Multi-Lookup Table FPGA Implementation of an Adaptive Digital Predistorter for Linearizing RF Power Amplifiers With Memory Effects

Abstract

Abstract-This paper presents a hardware implementation of a digital predistorter (DPD) for linearizing RF power amplifiers (PAs) for wideband applications. The proposed predistortion linearizer is based on a nonlinear auto-regressive moving average (NARMA) structure, which can be derived from the NARMA PA behavioral model and then mapped into a set of scalable lookup tables (LUTs). The linearizer takes advantage of its recursive nature to relax the LUT count needed to compensate memory effects in PAs. Experimental support is provided by the implementation of the proposed NARMA DPD in a field-programmable gate-array device to linearize a 170-W peak power PA, validating the recursive DPD NARMA structure for W-CDMA signals and flexible transmission bandwidth scenarios. To the best of the authors' knowledge, it is the first time that a recursive structure is experimentally validated for DPD purposes. In addition to the results on PA efficiency and linearity, this paper addresses many practical implementation issues related to the use of FPGA in DPD applications, giving an original insight on actual prototyping scenarios. Finally, this study discusses the possibility of further enhancing the overall efficiency by degrading the PA operation mode, provided that DPD may be unavoidable due to the impact of memory effects.Index Terms-Digital predistortion (DPD), field programmable gate array (FPGA), nonlinear auto-regressive moving average (NARMA) models, power amplifier (PA) linearization.

Kalman Filter Algorithm for Adaptive Digital Predistortion

“…The power spectrum comparison for the CDMA signal among input signal, distortion signal and compensation signal is shown in the Figure.5. Form the figure, we can see the improvement of adjacent channel power ratio (ACPR) [6] is greater than 20dB when the predistorter is worked. …”

“…Recently, many predistorter models, such as memory polynomials model, Hammerstein model, Wiener model, NARMA model and so on, are proposed in literature [3,6,7]. The paper takes memory-polynomials model as an example to mainly focus on the application of Kalman filter in the predistorter estimation.…”

Markov mode-multiplexing mode in OFDM outphasing transmitters

“…. π(n) can be easily generated in a digital platform (DSP or FPGA) or can be offline created and stored in a RAM or ROM if desired to reduce the computational cost [12]. In addition, it should fulfill the so-called Markov property [13], and each binary value, i.e., the state value, controls the transmitting mode ("OUTPHASING (O)", "BALANCED (B)").…”

A Modified Decomposed Vector Rotation-Based Behavioral Model With Efficient Hardware Implementation for Digital Predistortion of RF Power Amplifiers

“…In the predistorter, the model coefficients ̃ are known after model extraction and the partition thresholds are also pre-determined, the summation ∑̃(|̃( )|, ) =1 result in (13) turns out only depending on the magnitude value of the input signal, i.e., |̃( )| . This leads that a low-cost implementation strategy can be applied, similar to that discussed in [19]- [21]. The idea is to merge all the valid coefficients together before multiplying with |̃( )| , as explained below.…”