Evolutionary multiobjective optimization for adaptive dataflow-based digital predistortion architectures
Research output: Contribution to journal › Article › Scientific › peer-review
|Journal||EAI Endorsed Transactions on Cognitive Communications|
|Publication status||Published - 23 Feb 2017|
|Publication type||A1 Journal article-refereed|
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.