Preliminary Technical
Data
Rev. PrA | Page 7 of 82
Figure 7. Response for Both Half Band 1 and 2 Enabled (x4): 41% of Fs
DPD ALGORITHM OVERVIEW
The ADRV9029 DPD algorithm supports both indirect learning and direct learning DPD mechanisms for extracting DPD model
coefficients. The details of direct and indirect DPD learning mechanisms are provided in the following sections.
The user can configure the transceiver DPD learning algorithm through the adi_adrv9025_DpdTrackingConfigSet() API using the
settings listed in Table 1.
Table 1. DPD Direct Learning Setting
adi_adrv9025_DpdTrackingConfig_t.
enableDirectLearning
DPD Learning Mechanism Selected
0
Indirect Learning
1
Direct Learning
Indirect Learning
Indirect learning involves using the observation receiver data (PA output data) as a reference for predicting the input samples
corresponding to the reference. The function used for predicting the input samples is known as the inverse PA model. Once the
prediction of input samples corresponding to the observed data is good, the estimated inverse PA model is used to pre-distort the
transmit data. In Figure 8, Y represents the observed samples at the output of the PA and X represents the input samples to the
PA. The estimation engine computes the inverse PA model that is applied to transmit data (represented as U) in the DPD actuator.
X
U
Y
Figure 8. DPD Indirect Learning Architecture
