Grading Optimization for Dimensions-Reduced Orthogonal Volterra DPD
High-speed optical communication systems may suffer from a combination of impairments such as memory effect and nonlinear behavior of the optoelectronic components. Nonlinear digital pre-distortion (DPD) is one of the well-known technique to alleviate these effects. As typical implementation of Volt...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Photonics Journal |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8913473/ |
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| Summary: | High-speed optical communication systems may suffer from a combination of impairments such as memory effect and nonlinear behavior of the optoelectronic components. Nonlinear digital pre-distortion (DPD) is one of the well-known technique to alleviate these effects. As typical implementation of Volterra-based DPD is considered complex and consumes high power, more efficient orthogonal-based Volterra series representation has been proposed. Previous works offered ways to perform efficient grading of the most dominant dimensions based on the combination of the dimensions variances and the signal projection. Here, it is shown that normalization of the data dynamic range further improves this method and decreases significantly the number of required dimensions. Using normalization combined with the previous methods, maximizes the DPD performance by means of error vector magnitude (EVM) and bit error rate (BER), while minimizing the DPD complexity in the terms of required series dimensions. Extensive simulation and lab measurements indicate a potential saving of up to 87% in the number of dimensions with a negligible performance penalty. |
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| ISSN: | 1943-0655 |