Computational complexity of Volterra based nonlinearcompensators

Tsimbinos, J.; Lever, K.V.

doi:10.1049/el:19960544

Cited by 48 publications

(23 citation statements)

References 2 publications

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“…It is noticeable that the optimal poles of the inverse model have the same positions as those of the direct model, which indicates that the memory effects of the direct and inverse functions are the same. This result is somewhat unexpected since inverse functions are more complex and have longer memory than direct ones, in a general case [14]. The inverse, K, of a nonlinear dynamic system, eq.…”

Section: Direct and Inverse Kv Modelsmentioning

confidence: 97%

“…Block structures have been shown to be good approximations of the PA's inverse [13], although the problem of finding an approximate inverse function is more complicated than finding an approximate direct model. The inverse model is also nonlinear with memory, but has, in many cases, longer memory compared to the direct model of the same system [14].…”

Section: Introductionmentioning

confidence: 99%

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A parameter-reduced volterra model for dynamic RF power amplifier modeling based on orthonormal basis functions

Isaksson

Rönnow

2007

Int J RF and Microwave Comp Aid Eng

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A nonlinear dynamic behavioral model for radio frequency power amplifiers is presented. It uses orthonormal basis functions, Kautz functions, with complex poles that are different for each nonlinear order. It has the same general properties as Volterra models, but the number of parameters is significantly smaller. Using frequency weighting the out-of-band model error can be reduced. Using experimental data it was found that the optimal poles were the same for different input powers and for the different nonlinear orders. The optimal poles were also the same for direct and inverse models, which could be explained theoretically to be a general property of nonlinear systems with negligible linear memory effects. The model can be used as either a direct or inverse model with the same model error for power amplifiers with negligible linear memory effects.

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Section: Direct and Inverse Kv Modelsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

A parameter-reduced volterra model for dynamic RF power amplifier modeling based on orthonormal basis functions

Isaksson

Rönnow

2007

Int J RF and Microwave Comp Aid Eng

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“…Similarly, the total energy of the distortions could be calculated accurately if the order of the FIR bandstop filter B is big enough. However, large B, M and N result in increased computational complexity [6] and large data length requirements for estimation purposes. In practice, the selection of appropriate B, M and N involves a tradeoff between good nonlinear compensation performance and low computational complexity.…”

Section: Model Parameters' Lms Identificationmentioning

confidence: 99%

Blind Identification and Digital Calibration of Volterra Model Based on Least Mean Square Method

Peng¹,

Hai-hua²,

Chen³

2013

Proceedings of the 2nd International Symposium on Computer, Communication, Control and Automation

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“…Firstly, it is enlarged to a 32x32 square matrix. Secondly , it is divided into two hundred and fifty-six 2x2 sub square matrixes A pq • The sub square matrixes of L and V are obtained as: [4] can be written as: Then, (I) can be rewritten as: (5) Increased computational complexity will be caused by Large N, Band M will [5], as well as large data length requirements. Through simulation and analysis on the memory depth and order of the Volterra series, we set the coefficients of the identification and calibration system with FPGA implementation as follows .…”

Section: A Blind Identification Technique Based On Rls Algorithmmentioning

confidence: 99%

FPGA implementation for a recursive least square algorithm

Peng

Guocang

Hai-hua

et al. 2013

2013 2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA)

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A recursive least square algorithm is implemented in this paper. Memory Nonlinearity of digital receiver is compensated by using a blind identification algorithm based on nonlinear model. A least-squared blind identification criterion to minimize all the energy of the nonlinearity in the receiver's output signal is implemented under circumstances of not knowing the information of the receiver's input signal. The orders and memory depths of the Volterra model are tested and updated automatically. A double-precision arbitrarydimensional matrix inversion module is implemented in the requirement of the least-squared method. The digital post calibration processes in real-time. Experimental results on the actual nonlinear circuit illustrate the validity of the implemented technique.

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Computational complexity of Volterra based nonlinearcompensators

Cited by 48 publications

References 2 publications

A parameter-reduced volterra model for dynamic RF power amplifier modeling based on orthonormal basis functions

A parameter-reduced volterra model for dynamic RF power amplifier modeling based on orthonormal basis functions

Blind Identification and Digital Calibration of Volterra Model Based on Least Mean Square Method

FPGA implementation for a recursive least square algorithm

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