 acterized by AM/AM (amplitude of output as a function of the amplitude of the input) and AM/PM (phase of the output as a function of amplitude of the input) curves. A memory modeled PA becomes relevant for higher output powers and higher bandwidth PAs such as used in enhanced Mobile Broadband (eMBB) 5G/NR applications. Memory effects mean that the output of the PA is not only impacted by the current input signal amplitude but also by the amplitude of previously transmitted signals, represented by previ- ous (digital) samples in the digital domain. Causes for memory effects include thermals and other parasitic effects. A PA that exhibits memory behavior can only be limitedly compen- sated by a memoryless algorithm. By implication, DPD can become much exponentially more complex to implement for high-end PAs that support higher bandwidth-both the sample rate increases, and memory effects need to be compensated for. Both memoryless and memory models are commonly pre-distorted by one out of two techniques: polynomial algorithm or Look-Up Table (LUT) based algorithm models. Within the polynomial models, Volterra series are used, including derivatives that reduce computa- tional complexity. Derivatives include Wiener, Hammerstein, Wiener-Hammerstein and par- allel Wiener structures, or as a Generalized Memory Polynomial ³: Ka-1La-1 k=1 l=0 m=1 Where KaLa are the coefficient counts for the aligned signal and envelope including memory, are the coefficients for the lagging signal and KLM-c are the coefficients for the leading signal. Not all coefficients are required, for example, even coefficients are often elimi- nated altogether. The number of nonzero coefficients defines the compute complexity (and associated power consumption). The implementation of the DPD forward path is like that of a filter and can be optimized with similar techniques including polyphase implementations that allow more efficient parallelization of the math. Alternatively, an LUT can be used where the amplitude of the input sample is used as an in