Coefficient Freeze

The coefficient freeze is a gating function that disables the update path of an adaptive filter. When activated, the current predistorter coefficients are held constant in hardware registers, and the error signal from the feedback receiver is ignored by the adaptation engine. This prevents the optimizer from chasing noise or injecting instability when the signal-to-noise ratio drops below a usable threshold.

A freeze is typically asserted by a supervisory state machine monitoring several real-time metrics:

• Signal Presence Detector: Freeze when input power falls below a squelch threshold, indicating no transmission.
• Feedback Integrity Monitor: Freeze if the observation receiver reports a fault, such as a disconnected cable or saturated ADC.
• PAPR Anomaly: Freeze if the Peak-to-Average Power Ratio deviates wildly from the expected waveform profile, suggesting a corrupted transmit chain.
• Temperature Shock: Freeze briefly during rapid thermal transients to let the thermal memory effect settle before resuming adaptation.

Without a freeze mechanism, an adaptive algorithm like Least Mean Squares (LMS) or Recursive Least Squares (RLS) will attempt to minimize an error signal that consists purely of noise. This causes the correlation matrix estimation to become singular and the coefficients to drift toward random, often extreme, values. The result is catastrophic spectral regrowth and a spike in Adjacent Channel Leakage Ratio (ACLR) when the signal returns. The freeze acts as a logical firewall against this pathological state.

In FPGA-Based DPD Implementation, the freeze is a single-bit control signal that gates the write-enable line of the coefficient register bank. The adaptation processor continues to compute updates, but they are discarded at the hardware interface. This is distinct from simply setting the learning rate to zero in software, as a hardware freeze guarantees atomic, glitch-free coefficient stability during mode transitions. The freeze is often synchronized with a time alignment reset to ensure clean restart conditions.

During a prolonged freeze, recursive algorithms with a forgetting factor (λ < 1) face a unique problem. The internal correlation matrix continues to decay exponentially even though no new data is being processed. When the freeze is lifted, the matrix may be severely ill-conditioned, causing a transient burst of instability. Robust implementations either pause the forgetting mechanism during the freeze or re-initialize the matrix with a regularization parameter upon restart.

A well-designed freeze controller includes hysteresis to prevent rapid toggling. The un-freeze threshold for signal presence is set higher than the freeze threshold. Upon release, the adaptation loop often enters a re-acquisition mode with a temporarily increased learning rate to quickly re-converge before settling back to a low-noise steady-state step size. This ensures the convergence rate is fast enough to track any PA characteristic drift that occurred during the idle period.