Bandwidth-Scalable Digital Predistortion of Active Phased Array Using Transfer Learning Neural Network
نویسندگان
چکیده
This paper proposes a transfer learning neural network (TLNN) approach for digital predistortion (DPD) of mm-Wave active phased arrays (APA) operated under variable signal bandwidth regimes. Compared with the conventional artificial (ANN) method, proposed can achieve similar linearization performance much lower computational complexity by transferring part trained model from one to another bandwidth. In recently introduced 5G, increased triggers considerable memory effects in APA. Moreover, dealing different bandwidths typically requires time-consuming recalculation predistorter parameters. this paper, authors propose have those challenges solved using DPD based on method. The was validated over-the-air (OTA) measurements an APA excited signals varying bandwidth, namely 20 MHz 100 MHz. Experimental results show significant reduction training time while ensuring good performance. With applied TLNN DPD, 8.5 dB improvement adjacent channel leakage ratio (ACLR) and 8.6 % points error vector magnitude (EVM) is achieved. Under regime, terms number multiplications reduced 199168 160. proved be robust concerning variation excitation signal.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2023
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2023.3242648