Linearization Bandwidth

The feedback receiver must capture the full spectral regrowth to train the predistorter. Analog-to-digital converter (ADC) sampling rate is the primary bottleneck—Nyquist requires sampling at >2x the highest distortion frequency. For a 100 MHz signal with 5th-order nonlinearities, this demands >500 MHz of observation bandwidth. Anti-aliasing filters and RF downconverter flatness further constrain usable bandwidth.

The predistorter itself must operate at a rate sufficient to generate cancellation products. Multi-rate DPD architectures decouple the predistortion processing rate from the baseband symbol rate, but this increases FPGA resource consumption. Key tradeoffs include:

• Higher sampling rates capture wider distortion bandwidths
• Interpolation filters introduce latency and group delay variation
• JESD204B/C interface rates between DAC/ADC and FPGA limit maximum throughput

Long-term memory effects from thermal dynamics and bias network impedance require the DPD model to span many sample periods. As signal bandwidth increases, the memory depth in terms of samples grows proportionally. A 200 MHz signal with 100 ns of memory requires 20 taps—quadratically expanding the coefficient count in Volterra-based models. This directly impacts:

• Coefficient estimation convergence time
• FPGA multiplier and block RAM utilization
• Numerical stability of the adaptation algorithm

Real-time adaptation loops must compute updated predistorter coefficients within the coherence time of the PA nonlinearity. Wideband signals demand faster update rates because:

• Rapid envelope variations expose short-term memory effects
• Thermal transients shift the optimal linearization point
• Recursive least squares (RLS) converges faster than LMS but requires O(N²) complexity
• Matrix inversion in direct learning architectures becomes the computational bottleneck at wide bandwidths

Insufficient observation path sampling causes aliasing distortion that folds out-of-band energy back into the Nyquist zone, corrupting the training signal. Simultaneously, ADC clipping from high PAPR signals truncates amplitude peaks, introducing nonlinearity in the feedback path itself. Mitigation strategies include:

• Oversampling the observation path by 5-7x the signal bandwidth
• Implementing feedback path linearization to pre-compensate receiver nonlinearity
• Using dithering techniques to decorrelate quantization noise

Wideband DPD implementations push FPGA fabric to its limits. The critical path includes:

• Complex multiply-accumulate operations for polynomial evaluation at multi-GSPS rates
• Look-up table (LUT) address generation with minimal pipeline delay
• High-speed transceiver IP for JESD204C interfaces
• Timing closure at >400 MHz fabric clock rates often requires extensive pipelining, which adds group delay that must be compensated in the alignment between forward and observation paths.