A Sparse Neural Network-Based Power Adaptive DPD Design and Its Hardware Implementation

Abstract

In this paper, an efficient neural-network-based adaptive DPD design which performs well under power varying conditions is presented. The DPD design is derived on the basis of the envelop timedelay neural network (ETDNN). The redefined ETDNN-DPD requires the part of parameter updates, which enables to adapt it to the rapid change of power amplifier (PA) distortion. Additionally, the redefined ETDNN-DPD also maintains the stability of the compensation performances under varying power condition while its structure is pruned by the structured pruning. Furthermore, to verify its practical use, we also propose the weight scaling technique, which reduces multiplications of the redefined ETDNN-DPD, and applied it to the implementation of the redefined ETDNN-DPD on FPGA. Compared FPGA-implemented ETDNN-DPD with FPGA-implemented conventional memory polynomial DPD, we verified that our proposed DPD achieved 3.2 dB better error vector magnitude (EVM) while lower hardware resource utilization at the fixed power level. Moreover, our proposed DPD kept better performance under the varying power condition only by the partial update of its parameters than memory polynomial DPD.

A neural network-based DPD coefficient determination for PA linearization in 5G and beyond-5G mmWave systems

“…A feature that unifies all these architectures is that the NN forms part of the DPD datapath, therefore, corresponding calculations have to be performed at the clock rate of the baseband signal. Given that most NN architectures are considerably larger and more complex than polynomial models, this represents a drawback, especially in terms of power consumption, and attempts have been made to alleviate this issue by sparsifying the network connections, or reducing the sampling rate of the DPD calculations [14][15][16][17][18][19][20][21][22][23][24]. In this paper, we propose a new approach that employs an artificial NN for estimating the polynomial coefficients.…”