Abstract
In this article, we propose a new behavioral modeling method to reduce the running complexity and power consumption of digital predistortion (DPD) models for radio frequency power amplifiers. By employing the proposed method, different cross terms in a DPD model can be switched dynamically in real time. Each cross-term branch can further choose the model coefficients from multiple coefficient sets, which improves the DPD performance with little extra complexity. The switch of both cross-term branches and model coefficients is realized using a decision tree-based switch controller, leading to very low run-time complexity. By optimizing the selection process, different input samples can choose the most suitable model configuration that contributes most to the linearization performance. As only one branch is activated at a time, the power consumption can be greatly reduced. Based on the experimental results, the proposed method can achieve excellent linearization performance with significantly reduced power consumption.
Highlights
W ITH various emerging applications, we are seeing a new landscape of wireless connectivity
Compared to that in earlier generation macro base stations, the number of radio frequency (RF) chains in 5G transmitters is significantly increased, which demands for energy-efficient, low-cost, and highly integrated solutions
For models that are linear in parameters, digital predistortion (DPD) can be expressed in a matrix format as u = Xc where u = [u N, u N−1, . . .]T is the predistorted signal vector consisting of N data samples, c = [c1, c2, . . .]T is a vector including all Q model coefficients, and X is an N × Q regression matrix containing all basis functions constructed with the input signal samples x . xand uare both baseband complex envelope signals
Summary
W ITH various emerging applications, we are seeing a new landscape of wireless connectivity. To reduce power consumption of the overall system, when the PAs are designed to produce lower power, the power budget for DPD shall shrink correspondingly It becomes important in optimizing the model complexity and power consumption of DPD blocks to meet the system efficiency requirements [4]. A novel real-time model switching technique is presented to reduce the computational complexity of DPD models. While multiple cross terms and basis functions are implemented in the system, only the most useful ones are selected and activated for each input data sample in real-time operation. By using the proposed approach, the computational complexity is significantly reduced when calculating the predistorted output, and the power consumption of DPD can be much lower, compared to that using the conventional methods.
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