Evolutionary Multiobjective Optimization for Digital Predistortion Architectures
In wireless communication systems, high-power transmitters suffer from nonlinearities due to power amplifier (PA) characteristics, I/Q imbalance, and local oscillator (LO) leakage. Digital Predistortion (DPD) is an effective technique to counteract these impairments. To help maximize agility in cognitive radio systems, it is important to investigate dynamically reconfigurable DPD systems that are adaptive to changes in the employed modulation schemes and operational constraints. To help maximize effectiveness, such reconfiguration should be performed based on multidimensional operational criteria. With this motivation, we develop in this paper a novel evolutionary algorithm framework for multiobjective optimization of DPD systems. We demonstrate our framework by applying it to develop an adaptive DPD architecture, called the adaptive, dataflow-based DPD architecture (ADDA), where Pareto-optimized DPD parameters are derived subject to multidimensional constraints to support efficient predistortion across time-varying operational requirements and modulation schemes. Through extensive simulation results, we demonstrate the effectiveness of our proposed multiobjective optimization framework in deriving efficient DPD configurations for run-time adaptation.
KeywordsDigital predistortion Multiobjective optimization Evolutionary algorithms
This research was supported in part by Tekes, the Finnish Funding Agency for Innovation; and the U.S. National Science Foundation.
- 4.Ghazi, A., et al.: Low power implementation of digital predistortion filter ona heterogeneous application specific multiprocessor.In: Proceedings of the International Conference on Acoustics, Speech,and Signal Processing, pp. 8391–8395. Florence, Italy (2014)Google Scholar
- 6.Llamocca, D., Pattichis, M.: Dynamic energy, performance, and accuracy optimization and management using automatically generated constraints for separable 2D FIR filtering for digital video processing. Transactions on Reconfigurable Technology and Systems 7(4). Article No. 31(2015)Google Scholar
- 7.Nizamuddin, M.: Predistortion for nonlinear power amplifiers with memory. Ph.D. thesis, Virginia Polytechnic Institute and State University (2002)Google Scholar
- 8.Shen, C., Plishker, W., Wu, H., Bhattacharyya, S.S.: A lightweight dataflow approach for design and implementation of SDR systems. In: Proceedings of the Wireless Innovation Conference and Product Exposition (2010)Google Scholar
- 9.Sills, J.A., Sperlich, R.: Adaptive power amplifier linearization by digital pre-distortion using genetic algorithms. In: Proceedings of the Radio and Wireless Conference, pp. 229–232 (2002)Google Scholar
- 11.Wang, L.H., et al.: Dataflow modeling and design for cognitive radio networks. In: Proceedings of the International Conference on Cognitive Radio Oriented Wireless Networks, pp. 196–201 (2013)Google Scholar
- 12.Zitzler, E.: Evolutionary algorithms for multiobjective optimization: Methods and applications. Ph.D. thesis, Swiss Federal Institute of Technology (ETH) Zurich (1999)Google Scholar