TCN-DPD: Parameter-Efficient Temporal Convolutional Networks for Wideband Digital Predistortion

Abstract

As a crucial component, one common challenge of power amplifiers (PA) is the nonlinearity in the wireless communication system. Digital predistortion (DPD) is essential for mitigating nonlinearity in radio frequency (RF) power amplifiers, particularly for wideband applications. This thesis work aims to present the TCN-DPD model, a novel parameter-efficient architecture based on temporal convolutional networks (TCNs) to enhance the performance of PA. Therefore, the main problem should focused on the TCN-DPD model implementation with parameter efficiency. When TCN architecture was designed with several noncausal and dilated depthwise convolution layers and 1*1 convolution layers, optimized activation functions should be explored to complete the TCN architecture. By evaluating on the OpenDPD framework with the DPA_200MHz dataset, Hardswish, Tanh, SiLU, and GELU were considered by benchmarking different activations of TCN-DPD based on SIM- NMSE and SIM-ACLR metrics on average. Hardswish was confirmed in the later experiments as the optimized activation function in TCN architecture based on simulated results ACLR of -51.54 dBc and
NMSE of -44.61 dB. Since the TCN-DPD architecture was completed, this proposed model’s performance in PA and DPD benchmarks is desirable to be tested, and later experiments will use the same dataset and framework as the benchmark of activation function did. In PA benchmarking, the TCN model achieves SIM-NMSE -34.99 dB on average compared to other models, LSTM, GRU, RVTDCNN, VDLSTM, PNTDNN, and DGRU. This achievement shows the TCN architecture has a high potential to handle a range of dependencies efficiently in the DPD application system. Furthermore, DPD benchmarking is the main experiment in this thesis. Two architectures were selected as the pre-trained PA model: the DGRU and TCN models. When the pre-trained PA is fixed as the DGRU model, TCN-DPD demonstrates superior linearization performance with only 500 real-valued parameters, achieving averaged and simulated ACPRs of -51.58/-49.26 dBc (L/R), EVM of -47.52 dB, and NMSE of -44.61dB. The results are simulated ACPRs of -50.39/-50.01 dBc (L/R), EVM of -47.88 dB, and NMSE of -45.51 dB in average when the pre-trained PA model is TCN. Both DPD benchmarks include different DNN-DPD models, and TCN-DPD has superior performance in the comparison, especially the SIM-NMSE and SIM-EVM performance is significantly higher than other models when the pre-trained model is TCN. These results establish TCN-DPD as a promising solution for efficient wideband PA linearization. Moreover, the evaluation extended to DNN-DPD performance with various numbers of parameters ranging from 200 to 1000, where the TCN-200 model highlighted its effectiveness by showing impressive results in SIM-NMSE -41.27dB/-43.51dB(DGRU/TCN PA), achieving superior linearization performance while using significantly fewer parameters than existing deep neural network solutions, proving the TCN-DPD model’s parameters efficiency.
The research in this thesis conclusively demonstrates that TCNs can be implemented in DPD applications, providing more parameters efficiency, better performance, and robust PA linearization solutions, potentially setting a new alternative in DPD technology.