2023
DOI: 10.1109/access.2023.3287776
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Bandwidth, Power and Carrier Configuration Resilient Neural Networks Digital Predistorter

Abstract: This paper proposes a neural networks predistorter based on the bidirectional long-short-term memory (BiLSTM) structure. The proposed predistorter was trained while ensuring that it captures the full intrinsic behavior of the device under test including its memory effects and nonlinear distortions. For this purpose, the device under test was characterized while operating at peak power level with a test signal that emulates strong memory effects. Extensive experimental validation carried on a commercial Gallium… Show more

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Cited by 3 publications
(1 citation statement)
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“…These include the linearization of millimeter-wave power amplifiers [10], the reduction of the computational complexity associated with delay estimation algorithm [11], and the computational stability of low sampling rate systems [12]. Furthermore, neural networks are gaining popularity, and have recently been reported for bandwidth, power and carrier configuration resilient predistorters [13], multi-input multi-output (MIMO) beamforming applications [14], 5G radio over fiber applications [15] and 6G dynamic spectrum aggregation [16]. Through all recent work, a common issue can be clearly identified.…”
Section: Introductionmentioning
confidence: 99%
“…These include the linearization of millimeter-wave power amplifiers [10], the reduction of the computational complexity associated with delay estimation algorithm [11], and the computational stability of low sampling rate systems [12]. Furthermore, neural networks are gaining popularity, and have recently been reported for bandwidth, power and carrier configuration resilient predistorters [13], multi-input multi-output (MIMO) beamforming applications [14], 5G radio over fiber applications [15] and 6G dynamic spectrum aggregation [16]. Through all recent work, a common issue can be clearly identified.…”
Section: Introductionmentioning
confidence: 99%