NN-Based Frequency Domain DPD for OFDM Massive MIMO Transmitters With Multiple States

Fuente: arXiv
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Autores principales: Zhang, Yundi, Sun, Yanshi, Chen, Li
Formato: Preprint
Publicado: 2025
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author Zhang, Yundi
Sun, Yanshi
Chen, Li
author_facet Zhang, Yundi
Sun, Yanshi
Chen, Li
contents Frequency domain (FD)-digital predistortion (DPD) is a low-complexity DPD solution for massive multiple-inputmultiple-output (MIMO) transmitters (TXs). In this letter, we extend FD-DPD to scenarios with multiple signal states (e.g., bandwidths and power levels). First, we propose a new neural network (NN)-based FD-DPD model, whose main idea is to use a hypernetwork (HN) to generate parameters for the output layer of the main NN based on the signal states. Then, we introduce how to effectively train the model with the help of time-domain (TD)-DPD. Experimental results show that the proposed model can achieve excellent performance, without requiring additional online training when signal states change.
format Preprint
id arxiv_https___arxiv_org_abs_2512_10731
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle NN-Based Frequency Domain DPD for OFDM Massive MIMO Transmitters With Multiple States
Zhang, Yundi
Sun, Yanshi
Chen, Li
Signal Processing
Frequency domain (FD)-digital predistortion (DPD) is a low-complexity DPD solution for massive multiple-inputmultiple-output (MIMO) transmitters (TXs). In this letter, we extend FD-DPD to scenarios with multiple signal states (e.g., bandwidths and power levels). First, we propose a new neural network (NN)-based FD-DPD model, whose main idea is to use a hypernetwork (HN) to generate parameters for the output layer of the main NN based on the signal states. Then, we introduce how to effectively train the model with the help of time-domain (TD)-DPD. Experimental results show that the proposed model can achieve excellent performance, without requiring additional online training when signal states change.
title NN-Based Frequency Domain DPD for OFDM Massive MIMO Transmitters With Multiple States
topic Signal Processing
url https://arxiv.org/abs/2512.10731