Feature: Digital design
evolve (2G-5G, and beyond), so too do the requirements placed on it. Among these challenges are wider bandwidth, higher power, carrier placements, higher peak-to- average signal ratio and greater density of cellular base stations. PAs and DPD share a partially symbiotic
Figure 2: Conceptual representation of a digital predistortion system
PA efficiency Over the decades, the efficiency of the PA has improved to above 50%, achieved through smart architectures (such as Doherty) and advanced process technologies (such as GaN). Still, this level of efficiency comes at the cost of linearity, with two main consequences: in-band distortions and out-of-band emissions. In-band distortions disrupt the transmitted signal’s fidelity, and can be represented by a degradation in error vector modulation (EVM) performance. Out-of-band emissions break the 3GPP emissions mask, and can cause unwanted interference to signals in adjacent-channel frequency allocations, measured as adjacent-channel leakage ratio (ACLR). GaN PAs also suffer from in-band
distortions produced by the charge- trapping effect. Tese are dynamic in nature and unrelated to any SNR imposed by the ACLR. Correcting PA non-linearity is
essential. In principle, if we know the PA’s transfer function, using the inverse on the data should nullify the non-linearities. However, the PA has a somewhat “dynamic” transfer function, with its input-to-output characteristics continuously in flux. Furthermore, the dynamic transfer function depends on a combination of the PA characteristics (power, voltage, temperature, process, etc.), the input signal it receives, and the signals already received/processed (memory effects). Hence, the dynamic non-linear behaviour of the PA must be modelled before it can be corrected, which requires digital predistortion (DPD), adaptive to the environment’s dynamics.
Figure 2 shows the core elements
for many DPD systems: observation, estimation and actuation. Tis concept generates a model that tracks the expected response of the PA so that an appropriate cancellation signal can be generated to nullify the PA’s predicted non-linear behaviour. Tere are many models, including the ubiquitous generalised memory polynomial (GMP). A PA operating in its linear region
generates fewer out-of-band distortions and, as seen in Figure 3, has a notable reduction in the level of noise that leaks into the adjacent channels. Figure 3 is a screenshot from a spectrum analyser on a typical DPD test bench, used to demonstrate static DPD performance that meets the requirements of many ACLR compliance tests. DPD has been applied to cellular
base stations since the 1990s, and as the technology and generational requirements
relationship: in some instances harmonious, in others more difficult. A PA may be do well with one DPD but struggle with another. Oſten, optimal performance is achieved when both, DPD and PA, are configured and tuned to match the specific application. But, as wideband and dual-band applications become the norm, PA developers are challenged on how to achieve wider bandwidths at higher frequencies whilst meeting performance expectations.
Meeting the challenges Quantifying DPD performance is not straightforward; there’s a matrix of conditions, scenarios and mitigating dependencies related to the DPD to consider. Regarding its performance, the test conditions’ specifics must be clearly defined: achieving > 50% efficiency at 200MHz bandwidth is a greater challenge than, say, at 20MHz. Te situation becomes more complex when we consider carrier placement within the allocated spectrum; it may be a contiguous signal, but it may also be a segmented carrier allocation in the occupied parts of the spectrum. At a high level, there are quantitative
Figure 3: Adjacent-channel leakage with and without digital predistortion
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