[2023] Hardware Accelerated AI for Digital Predistortion

Digital Predistortion (DPD) is used to counteract the nonlinearity in power amplifiers (PA) [1] to minimize signal distortion and enhance PA efficiency, which is critical to reducing the energy consumption of communication systems [2]. In the era of 5G, the wideband signal increases the memory effect in PAs, and we need better DPD algorithms that can better capture the temporal dependency of data. Deep neural networks, such as recurrent neural networks (RNNs) [2] and convolutional neural networks (CNNs) [3], are promising candidates to enhance the performance of DPD at a lower cost compared to conventional memory polynomial methods [4]. You will also design a hardware accelerator on FPGA to realize real-time inference of your algorithms.

Background Material

1. S. P. Yadav and S. C. Bera, "Nonlinearity effect of Power Amplifiers in wireless communication systems," 2014 International Conference on Electronics, Communication and Computational Engineering (ICECCE), 2014, pp. 12-17, doi: 10.1109/ICECCE.2014.7086613.

2. How DPD improves power amplifier efficiency - 5G Technology World

3. Understanding LSTM Networks -- colah's blog

4. Understanding GRU Networks. In this article, I will try to give a… | by Simeon Kostadinov | Towards Data Science

5. D. R. Morgan, Z. Ma, J. Kim, M. G. Zierdt and J. Pastalan, "A Generalized Memory Polynomial Model for Digital Predistortion of RF Power Amplifiers," in IEEE Transactions on Signal Processing, vol. 54, no. 10, pp. 3852-3860, Oct. 2006, doi: 10.1109/TSP.2006.879264.

6. C. Tarver, L. Jiang, A. Sefidi and J. R. Cavallaro, "Neural Network DPD via Backpropagation through a Neural Network Model of the PA," 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019, pp. 358-362, doi: 10.1109/IEEECONF44664.2019.9048910.

7. C. Tarver, A. Balatsoukas-Stimming and J. R. Cavallaro, "Design and Implementation of a Neural Network Based Predistorter for Enhanced Mobile Broadband," 2019 IEEE International Workshop on Signal Processing Systems (SiPS), 2019, pp. 296-301, doi: 10.1109/SiPS47522.2019.9020606.

8. MATLAB: Digital Predistortion to Compensate for Power Amplifier Nonlinearities

Assignment

1. Design deep neural network-based DPD algorithms.

2. Evaluate their performance in simulators and with real transmitter circuits.

3. Design a hardware accelerator for your algorithms on FPGAs.

Requirements

• Familiar with MATLAB & Python.

• Knowledge of digital signal processing.

• Previous experience with PyTorch is a plus.

• Knowledge of Verilog/SystemVerilog.

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Last modified: 2023-11-12