relay into multiple scales. Then the optimization algorithm of parameters in volterra series is improved, and is used to construct a chaotic forecasting model for the high and low frequency time sequence components gained by the wavelet packet transform. At last, the chaotic forecasting results of the high and low frequency components are combined by taking the wavelet packet reconstruction approach, so as to predict the lifetime of the studied space relay. The algorithm can predict the life curve of the relay accurately and reflect the characteristics of the relay performance with sufficient accuracy. The proposed method is validated via a case study of a space relay.
TL;DR: In this paper, a fast approach to the numerical solution of induction heating problems is proposed, where the projection space is efficiently determined by numerically computing a few Volterra kernels of the solution to the problem.
Abstract: A fast approach to the numerical solution of induction heating problems is proposed. The projection space is efficiently determined by numerically computing a few Volterra kernels of the solution to the problem. Numerical results show that the construction of the reduced nonlinear model is performed at a computational cost that is orders of magnitude less than that for the numerical integration of the full problem. The reduced order model solution then allows accurately reconstructing the whole space-time distribution of magnetic and temperature fields at negligible computational cost.
TL;DR: It is observed that the TMV model reduces the normalized mean-square error and the adjacent channel error power ratio for the upper adjacent channel (upper ACEPR) by 1.6 dB when it is compared to the previous LV and KV models under the same computational complexity.
Abstract: In this paper, a Takenaka---Malmquist---Volterra (TMV) model structure is employed to improve the approximations in the low-pass equivalent behavioral modeling of radio frequency (RF) power amplifiers (PAs). The Takenaka---Malmquist basis generalizes the orthonormal basis functions previously used in this context. In addition, it allows each nonlinearity order in the expanded Volterra model to be parameterized by multiple complex poles (dynamics). The state-space realizations for the TMV models are introduced. The pole sets for the TMV model and also for the previous Laguerre---Volterra (LV) and Kautz---Volterra (KV) models are obtained using a constrained nonlinear optimization approach. Based on experimental data measured on a GaN HEMT class AB RF PA excited by a WCDMA signal, it is observed that the TMV model reduces the normalized mean-square error and the adjacent channel error power ratio for the upper adjacent channel (upper ACEPR) by 1.6 dB when it is compared to the previous LV and KV models under the same computational complexity.
Dissertation•
15 Apr 2016
TL;DR: In this article, the authors proposed and implemented the nonlinear frequency response (NFR) method, which is a relatively new method, mathematically based on Volterra series, generalized Fourier transform and the concept of higher-order frequency response functions (FRFs).
Abstract: The conventional way to design and operate the processes in chemical engineering is to determine the optimal steady-state design and to operate as close as possible to that steady-state. Nevertheless, many investigations have proven that periodic operations, when one or more inputs are periodically modulated, can result with better process performances, especially for chemical reactors. The origin of improvement of the reactor performance lies in fact that for nonlinear systems, the periodic modulation of one or more inputs will cause the outputs to change periodically, as well, with the mean value which is, in general, different from their steady-state values. In this work, we propose and implement the nonlinear frequency response (NFR) method for fast and easy evaluation of possible reactor improvements throughout periodic modulations. The NFR method is a relatively new method, mathematically based on Volterra series, generalized Fourier transform and the concept of higher-order frequency response functions (FRFs). The change of the reactor performances caused by periodic operations can be evaluated from the DC (non-periodic) component of the frequency response of the reactor, if it is a weakly nonlinear system. The DC component can be calculated exactly as a sum of an indefinite series, with members which are proportional to the asymmetrical even order FRFs. Nevertheless, based on the NFR method, the DC component can be estimated only from the first, dominant term of this series, which is proportional to the asymmetrical second order frequency response function G2(ω,-ω). In that way, for analysis of possible improvements of forced periodically operated chemical reactors, it is enough to derive and analyze asymmetrical second order FRFs G2(ω,-ω). For this reason, the NFR method is essentially approximate.
01 Sep 2016
TL;DR: This work investigates the performance of a Volterra-based nonlinear equalizer and the digitalbackpropagation (DBP) method in multi-channel non linear equalization after 20×80 km transmission distance.
Abstract: We investigate the performance of a Volterra-based nonlinear equalizer and the digitalbackpropagation (DBP) method in multi-channel nonlinear equalization after 20×80 km transmission distance. The Volterra equalizer, which operates with single-step-per-span, performs similarly compared to DBP with 40 steps-per-span.
TL;DR: In this article, the adaptive prediction method of Volterra series method in chaotic time series based on matrix factorization method is presented. But, the prediction performance has no problems in selecting the initial value and can achieve highly-accurate prediction.
Abstract: This paper comes up with the adaptive prediction method of Volterra series method in chaotic time series based on matrix factorization method. Taking the monthly runoff of the Huaxian Hydrological Station as example, based on phase-space reconstruction, it identifies the chaotic characteristic through correlation dimension and Lyapunov index. Based on Volterra adaptive filter model, use matrix factorization to solve the equation, which avoid the local optimum problem caused by selecting initial value in Normalized Least Mean Square (NLMS), and at the same time, obtain the global optimal Volterra filter coefficient. According to the Numerical experiments, the prediction performance based on matrix factorization method has no problems in selecting the initial value and can achieve highly-accurate prediction. The conclusions are as follows: (1) The monthly runoff in Huaxian has chaotic characteristic. It’s embedding dimension and optimal embedding dimension is 3; (2) Matrix factorization avoids squar...
27 May 2016
TL;DR: In this article, the basis waveforms within the estimator are specified in the frequency domain as a function of memoryless waveforms and delay operators, thereby reducing the number of FFTs needed and allowing the tap spacing to be a fraction of a sample.
Abstract: Computationally efficient estimation of digital predistortion (DPD) coefficients for memory polynomial, gain polynomial, and pruned Volterra series models is proposed. In all three cases, the basis waveforms within the estimator are specified in the frequency domain as a function of memoryless waveforms and delay operators, thereby reducing the number of FFTs needed and allowing the tap spacing to be a fraction of a sample. The gain polynomial and pruned Volterra series cases are approximations that are sufficiently accurate for a closed loop estimator to converge to a desired steady-state.
TL;DR: The proposed technique has shown to be useful in identifying the dominant effects in the transmitter structure and it can be used to design behavioral models and compensation techniques.
Abstract: An instrumentation, measurement and post-processing technique is presented to characterize transmitters by multiple input multiple output (MIMO) Volterra series. The MIMO Volterra series is decomposed as the sum of nonlinear single-variable self-kernels and a multi-variable cross-kernel. These kernels are identified by sample averages of the outputs using inputs of different sample periodicity. This technique is used to study the HW effects in a RF MIMO transmitter composed by input and output coupling filters (cross-talk) sandwiching a non-linear amplification stage. The proposed technique has shown to be useful in identifying the dominant effects in the transmitter structure and it can be used to design behavioral models and compensation techniques.
TL;DR: In this paper, a novel approach is proposed to identify the cascade of Hammerstein model by using Volterra series analytical method, which consists of power series associated with linear subsystems.
Abstract: In this paper, a novel approach is proposed to identify the cascade of Hammerstein model by using Volterra series analytical method. The cascade of Hammerstein model consists of power series associated with linear subsystems. The relationship between the cascade of Hammerstein model and its associated Volterra model is firstly presented in this paper. The basic routine of the identification approach is that, from the system outputs under multilevel excitations, the Volterra series outputs of different order are first estimated by using the wavelet balance method. Then, through each order Volterra outputs and input, the impulse response functions of each order linear subsystems can be estimated, respectively. The simulation studies verify the effectiveness of the proposed identification method for the cascade of Hammerstein model.
01 Jul 2016
TL;DR: In this paper, the combined LMS/F adaptive filter is modified using Volterra expansion of input samples up to second order to form VLMS-F filter, which offers advantage in terms of estimation accuracy and computational complexity in a low SNR condition.
Abstract: Real time estimation of power system harmonics requires the proper choice of filter structure and algorithm for coefficient adjustment. Though LMS (Least Mean Square) adaptive filter has an elegant and simple structure; the performance degrades in low SNR and non-stationary condition. On the other hand LMF (Least Mean Fourth) adaptive filter provides better estimation but the performance is limited due to high computational complexity. Thus LMF is not a suitable choice for real time estimation of harmonics. In this paper; combined LMS/F adaptive filter is modified using Volterra expansion of input samples up to second order to form VLMS/F filter. This filter offers advantage in terms of estimation accuracy and computational complexity in a low SNR condition. Estimation results of VLMS/F are compared with other variants of LMS for harmonic parameters and decaying DC.
TL;DR: In this paper, an asymmetrical complexity-reduced Volterra series model is proposed, namely joint-digital predistortion (joint-DPD), which is inspired by the interaction between such two impairments, providing high degree of dynamic nonlinearities with large memory depths.
Abstract: I/Q imbalance and nonlinearities of power amplifier (PA) are the main impairments of wireless transmitters degrading the spectral purity and error vector magnitude (EVM). In order to jointly migrate them, an asymmetrical complexity-reduced Volterra series model is proposed, namely joint-digital predistortion (joint-DPD). Our joint-DPD is inspired by the interaction between such two impairments, providing high degree of dynamic nonlinearities with large memory depths. Also, by understanding the structure of joint-DPD for OFDM signals, lower computational complexity can be achieved by pruning the redundant terms with an asymmetrical structure. The corresponding analysis offers the theoretical basis and its related complexity over different Volterra-series-based joint-DPDs. The performances are evaluated under two scenarios. Especially when a 3.4-dB back-off input power is applied, where severe PA nonlinearities are presented with the I/Q imbalance, the measured (left, right) adjacent channel power ratio is improved from (?19.9, ?19.6 dBc) to (?37.3, ?37.9 dBc), and the EVM is reduced from 22.1 to 3.4 %. For performance comparison, other Volterra-series-based DPDs such as memory polynomial, dynamic deviation reduction and generalized memory polynomial, are also extended to joint-DPD.
TL;DR: In this article, the authors proposed a postcompensation technique for radio frequency (RF) receivers employed in passive intermodulation (PIM) and weakly nonlinear devices characterization (low gain compression of the order of 0.1 dB).
Abstract: This paper proposes a technique to characterize and postcompensate for the dynamic nonlinearity exhibited by radio frequency (RF) receivers employed in passive intermodulation (PIM) and weakly nonlinear devices characterization (low gain compression of the order of 0.1 dB). The proposed technique begins by accurately characterizing the RF receiver’s nonlinearity using two-tone and multitone stimuli with swept frequency spacing and input power level. Then, a pruned Volterra series-based postcompensation model is devised to mitigate the dynamic weak nonlinear distortions exhibited by the receiver. The technique was validated in two steps. First, the capacity of the postcompensation technique to separate the RF receiver’s intermodulation from the device under test’s intermodulation was assessed. Second, its capacity to postcompensate for the dynamic nonlinearity exhibited by the RF receiver and to enhance its spurious-free dynamic range (SFDR) was assessed using digitally modulated stimuli. The application of the proposed technique reduced the third-order intermodulation (IMD3) of the RF receiver by an average of 10 dB. This technique can significantly relax the requirements on duplexers and tone injection techniques used to enhance the accuracy of PIM measurements. Furthermore, the receiver’s SFDR was extended by up to 15 dB.
14 Mar 2016
TL;DR: A wireless non-invasive blood glucose measurement system modeled via a fractional Volterra series by grouping the vector of the system parameters into three different subvectors for finding a solution of the optimization problem.
Abstract: This This paper proposes a wireless non-invasive blood glucose measurement system. The system is modeled via a fractional Volterra series. The estimation of the parameters in the model is formulated as a nonconvex optimization problem. By grouping the vector of the system parameters into three different subvectors, an iterative approach is proposed for finding a solution of the optimization problem. Experimental results show that the accuracy of the model is very high.