• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.19 no.11
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• pp.1254-1264
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• 2008

This paper suggests an extended memory polynomial model that improves accuracy in modeling memory effects of RF power amplifiers(PAs), and verifies effectiveness of the suggested method. The extended memory polynomial model includes cross-terms that are products of input terms that have different delay values to improve the limited accuracy of basic memory polynomial model that includes the diagonal terms of Volterra kernels. The complexity of the memoryless model, memory polynomial model, and the suggested model are compared. The extended memory polynomial model is represented with a matrix equation, and the Volterra kernels are extracted using least square method. In addition, the structure of digital predistorter and digital signal processing(DSP) algorithm based on the suggested model and indirect learning method are proposed to implement a digital predistortion linearization. To verify the suggested model, the predicted output of the model is compared with the measured output for a 10W GaN HEMT RF PA and 30 W LDMOS RF PA using 2.3 GHz WiBro input signal, and adjacent-channel power ratio(ACPR) performance with the proposed digital predistortion is measured. The proposed model increases model accuracy for the PAs, and improves the linearization performance by reducing ACPR.

• 한국정보통신설비학회:학술대회논문집
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• 2008.08a
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• pp.218-222
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• 2008

RF power amplifier requires linearization in order to reduce adjacent channel interference. And most of the existing linearization algorithms assume that a PA has memory-less nonlinearity. But for the wider bandwidth signal, the memory effect of PA cannot be ignored. This paper investigates digital pre-distortion by use of a memory polynomial model which compensates for amplifier nonlinearity and memory effect. The look-up table based implementation scheme is used to reduce the computational complexity of the pre-distortion block. The linearization performance is demonstrated on wideband CDMA signal and class AB high power amplifier.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.20-23
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• 2020

This research is to design an effective prefetching method required for DRAM-PCM hybrid main memory systems especially used for big data applications and massive-scale computing environment. Conventional prefetchers perform well with regular memory access patterns. However, workloads such as graph processing show extremely irregular memory access characteristics and thus could not be prefetched accurately. Therefore, this research proposes an efficient dynamical prefetching algorithm based on the regression method. We have designed an intelligent prefetch engine that can identify the characteristics of the memory access sequences. It can perform regular, linear regression or polynomial regression predictive analysis based on the memory access sequences' characteristics, and dynamically determine the number of pages required for prefetching. Besides, we also present a DRAM-PCM hybrid memory structure, which can reduce the energy cost and solve the conventional DRAM memory system's thermal problem. Experiment result shows that the performance has increased by 40%, compared with the conventional DRAM memory structure.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.5 s.108
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• pp.408-415
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• 2006

This paper suggests an improved system-level model of RF power amplifiers(PAs) including memory effects, and validates the suggested model by analyzing the power spectral density of the output signal with a predistortion linearizer. The original three-box(Wiener-Hammerstein) model uses input and output filters to capture RF frequency response of PAs. The adjacent spectral regrowth that occurs in three-box model can be perfectly removed by Hammerstein structure predistorter. However, the predistorter based on Hammerstein structure achieves limited performance in real PA applications due to other memory effects except RF frequency response. The spectrum of the output signal can be predicted accurately using the suggested model that changes a memoryless block in a three-box model with a memory polynomial. The proposed model accurately predicts the output spectrum density of PA with Hammerstein structure predistorter with less than 2 dB errors over ${\pm}30$ MHz adjacent channel ranges for IEEE 802.11 g WLAN signal.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.11
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• pp.1220-1228
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• 2010

In this paper, we suggest a scheme to develop an efficient discrete nonlinear model based on polynomial structure for a RF power amplifier(PA). We describe a procedure to extract a discrete nonlinear model such as Taylor series or memory polynomial by sampling the input and output signal of RF PA. The performance of the model is analyzed varying the model parameters such as sample rate, nonlinear order, and memory depth. The results show that the relative error of the model is converged if the parameters are larger than specific values. We suggest an efficient modeling scheme considering complexity of the discrete model depending on the values of the model parameters. Modeling efficiency index(MEI) is defined, and it is used to extract optimum values for the model parameters. The suggested scheme is applied to discrete modeling of various RF PAs with various input signals such as WCDMA, WiBro, etc. The suggested scheme can be applied to the efficient design of digital predistorter for the wideband transmitter.

• Wind and Structures
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• v.7 no.2
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• pp.89-106
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• 2004

The response of tall buildings to gust buffeting is usually evaluated assuming that the structural damping is of a viscous nature. In addition, when dampers are incorporated in the design to mitigate the response, their effect is allowed for increasing the building modal damping ratios by a quantity corresponding to the additional energy dissipation arising from the presence of the devices. Even though straightforward, this procedure has some degree of inaccuracy due to the existence of a memory effect, associated with the damping mechanism, which is neglected by a viscous model. In this paper a more realistic viscoelastic model is used to evaluate the response to gust buffeting of tall buildings provided with energy dissipation devices. Both cases of viscous and hysteretic inherent damping are considered, while for the dampers a generic viscoelastic behaviour is assumed. The Laguerre Polynomial Approximation is used to write the equations of motion and find the frequency response functions. The procedure is applied to a 25-story building to quantify the memory effects, and the inaccuracy arising when the latter is neglected.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.679-682
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• 1998

This paper presents the VLSI implementation of RS(reed-solomon) decoder using the Modified Euclidean Algorithm(hereafter MEA) for DVD(Digital Versatile Disc) and CD(Compact Disc). The decoder has a capability of correcting 8-error or 16-erasure for DVD and 2-error or 4-erasure for CD. The technique of polynomial evaluation is introduced to realize syndrome calculation and a polynomial expansion circuit is developed to calculate the Forney syndrome polynomial and the erasure locator polynomial. Due to the property of our system with buffer memory, the MEA architecture can have a recursive structure which the number of basic operating cells can be reduced to one. We also proposed five criteria to determine an uncorrectable codeword in using the MEA. The overall architecture is a simple and regular and has a 4-stage pipelined structure.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.3
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• pp.312-319
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• 2011

In this paper, a new linearization algorithm for DPD(Digital PreDistorter) is suggested. This new algorithm uses DFP(Davidon-Fletcher-Powell) method. This algorithm is more accurate than that of the existing algorithms, and this method renew the best-fit value in every routine with out setting the initial value of step-size. In modeling power amplifier, the memory polynomial model which can model the memory effect of the power amplifier is used. And the overall structure of linearizer is based on an indirect learning architecture. In order to verify for performance of proposed algorithm, we compared with LMS(Least Mean-Squares), RLS(Recursive Least squares) algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.2C
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• pp.191-199
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• 2006

In this paper, we propose a method to implement a predistorter of the $rho$-th order inverse filter structure to prevent signal distortion and spectral re-growth due to the high PAPR (peak-to-average ratio) of the OFDM signals and the non-linearity of high-power amplifiers. We model the memory-less non-linearity of the high-power amplifier with a polynomial model and utilize the inverse of the model, the $rho$-th order inverse filter, for the predistorter. Once the non-linearity is modeled with a polynomial, since we can determine the $rho$-th order inverse filter only with the coefficients of the polynomial, large memory is not required. To update the coefficients of the non-linear high-power amplifier model, we can use LMS or RLS algorithms. The convergence speed is high since the number of coefficients is small, and the computation is simple since manipulation of complex numbers is not necessary.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.58-65
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• 2021

Meta-heuristic algorithms have been developed to efficiently solve difficult problems and obtain a global optimal solution. A common feature mimics phenomenon occurring in nature and reliably improves the solution through repetition. And at the same time, the probability is used to deviate from the regional optimal solution and approach the global optimal solution. This study compares the algorithm created based on the above common points with existed SA and HS to show advantages in time and accuracy of results. Existing algorithms have problems of low accuracy, high memory, long runtime, and ignorance. In a two-variable polynomial, the existing algorithms show that the memory increases and the accuracy decrease. In order to improve the accuracy, the new algorithm increases the number of initial inputs and increases the efficiency of the search by introducing a direction using vectors. And, in order to solve the optimization problem, the results of the last experiment were learned to show the learning effect in the next experiment. The new algorithm found a solution in a short time under the experimental conditions of long iteration counts using a two-variable polynomial and showed high accuracy. And, it shows that the learning effect is effective in repeated experiments.