Dpd Robustness - Analog Devices ADRV9029 User Manual

Preliminary Technical
Data

DPD ROBUSTNESS

This section provides an overview of the features that enhance DPD robustness. These features include DPD stability metrics and
flexible architecture for setting up recovery actions based on these pre-defined DPD metrics. The user can optionally turn on the
DPD robustness feature to protect DPD against erroneous adaptations in abnormal conditions.
In the transceiver DPD, abnormal conditions are detected by monitoring the following metrics:
1. Indirect error that indicates if the pre-distorted samples match the expected result after experiencing PA distortion
2.
3.
Transmit capture RMS power
4.
Transmit capture peak power
5.
Observed capture RMS power
6. Post-DPD Capture Tx to ORx EVM (Indirect EVM) which is a measure of non-linearity in the gain line up
Observed capture peak power
7. Pre-DPD capture Tx to ORx EVM (Direct EVM) which is a measure of DPD linearization performance
From a user's point of view, there are two sets of actions that are required to be taken:
Define the fault conditions via adi_adrv9025_DpdFaultConditionSet() API
Provide user-configurable thresholds and actions for fault conditions defined in previous step via
•
adi_adrv9025_DpdRecoveryActionSet() API
•
Example: measure transmit capture power, if power < threshold, abandon adaptation
After estimation of the DPD coefficients, the error between the predicted and measured predistortion is computed to determine
Calculation of Metrics
the expected DPD performance. Detection of a large error prevents application of bad coefficients can be calculated using the
following equation:
where is a vector of Tx samples after DPD actuator (post-DPD data);
formulated by ORx samples; is the DPD coefficients vector; and the operator
Similarly, indirect EVM and direct EVM are calculated using the following equations:
where is a vector of Tx samples before the DPD actuator (pre-DPD data) and is a vector of ORx samples. All samples are
time aligned and gain and phase equalized. The size of the vectors is the number of samples used in each update of the DPD
coefficients.
Transmit signal mean and peak power are calculated from the pre-DPD samples in
Observation receiver mean and peak power are calculated by the samples in .
The user can define fault conditions through the data structure adi_adrv9025_DpdFaultCondition_t described in Table 6. The
Defining Fault Conditions
fault conditions are programmed through the API adi_adrv9025_DpdFaultConditionSet().
Table 6. DPD fault condition definition data structure adi_adrv9025_DpdFaultCondition_t
Member Data Type
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is a matrix of features (such as items in GMP)
is the (Euclidean) norm of vectors.
and post-DPD samples in
Description
, respectively.