Next-Gen Digital Predistortion From Hardware Acceleration of Neural Networks: Trends, Challenges, and Future

Area of Science:

• Electrical Engineering
• Computer Science
• Signal Processing

Background:

• Next-generation communication systems face computational challenges in real-time signal processing, especially for digital predistortion (DPD).
• Traditional DPD methods struggle with the accuracy and adaptability required for modern systems.
• Neural network (NN)-based DPD shows promise but faces implementation hurdles like complexity and scalability.

Purpose of the Study:

• To comprehensively review NN-based DPD techniques.
• To analyze hardware acceleration strategies for efficient real-time implementation of NN-DPD.
• To identify challenges and future directions for scalable and energy-efficient DPD.

Main Methods:

• Review and analysis of various NN architectures (deep, convolutional, recurrent, hybrid) for DPD.
• Evaluation of hardware acceleration platforms including GPUs, FPGAs, and ASICs.
• Assessment of challenges such as evaluation standards and real-world validation.

Main Results:

• NN-based DPD offers superior modeling accuracy and adaptability compared to traditional methods.
• Different NN architectures and hardware platforms present distinct tradeoffs in performance and efficiency.
• Key challenges include fragmented evaluation metrics and limited practical deployment.

Conclusions:

• NN-based DPD is crucial for linearizing power amplifier nonlinearities in advanced communication systems.
• Hardware acceleration is essential for overcoming the computational complexity of NN-DPD.
• Future research should focus on model-hardware codesign and on-chip learning for scalable, energy-efficient solutions.