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Residual powers with respect to the iteration index after the different DSICs, for (a) 20 MHz, (b) 40 MHz, and (c) 80 MHz instantaneous bandwidths.
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In this paper, we present a class of cascaded nonlinear models for complex-valued system identification, aimed at baseband modeling of nonlinear radio systems. The proposed models consist of serially connected elementary linear and nonlinear blocks, with the nonlinear blocks implemented as uniform spline-interpolated look-up tables (LUT) and the li...
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... order to evaluate the convergence properties of the algorithms, Fig. 8 shows the residual powers of the received signals after cancellation, for the three bandwidths mentioned above. All the models are essentially achieving a similar convergence speed, despite the proposed spline based models not using orthogonalization. Altogether, the results show that excellent digital cancellation can be obtained with ...
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Citations
... In current paper we focus on the digital second order intermodulation (IMD2) cancellation. Traditional self-interference (SI) cancellation is provided by adaptation of behavioral models, such as memoryless, memory polynomials, spline polynomials etc [6][7][8]. ...
Due to the limited isolation of duplexer's stopband transceivers operating in frequency division duplex (FDD) encounter a leakage of the transmitted signal onto the receiving path. Leakage signal with the combination of the second-order nonlinearity of the low noise amplifier (LNA) and receiver down-conversion mixer may lead to second-order intermodulation distortion (IMD2) generation thus greatly reducing the receiver sensitivity. Cancellation of undesirable interferences based on adaptation of traditional models such as memoryless and memory polynomials, spline polynomial based Hammerstein and Wiener-Hammerstein models proved its efficiency in case of well-known nonlinearity nature. On the other hand, currently there is an intensive research in the field of nonlinearity detection by means of neural network (NN) structures. NN-based IMD cancellers are effective in the case of unknown interference content due to their high generalization ability. Therefore, NN approach can provide universal model, which is capable of IMD suppression even in case it is hard to separate intermodulation products generated by LNA, down-conversion mixer or even power amplifier in transmitter path. Nevertheless, such structures suffer from high complexity and can`t be implemented in hardware. Current paper presents low-complexity feed-forward NN-based model, which successfully competes with traditional architectures in terms of computational complexity. The testbench results demonstrate the acceptable performance of provided model, which can be equal to the polynomial nonlinear canceler's performance at a reduced computational cost. Current paper provides performance and required resources comparison of traditional memory polynomial-based scheme and NN-based model for IMD2 cancellation.
... For example, in [7], authors exploit a dynamic moving average method, whereas in [8] an artificial neural network structure, which is trained by the magnitude and phase of the base-band samples, is used for this purpose. Spline-based interpolation is another popular approach that can achieve high accuracy in modeling the memoryless nonlinear behavior [9]. ...
p>This paper presents a novel method for identification of the sub-system parameters of a Wiener-Hammerstein nonlinear (WHNL) system that is used for modeling RF Power Amplifier characteristics. The proposed method first isolates the overall linear system from the memoryless nonlinearity by exploiting the Bussgang Decomposition method. Then, DFT analysis is used for the estimation of the inner linear system. Finally, the outer linear system parameters are updated based on the inner system estimation. The estimated systems are then used to model the target system for an in-band-full-duplex (IBFD) scenario. Performance of self-interferene cancellation (SIC) has been evaluated under the existence of Signal-of-Interest (SoI). Error vector magnitude (EVM) metric of the SoI is used to compare with a half-duplex (HD) receiver under various inner linear system parameters. SIC performance has been examined with respect to the changing power levels of the SoI and self-interference signal for various delay and gain values of a practical two-tap inner linear system. The benefit of modeling the inner linear system has been revealed by comparing the SIC performance with Hammerstein nonlinear model. The performance has also been compared to well known black box models such as Generalized Memory Polynomial (GMP) and Artificial Neural Networks (ANN).</p
... For example, in [7], authors exploit a dynamic moving average method, whereas in [8] an artificial neural network structure, which is trained by the magnitude and phase of the base-band samples, is used for this purpose. Spline-based interpolation is another popular approach that can achieve high accuracy in modeling the memoryless nonlinear behavior [9]. ...
p>This paper presents a novel method for identification of the sub-system parameters of a Wiener-Hammerstein nonlinear (WHNL) system that is used for modeling RF Power Amplifier characteristics. The proposed method first isolates the overall linear system from the memoryless nonlinearity by exploiting the Bussgang Decomposition method. Then, DFT analysis is used for the estimation of the inner linear system. Finally, the outer linear system parameters are updated based on the inner system estimation. The estimated systems are then used to model the target system for an in-band-full-duplex (IBFD) scenario. Performance of self-interferene cancellation (SIC) has been evaluated under the existence of Signal-of-Interest (SoI). Error vector magnitude (EVM) metric of the SoI is used to compare with a half-duplex (HD) receiver under various inner linear system parameters. SIC performance has been examined with respect to the changing power levels of the SoI and self-interference signal for various delay and gain values of a practical two-tap inner linear system. The benefit of modeling the inner linear system has been revealed by comparing the SIC performance with Hammerstein nonlinear model. The performance has also been compared to well known black box models such as Generalized Memory Polynomial (GMP) and Artificial Neural Networks (ANN).</p
... For example, in [7], authors exploit a dynamic moving average method, whereas in [8] an artificial neural network structure, which is trained by the magnitude and phase of the base-band samples, is used for this purpose. Spline-based interpolation is another popular approach that can achieve high accuracy in modeling the memoryless nonlinear behavior [9]. ...
... Li et al [6] reported an adaptive estimator for the considered system where the adaptive law is designed through the usage of the parameter error and initial value. An efficient gradient estimation method is given in [13] by Campo et al. for the sandwich model, and the model is used to build a dynamic model of a nonlinear radio system. Although the aforementioned-published identification algorithms have achieved good results in sandwich system identification, these algorithms focus on the steady-state performance of sandwich system parameter identification, i.e., t ! ...
... Remark 1. The variable fading factor κ t in (13) is used to lift the utilization of the "new" system data other than manually tuned constant forgetting factor, which can improve the so-called data submerge problem of identification community. Go a step further, based on (11)-(13), the vector V t is defined by the following form: ...
The vast majority of reports mainly focus on the steady-state performance of parameter estimation. Few findings are reported for the instantaneous performance of parameter estimation because the instantaneous performance is difficult to quantify by using the design algorithm, for example, in the initial stage of parameter estimation, the error of parameter estimation varies in a specific region on the basis of the user’s request. With that in mind, we design an identification algorithm to address the transient performance of the parameter estimations. In this study, the parameter estimation of nonlinear sandwich system is studied by using the predefined constraint technology and high-effective filter. To achieve the above purpose, the estimation error information reflecting the transient performance of parameter estimation is procured using the developed some intermediate variables. Then, a predefined constraint function is used to prescribe the error convergence boundary, in which the convergence rate is lifted. An error equivalent conversion technique is then employed to obtain the transformed error data for establishing an parameter adaptive update law, in which the estimation error convergence and the predefined domain can be achieved. In comparison with the available estimation schemes, the good instantaneous performance is obtained on the basis of the numerical example and practical process results.
... Since the significant contributor to nonlinearity is HPA, it is essential to describe the HPA model correctly. In the feedback path, the level of noise or sensitivity of the DPD is with the low-noise amplifier (LNA) as discussed in Campo et al. [44]. Thus the LNA plays a significant role in the observation path. ...
... Here f itness min indicates the best values of the objective function, f itness max indicates the worst values of the objective function,f itness i represents current value of objective function. From (44), spider positions with low pheromone rates are updated as ...
The greatest challenge faced by the Simultaneous Wireless Information and Power Transfer (SWIPT) system during implementation is hardware impairment.This article proposes a bio-inspired digital pre-distortion scheme to overcome the high power amplifier nonlinearity and in-phase and quadrature imbalances in the SWIPT system.Here, the memory polynomial model characterises the high power amplifier. The digital pre-distortion algorithm uses the latest bio-inspired methods: Dingo Optimization, Jumping Spider Optimization, Seagull Optimization, Mexican Axolotl Optimization, and Black Widow Optimization. The power conversion efficiency, harvested energy, and rate energy region at the receiver side analyse the efficiency of bio-inspired digital pre-distortion enabled SWIPT. Among the various bio-inspired algorithms, the Seagull Optimisation Algorithm gave a maximum harvested energy of 35.95 μW, keeping a Bit Error Rate of 1.33×10
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for the 32-QAM scheme. The Seagull Optimisation Algorithm also showed a maximum improvement of 6.45% in power conversion efficiency compared to the conventional digital pre-distortion scheme.
... Wiener-Hammerstein Model [40], [91], [94] ...
... Generalized Memory Polynomial [39], [94], [97] Y GM P (n) = M m=0 Ka k=1 a mk .X GM P (n − m)|x(n − m)| k−1 ...
... Meanwhile, from (5), another pruning edition of VoS is in the form of the Wiener-Hammerstein (W-H) or H-W model, which is the serial collaboration of each other where three-block orientation is equipped to conjunct an LTI system which precedes the nonlinear LUT followed by another LTI filter, and vice versa, as shown in Fig. 4(d). This topology enables the simple gradient-based learning algorithm to cover several iterations according to the nonlinearities identification of different systems such as filters, microwave mixers, and PAs with full-duplex mode for large coefficients modeling of real-time MIMO applications [40], [93], [94]. b) Memoryless: Likewise, those mentioned above, LUTbased memory-less VoS models (as illustrated in Fig. 4(e)) are exempted from complex coefficient analytics. ...
The next-generation (5G/6G) wireless communication aims to leapfrog the currently occupied sub-6 GHz spectrum to the wideband millimeter-wave (MMW) spectrum. However, MMW spectrums with high-order modulation schemes drive the power amplifier (PA) at significant back-off, causing severe nonlinear distortions, thus deteriorating the transceiver’s (TRXs) modulation process. Typically, the TRX efficacy is quantified with standardized linearization matrices, which take advantage of different predistortion (PD) schemes to handle deep compression of the PA. In this regard, TRX baseband signals are mostly linearized in the digital domain, where digitally controlled linearization needs higher sampling rates due to increasing MMW bandwidths to compensate for intermodulation (IMD) products, resulting in increased system cost and power consumption. Alternately, the digitally controlled analog-based linearization, i.e., the hybridization of digital predistortion (DPD) and analog predistortion (APD), is highly productive and cost-effective for fulfilling the linearized energy-efficient design vision of MMW networks. Therefore, this paper puts an extensive spotlight on the progress in PD-based linearization for 5G and beyond communications. It first provides background information on the advancements of PD schemes through recent surveys, then classifies the general roadmap of PD waveform processing across the TRX system models as preliminary. After this, we present three prominent PD architectures and their design approaches with intrinsic performance metrics. Finally, we explore four case studies encompassing PD operation under certain nonlinear constraints of different communication schemes. We examine the suitability of PD-based linearization solutions, both existing and proposed till the first quarter of 2022, and identify the potential prospects in this domain.
... Digital predistortion (DPD) is a widely used and effective technology for PA linearization. Many conventional DPD models considering both the memory effect and nonlinear effect have been proposed, e.g., the generalized memory polynomial (GMP) model [1] and cascaded model [2]. ...
In this paper, a novel adaptive time alignment method for digital predistortion (DPD) is proposed to deal with the linearization of power amplifiers (PAs) when severer group delay distortion (GDD) exists in the feedback path. Group delay mismatch significantly influences the precision during the extraction of the DPD model’s coefficients. Based on the mathematical analysis of group delay, the proposed method can weaken the effects of GDD. Experiments’ results confirm that the DPD model with the proposed method has better performance than the DPD model only with the traditional time alignment method.
... Complex-valued adaptive filtering algorithm has a wide range of engineering applications in radio systems [1], system identification [2], environment signal processing [3], and other fields. Generally speaking, complex-valued adaptive filtering algorithm is an extension of the real-valued adaptive filtering algorithm. ...
Augmented IIR filter adaptive algorithms have been considered in many studies, which are suitable for proper and improper complex-valued signals. However, lots of augmented IIR filter adaptive algorithms are developed under the mean square error (MSE) criterion. It is an ideal optimality criterion under Gaussian noises but fails to model the behavior of non-Gaussian noise found in practice. Complex correntropy has shown robustness under non-Gaussian noises in the design of adaptive filters as a similarity measure for the complex random variables. In this paper, we propose a new augmented IIR filter adaptive algorithm based on the generalized maximum complex correntropy criterion (GMCCC-AIIR), which employs the complex generalized Gaussian density function as the kernel function. Stability analysis provides the bound of learning rate. Simulation results verify its superiority.
... In 2021, Campo et al. proposed SAF with cascade structure [23], which was used for compensation of nonlinear systems and interference cancellation of full-duplex system communication, and reached the standard of output signal linearization. Spline adaptive filter for nonlinear system identification performance is good, but its convergence time and performance is affected by the data and the number of iterations, so many scholars put forward a new algorithm to optimize a spline adaptive filter, the main way according to the data characteristics of the corresponding iterative method is proposed and calculated according to the proposed optimization computing process of convergence coefficient iteration algorithm, etc., Moreover, the convergence performance of the system is improved while the convergence time is reduced, and the nonlinear system can be identified more accurately [24] - [29]. ...
To improve the modeling accuracy and digital predistortion compensation effect, this paper proposed a kind of polynomial based spline adaptive filter (SAF) power amplifier (PA) behavioral modeling method. The proposed model can solve the shortcomings of the traditional memory polynomial model (MP), such as higher nonlinear order, highly coefficient calculation difficulty, poor hardware stability, low modeling accuracy and poor digital predistortion compensation. Meanwhile, the proposed SAF based model can avoid the pseudo-inversion operation of the matrix when calculating the coefficients through the indirect learning structure, which can improve the compensation effect of digital predistortion. In order to compare the performance of the different models, the modeling processes of original filtering model, MP model and dynamic variation reduction (Dynamic Deviation Reduction, DDR) model based on SAF method (OSAF, MPSAF, DDRSAF) are presented respectively. The proposed models are verified by a class F PA, which are stimulated by a set of measured input and output data of an LTE signal with 20 MHz bandwidth. The measured results shows that compared with the conventional MP model, the proposed DDRSAF model presents the best model performance and improves the modeling accuracy by 6.6dB while maintaining the similar computational complexity, and the ACPR compensation effect increases by 19.58dB and 20.47dB respectively, which have the important significance for practical engineering application.
