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Memory polynomial with shaped memory delay profile and modeling the thermal memory effect

2013 IEEE 20th International Conference on Electronics, Circuits, and Systems (ICECS), 2013
This paper presents a proposal for a new memory polynomial modeling technique with non-uniform delay taps to capture the thermal memory effects in power amplifiers. In the proposed modeling structure, each order of the memory polynomial is assigned a different memory delay.
Ahmet Hayrettin Yüzer, Seyed Aidin Bassam, Fadhel M. Ghannouchi, Simsek Demir   +3 more
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Theoretical Characterization of Memory Polynomial Models With Gaussian Inputs

IEEE Signal Processing Letters, 2009
We consider distortion of a Gaussian signal by a memory polynomial system and prove a theorem about a linear representation of the output. As an application example, we analyze an OFDM system with a nonlinear high power amplifier at the transmitter.
N Y Ermolova, O Tirkkonen, Olav Tirkkonen   +2 more
exaly   +2 more sources

Calculating polynomial zeros on a local memory parallel computer

Parallel Computing, 1989
Abstract We consider the calculation, on a local memory parallel computer, of all the zeros of an n th degree polynomial P n ( x ) which has real coefficients. We describe a generic parallel algorith, which approximates all the zeros simultaneously and we give three specific examples of this algorithm which have orders of convergence two, three
exaly   +2 more sources

Simplified Memory Polynomial modelling of Power Amplifier

2015 International Conference and Workshop on Computing and Communication (IEMCON), 2015
Accurate Power Amplifier modelling is of major concern for wireless communication system designers. Volterra series is most suitable to represent the actual behavior of non-linear power amplifier with memory. But as the order of memory polynomial increases, the number of Volterra terms also increases, resulting in the implementation complexity of ...
Amandeep Singh Sappal
exaly   +2 more sources

Frequency Identification of a Memory Polynomial Model for PA Modeling [PDF]

2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2022
In this paper, we describe the validation of the modeling of a power amplifier. The model used is a memory polynomial model. We propose a method of identification in the frequency domain, from characteristic points (in amplitude and in phase) which are ...
Stanislas Dubois, Bruno Lelong, Jean-Michel Hode, Guillaume Ferré, Dominique Dallet   +4 more
openaire   +2 more sources

Low-rate identification of memory polynomials

2014 IEEE International Symposium on Circuits and Systems (ISCAS), 2014
We propose a new approach for the low-rate identification of memory polynomials (MPs), which are frequently used to model RF power amplifiers (PAs). Based on ideas from the finite rate of innovation framework, we find the coefficients of the MP in the frequency domain, which requires a relatively small number of measurements (samples), commensurate ...
Nikolaus Hammler, Yonina C. Eldar, Boris Murmann   +2 more
openaire   +1 more source

A Polynomial-Time Algorithm for Memory Space Reduction

International Journal of Parallel Programming, 2005
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yonghong Song, Cheng Wang 0019, Zhiyuan Li 0001   +2 more
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Triangular Memory Polynomial Predistorter

2009 5th International Conference on Wireless Communications, Networking and Mobile Computing, 2009
To predistort a power amplifier nonlinear with memory effects, a triangular memory polynomial (TMP) predistorter is presented based on the traditional memory polynomial predistorter. The coefficients of the predistorter can be directly extracted from an offline system identification process using an open-loop structure.
Cuiping Yu, Yuanan Liu, Shulan Li
openaire   +1 more source

Polynomial Trend Regression With Long‐memory Errors

Journal of Time Series Analysis, 2005
Abstract.  For a time series generated by polynomial trend with stationary long‐memory errors, the ordinary least squares estimator (OLSE) of the trend coefficients is asymptotically normal, provided the error process is linear. The asymptotic distribution may no longer be normal, if the error is in the form of a long‐memory linear process passing ...
Ho, Hwai-Chung, Hsu, Nan-Jung
openaire   +2 more sources