Attention Mechanism Based Bidirectional LSTM Model for Broadband Power
Amplifier Linearization
Abstract
In this letter, a novel model for broadband power amplifier (PA)
linearization is proposed, namely Attention Mechanism based
Bidirectional Long Short-term Memory network (AM-BiLSTM). In order to
verify the linearization performance of the AM-BiLSTM model, a 100MHz
bandwidth 5G new radio (5G NR) signal is employed to test the sub-6G PA
operating at 2.6-GHz. The experimental results show that the adjacent
channel power ratio (ACPR) of the PA with AM-BiLSTM can be improved by
24dB which is 6-dB better than the generalized memory polynomial (GMP)
and 3-dB better than the Chebyshev polynomials LSTM (CP-LSTM) in
ref[1]. Therefore, the proposed AM-BiLSTM is very effective for the
linearization of broadband PA.