Frequency-domain digital predistortion for Massive MU-MIMO-OFDM Downlink
Wu, Yibo; Gustavsson, Ulf; Valkama, Mikko; I Amat, Alexandre Graell; Wymeersch, Henk (2022)
Wu, Yibo
Gustavsson, Ulf
Valkama, Mikko
I Amat, Alexandre Graell
Wymeersch, Henk
IEEE
2022
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202302152410
https://urn.fi/URN:NBN:fi:tuni-202302152410
Kuvaus
Peer reviewed
Tiivistelmä
Digital predistortion (DPD) is a method commonly used to compensate for the nonlinear effects of power amplifiers (sPAs). However, the computational complexity of most DPD algorithms becomes an issue in the downlink of massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM), where potentially up to several hundreds of PAs in the base station (BS) require linearization. In this paper, we propose a convolutional neural network (CNN)-based DPD in the frequency domain, taking place before the precoding, where the dimensionality of the signal space depends on the number of users, instead of the number of BS antennas. Simulation results on generalized memory polynomial (GMP)-based PAs show that the proposed CNN-based DPD can lead to very large complexity savings as the number of BS antenna increases at the expense of a small increase in power to achieve the same symbol error rate (SER).
Kokoelmat
- TUNICRIS-julkaisut [16983]