Nonlinear Compensation of High Power Amplifier Distortion for Communication Using a Histogram-Based Method

Huang, Dongliang; Huang, Xinping; Leung, Henry

doi:10.1109/tsp.2006.881183

Cited by 19 publications

(8 citation statements)

References 32 publications

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“…Denoting by Φ( u ) the Gaussian cdf, which transforms a unit variance Gaussian variable into a uniform random variable in [0, 1], it is clear that Φ −1 ( U )is a unit variance Gaussian random variable. Then, a simple approximation of the inverse g of the nonlinear mapping function f is [13,14]. …”

Section: Methodsmentioning

confidence: 99%

Inverting Monotonic Nonlinearities by Entropy Maximization

2016

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This paper proposes a new method for blind inversion of a monotonic nonlinear map applied to a sum of random variables. Such kinds of mixtures of random variables are found in source separation and Wiener system inversion problems, for example. The importance of our proposed method is based on the fact that it permits to decouple the estimation of the nonlinear part (nonlinear compensation) from the estimation of the linear one (source separation matrix or deconvolution filter), which can be solved by applying any convenient linear algorithm. Our new nonlinear compensation algorithm, the MaxEnt algorithm, generalizes the idea of Gaussianization of the observation by maximizing its entropy instead. We developed two versions of our algorithm based either in a polynomial or a neural network parameterization of the nonlinear function. We provide a sufficient condition on the nonlinear function and the probability distribution that gives a guarantee for the MaxEnt method to succeed compensating the distortion. Through an extensive set of simulations, MaxEnt is compared with existing algorithms for blind approximation of nonlinear maps. Experiments show that MaxEnt is able to successfully compensate monotonic distortions outperforming other methods in terms of the obtained Signal to Noise Ratio in many important cases, for example when the number of variables in a mixture is small. Besides its ability for compensating nonlinearities, MaxEnt is very robust, i.e. showing small variability in the results.

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Section: Methodsmentioning

confidence: 99%

Inverting Monotonic Nonlinearities by Entropy Maximization

2016

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“…Fig. 5 compares the proposed method with several parametric models, such as the static nonlinear, memory polynomial, generalized memory polynomial [4], Multi-LUT [21], Volterra [2] and Kautz-Volterra [22] models, and non-parametric models, such as the Histogram model [8]. Different points correspond to different model settings (nonlinearity order and memory depth) being tested.…”

Section: (13)mentioning

confidence: 99%

Finding Structural Information About RF Power Amplifiers Using an Orthogonal Nonparametric Kernel Smoothing Estimator

Zenteno

Khan

Isaksson

et al. 2016

IEEE Trans. Veh. Technol.

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A non-parametric technique for modeling the behavior of power amplifiers is presented. The proposed technique relies on the principles of density estimation using the kernel method and is suited for use in power amplifier modeling. The proposed methodology transforms the input domain into an orthogonal memory domain. In this domain, non-parametric static functions are discovered using the kernel estimator. These orthogonal, non-parametric functions can be fitted with any desired mathematical structure, thus facilitating its implementation. Furthermore, due to the orthogonality, the non-parametric functions can be analyzed and discarded individually, which simplifies pruning basis functions and provides a tradeoff between complexity and performance. The results show that the methodology can be employed to model power amplifiers, therein yielding error performance similar to state-of-the-art parametric models. Furthermore, a parameter-efficient model structure with 6 coefficients was derived for a Doherty power amplifier, therein significantly reducing the deployment's computational complexity. Finally, the methodology can also be well exploited in digital linearization techniques.Index Terms-Power amplifier, non-parametric model, kernel, basis functions, power amplifier linearization, Digital predistortion.

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“…In addition, the proposed technique also eliminates the need for decoupling these imbalances. Statistics-based methods have been shown to perform reasonably well in the case of other front-end impairments such as power amplifier nonlinearity [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Blind Compensation of I/Q Impairments in Wireless Transceivers

Aziz

Ghannouchi

Helaoui

2017

Sensors

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The majority of techniques that deal with the mitigation of in-phase and quadrature-phase (I/Q) imbalance at the transmitter (pre-compensation) require long training sequences, reducing the throughput of the system. These techniques also require a feedback path, which adds more complexity and cost to the transmitter architecture. Blind estimation techniques are attractive for avoiding the use of long training sequences. In this paper, we propose a blind frequency-independent I/Q imbalance compensation method based on the maximum likelihood (ML) estimation of the imbalance parameters of a transceiver. A closed-form joint probability density function (PDF) for the imbalanced I and Q signals is derived and validated. ML estimation is then used to estimate the imbalance parameters using the derived joint PDF of the output I and Q signals. Various figures of merit have been used to evaluate the efficacy of the proposed approach using extensive computer simulations and measurements. Additionally, the bit error rate curves show the effectiveness of the proposed method in the presence of the wireless channel and Additive White Gaussian Noise. Real-world experimental results show an image rejection of greater than 30 dB as compared to the uncompensated system. This method has also been found to be robust in the presence of practical system impairments, such as time and phase delay mismatches.

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Nonlinear Compensation of High Power Amplifier Distortion for Communication Using a Histogram-Based Method

Cited by 19 publications

References 32 publications

Inverting Monotonic Nonlinearities by Entropy Maximization

Inverting Monotonic Nonlinearities by Entropy Maximization

Finding Structural Information About RF Power Amplifiers Using an Orthogonal Nonparametric Kernel Smoothing Estimator

Blind Compensation of I/Q Impairments in Wireless Transceivers

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