Low complexity output generalized memory polynomial model for digital predistortion of RF power amplifiers

Chen, Yu; Wang, Guangjiang; Liu, Yuanan

doi:10.1002/mmce.21465

Cited by 4 publications

(3 citation statements)

References 9 publications

(22 reference statements)

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“…For sandstorm weather conditions, the behaviors of nonlinearity become much more unstable and unpredictable. Many different models have been proposed in previous literature, such as Volterra series-based or generalized memory polynomials [17][18][19][20], and dynamic deviation reduction [21,22]. Most of the time, the model is constructed based on the device behavior to mitigate cross-coupling amongst signal channels.…”

Section: Introductionmentioning

confidence: 99%

A Novel Autoregressive Spectral Estimation-Based Neural Network Crest Factor Reduction Structure in Power Amplifiers of 5G Systems in Sandstorm Environment

Cao

Wang

Cai

2022

Scientific Programming

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In this paper, an autoregressive spectral estimation using a multilayered neural network is proposed to reduce the nonlinearities of 5G MIMO power amplifiers and increase the signal-transmitting qualities. The proposed method adopts the two-sided doubly exponential lattice algorithm to achieve the most suitable estimation for in-band compensation. Also, based on the iterative learning control method, a novel crest factor reduction digital predistortion is combined with the multilayered neural network. Based on the results, the proposed algorithm has increased the linearity and stability of the PA performance. The proposed method can improve the robustness of 5G MIMO wireless communication systems.

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Section: Introductionmentioning

confidence: 99%

A Novel Autoregressive Spectral Estimation-Based Neural Network Crest Factor Reduction Structure in Power Amplifiers of 5G Systems in Sandstorm Environment

Cao

Wang

Cai

2022

Scientific Programming

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“…A series of methods to reduce model complexity are proposed. [13][14][15] Bandwidth adaptation is one of the essential characteristics of next-generation communication systems. Cognitive radio requires the ability to adjust signal bandwidth, power, and several other parameters according to its environment and location.…”

Section: Introductionmentioning

confidence: 99%

Bandwidth adaptive behavioral model with dynamic structures for radio frequency power amplifiers

Peng

et al. 2021

Int J RF Mic Comp-Aid Eng

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To cope with the degradation of digital predistortion when bandwidth changes, a bandwidth adaptive behavioral model with dynamic structures for radio frequency (RF) power amplifiers (PAs) is proposed in this article. By applying the nonlinear post-compensation technology, PA's nonlinearity is decomposed and analyzed. Short-time Fourier transform and transfer functions are adopted to study the influence of bandwidth variation on the linear memory effect. Based on PA's memory effect analysis, a bandwidth adaptive model with dynamic structures and a coefficient update scheme with low complexity are proposed. Experiments carried on PAs show that the proposed model achieves similar linearization performance as other conventional models. Simultaneously, the proposed model has fewer coefficients and a reduction in the coefficient update of at least 50%.

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“…An adaptive basis direct learning algorithm was proposed in [18] for the linearization of power amplifiers having improved normalized mean square error and adjacent channel power ratios than the conventional direct learning method. A unique model discussed in [19] was output generalized memory polynomial (OGMP) model using previous output signal to characterize memory effects for digital predistortion of power amplifiers. By using the recursive prediction error minimization method, an algorithm based on adaptive indirect learning architecture of DPD was pro-posed in [20] for linearizing RF power amplifiers used in arising wideband correspondence frameworks.…”

Section: Introductionmentioning

confidence: 99%

Characterization of RF Power Amplifier for Narrow and Wide Band Memory Polynomial Implementations

Mohiuddin

Shaikh

Dahri

et al. 2021

QUEST Research Journal

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Linearly efficient RF power amplifiers have a tremendous role in wireless communication and radar systems as they lie at the front end of most RF systems. In today’s world of wireless communication, it is not an easy task to design a RF power amplifier that is linearly efficient. There are two main key challenges that one face for making RF power amplifier’s behavior linearly efficient. First is to characterize RF power amplifier’s coefficients smartly. Second is to propose an approach that works on input signal and make its behavior inverse to that of the designed amplifier behavior so that overall response of the system becomes linear. For countering first challenge, most advanced universally accepted algorithms like Memory Polynomial, Generalized Hammerstein, Cross-term Memory Polynomial and Cross-term Hammerstein are implemented to design RF power amplifier models. For countering second challenge, latest DPD algorithms are implemented which make net response of a system linear. The memory models for modelling RF power amplifier are categorized for narrowband and wideband applications. The narrowband power amplifier models include Memory Polynomial and Cross-term Memory Polynomial models whereas wideband power amplifier models include Generalized Hammerstein and Cross-term Hammerstein models. In this paper, various performance indicators like Standard Deviation (SD), Third Order Intercept (TOI), Intermodulation Distortion Products (IMD3), Modulation Error Ratio (MER), Spurious Free Dynamic Range (SFDR) and Error Vector Magnitude (EVM) are used to characterize RF power amplifier for both narrow and wide band applications. The simulation results show that under narrowband applications, Cross-term Memory Polynomial model works best as it has least standard deviation and is also satisfying other performance parameters up to appreciable level with and without DPD algorithm implementation. While for wideband applications, Cross-term Hammerstein model satisfies the performance measuring parameters excellently.

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Low complexity output generalized memory polynomial model for digital predistortion of RF power amplifiers

Cited by 4 publications

References 9 publications

A Novel Autoregressive Spectral Estimation-Based Neural Network Crest Factor Reduction Structure in Power Amplifiers of 5G Systems in Sandstorm Environment

A Novel Autoregressive Spectral Estimation-Based Neural Network Crest Factor Reduction Structure in Power Amplifiers of 5G Systems in Sandstorm Environment

Bandwidth adaptive behavioral model with dynamic structures for radio frequency power amplifiers

Characterization of RF Power Amplifier for Narrow and Wide Band Memory Polynomial Implementations

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