Manno Versluis

Manno Versluis, Yizhuo Wu, Chang Gao

Digital predistortion (DPD) is crucial for linearizing radio frequency (RF) power amplifiers (PAs), improving signal integrity and efficiency in wireless systems. Neural network (NN)-based DPD methods surpass traditional polynomial models but face computational challenges limiting their practical deployment. This paper introduces SparseDPD, an FPGA accelerator employing a spatially sparse phase-normalized time-delay neural network (PNTDNN), optimized through unstructured pruning to reduce computational load without accuracy loss. Implemented on a Xilinx Zynq-7Z010 FPGA, SparseDPD operates at 170 MHz, achieving exceptional linearization performance (ACPR: -59.4 dBc, EVM: -54.0 dBc, NMSE: -48.2 dB) with only 241 mW dynamic power, using 64 parameters with 74% sparsity. This work demonstrates FPGA-based acceleration, making NN-based DPD practical and efficient for real-time wireless communication applications. Code is publicly available at https://github.com/MannoVersluis/SparseDPD.

Huanqiang Duan, Manno Versluis, Qinyu Chen et al.

Digital predistortion (DPD) is essential for mitigating nonlinearity in RF power amplifiers, particularly for wideband applications. This paper presents TCN-DPD, a parameter-efficient architecture based on temporal convolutional networks, integrating noncausal dilated convolutions with optimized activation functions. Evaluated on the OpenDPD framework with the DPA_200MHz dataset, TCN-DPD achieves simulated ACPRs of -51.58/-49.26 dBc (L/R), EVM of -47.52 dB, and NMSE of -44.61 dB with 500 parameters and maintains superior linearization than prior models down to 200 parameters, making it promising for efficient wideband PA linearization.