Adaptive DPD

The defining characteristic of adaptive DPD is its ability to continuously update predistorter coefficients during live transmission without interrupting the signal. Unlike static DPD, which uses a fixed set of parameters extracted once during calibration, adaptive systems employ online training algorithms that monitor the power amplifier output through a dedicated feedback path and recalculate correction terms on the fly.

• Tracks thermal memory effects as the PA heats up during operation
• Compensates for aging-related drift in transistor characteristics over months of deployment
• Adjusts to frequency hopping patterns where PA nonlinearity varies across channels
• Maintains ACLR and EVM targets under dynamic operating conditions

Power amplifiers exhibit short-term and long-term memory effects that static DPD cannot address. Short-term effects arise from bias circuit impedance and trapping phenomena in GaN transistors, manifesting as envelope-dependent distortion. Long-term effects stem from thermal time constants as the die heats and cools.

Adaptive DPD continuously models these effects using:

• Memory polynomial models with delayed envelope terms to capture electrical memory
• Thermal tracking loops that adjust coefficients as junction temperature changes
• Volterra kernel adaptation for the most general nonlinear memory characterization
• Real-time updates prevent spectral regrowth during bursty transmission patterns

Implementing adaptive DPD on FPGAs requires balancing linearization accuracy against resource utilization and power consumption. The predistorter core in the forward path must operate at sample rate with minimal latency, while the adaptation engine can run at a slower update rate.

• Fixed-point arithmetic reduces DSP48 slice usage compared to floating-point
• Pipelining increases clock frequency at the cost of latency
• Coefficient quantization trades bit width for hardware efficiency
• Look-up table (LUT) DPD offers lower complexity than polynomial-based approaches
• High-Level Synthesis (HLS) accelerates development of complex adaptation algorithms
• Partitioning between FPGA fabric and ARM cores on Zynq UltraScale+ enables software-defined adaptation control

The dominant closed-loop topology for adaptive DPD. In an ILA, a post-distorter model is trained on the PA's output in the feedback path. The learned coefficients are then copied directly to the predistorter in the forward path. This elegant structure avoids the need to mathematically invert the power amplifier model, making it robust to numerical instability during real-time adaptation.

An alternative adaptive topology where the predistorter parameters are estimated by directly modeling the inverse of the PA's nonlinear behavior. The algorithm minimizes the error between the desired ideal signal and the actual PA output. While conceptually straightforward, DLA requires solving a more complex optimization problem in real-time, often demanding higher computational resources than ILA.

A compact behavioral model that captures both static nonlinearity and memory effects. It extends a simple polynomial by including delayed envelope terms:

• Odd-order terms: Model AM/AM and AM/PM distortion
• Memory taps: Capture thermal and electrical memory
• Pruned variants: Reduce coefficient count for efficient FPGA implementation This structure provides an excellent trade-off between linearization accuracy and hardware complexity for adaptive systems.

The observation receiver chain that enables adaptation by providing a digitized copy of the PA's distorted output. Critical components include:

• Directional coupler: Samples a fraction of the RF output
• Downconverter: Mixes the signal to baseband or IF
• ADC: Digitizes with sufficient dynamic range to capture distortion products
• Time alignment: Precisely synchronizes feedback with the reference signal The fidelity of this path directly limits the ultimate linearization performance.

The capability to update predistortion coefficients continuously during live transmission without interrupting the signal. This is essential for tracking dynamic changes:

• Thermal drift: PA characteristics shift as temperature rises
• Aging effects: Semiconductor degradation over months and years
• Frequency hopping: Different carrier frequencies excite different nonlinear modes
• Load mismatch: Antenna impedance changes due to environment Modern systems update coefficients every few milliseconds.

The process of converting high-precision floating-point DPD model parameters into fixed-point representations for FPGA implementation. This involves a critical engineering trade-off:

• More bits: Higher linearization accuracy, but consumes more DSP slices and block RAM
• Fewer bits: Lower resource usage, but introduces quantization noise that degrades ACLR Optimal bit-width selection is determined through extensive fixed-point simulation and hardware-in-the-loop validation.