The computational demands of next-generation (Next-Gen) communication systems pose major challenges for real-time signal processing, particularly in digital predistortion (DPD), which is essential for linearizing power amplifier (PA) nonlinearities. While traditional DPD methods-such as polynomial and Volterra series models-remain prevalent, neural network (NN)-based approaches offer superior modeling accuracy and adaptability. However, their deployment is hindered by high computational complexity, limited scalability, and hardware integration challenges. This review presents a comprehensive analysis of NN-based DPD techniques and hardware acceleration strategies for efficient real-time implementation. We assess the strengths of various NN architectures-deep, convolutional, recurrent, and hybrid-and evaluate their tradeoffs across graphics processing unit (GPU), field-programmable gate arrays (FPGA), and application-specific integrated circuits (ASIC) platforms. We also examine key challenges, including fragmented evaluation standards and limited real-world validation. Finally, we outline future directions emphasizing model-hardware codesign, reconfigurable computing, and on-chip learning to enable scalable, energy-efficient DPD for 5G, 6G, and beyond.
Khan et al. (2026) studied this question.