Neural Network on the Edge: Efficient and Low Cost FPGA Implementation of Digital Predistortion in MIMO Systems

Base stations in cellular networks must operate linearly, power efficiently, and with ever increasing flexibility. Recent FPGA hardware advances have demonstrated linearization using neural networks, however the latency introduced by these solutions is a concern. We present a novel hardware implementation for a low digital cost, high throughput pipelined Real Valued Time Delay Neural Network (RVTDNN) structure with a hardware-efficient activation function. Network training times are reduced by minimizing the training signal samples used, based on a biased probability density function (pdf). The design has been experimen-tally validated using an AMD/Xilinx RFSoC ZCU216 board and surpasses the data throughput of conventional RVTDNN-based DPD while using a fraction of their hardware utilization.