Comparison of direct learning and indirect learning predistortion architechtures

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review


    Power amplifiers in a communication system are inherently nonlinear. Digital predistorters can compensate these nonlinearity effects. In this paper, two memory polynomial predistorters including direct and indirect learning architectures are compared with each other. To the best of our knowledge, no similar comparisons have been published. Both of these architectures are special cases of the self-tuning control. We have modeled predistorters and analysed nonlinear effects of a power amplifier and their digital compensation by using Matlab. Simulation results show that the memory polynomial model has convergence problems at large amplitudes and also problems of accuracy of representation. We observed that the results of the compensation depend also on the amplitude, not only on the frequency. The results of the linearisation show that the direct learning architecture achieves a better performance in almost all cases.
    Original languageEnglish
    Title of host publication2008 IEEE International Symposium on Wireless Communication Systems
    PublisherIEEE Institute of Electrical and Electronic Engineers
    ISBN (Electronic)978-1-4244-2489-4
    ISBN (Print)978-1-4244-2488-7
    Publication statusPublished - 30 Dec 2008
    MoE publication typeA4 Article in a conference publication


    • predistortion
    • power amplifier
    • constellation diagram
    • polynomials
    • nonlinear distortion
    • quadrature amplitude modulation


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