urement that are done on a test bench during development time, potentially augmented by an on-the-fly correction for temperature or other. For higher end systems, the characteriza- tion is done live by a feedback loop that measures the output of the PA and compares it against the DPD input signal to update DPD algorithms during system deployment (Fig. 2-35). Assume an implementation using polynomial modeling (for more information, see later on in this document), complexity of DPD algorithms is typically accounted by the number of (multiply-accumulate) operations that is done on the sample stream that is input to the DPD algorithm-and multiplied by the rate of that sample stream to define the aggregate required multiply-accumulate count. Low-end DPD algorithms targeting indoor, <1 W output power Chapter 2 System components, requirements, and interfaces 83 Coefficients Updater Coefficient Coefficients Calculation FIGURE 2-35 DPD feedback processing. DPD, digital predistortion. levels can suffice with around a dozen operations per sample, whereas higher end system can use multiple dozens of operations on each sample. Keep in mind that the sample rate is not equal to the IBW or baseband rate (say, 122.88MSPS for a 100 MHz signal) but to a mul- tiplier of the baseband rate to cover the out-of-band spectral components that need to be compensated. 3-5 X oversampling is a typical ratio seen in the field. Note that the use of DPD has an impact on the performance requirements of the DAC used in the transmit path: unless it has sufficient performance [precision as counted in signal-to-interference and noise ratio and/or effective bits (effective number of bits)], the DPD algorithm will not be able to deliver a signal to the DPD with sufficiently low-noise floor over high-enough bandwidth (own channel and adjacent channels). In addition, feedback- based DPD algorithms need an associated ADC supporting enough performance to measure the generated distortion in the output signal. Digital analog conversion (digital-to-analog con