.dfe - DFE Model

Behavioral model of a decision feedback equalizer (DFE).

Original Author: David Banas <capn.freako@gmail.com>

Original Date: 17 June 2014

This Python script provides a behavioral model of a decision feedback equalizer (DFE). The class defined, here, is intended for integration into the larger PyBERT framework.

Copyright (c) 2014 by David Banas; All rights reserved World wide.

class pybert.models.dfe.DFE(n_taps: int, gain: float, delta_t: float, alpha: float, ui: float, n_spb: int, decision_scaler: float, mod_type: int = 0, bandwidth: float = 100000000000.0, n_ave: int = 10, n_lock_ave: int = 500, rel_lock_tol: float = 0.01, lock_sustain: int = 500, ideal: bool = True, limits: list[tuple[float, float]] | None = None, agc_n_ave: int = 100)[source]

Bases: object

Behavioral model of a decision feedback equalizer (DFE).

Parameters:

  • n_taps – # of taps in adaptive filter

  • gain – adaptive filter tap weight correction gain

  • delta_t – CDR proportional branch constant (ps)

  • alpha – CDR integral branch constant (normalized to delta_t)

  • ui – nominal unit interval (ps)

  • n_spb – # of samples per unit interval

  • decision_scaler – multiplicative constant applied to the result of the sign function, when making a “1 vs. 0” decision. Sets the target magnitude for the DFE.

Keyword Arguments:

  • mod_type –

    The modulation type

    • 0: NRZ

    • 1: Duo-binary

    • 2: PAM-4

  • bandwidth – The bandwidth, at the summing node (Hz).

  • n_ave – The number of averages to take, before adapting. (Also, the number of CDR adjustments per DFE adaptation.)

  • n_lock_ave – The number of unit interval estimates to consider, when determining locked status.

  • rel_lock_tol – The relative tolerance for determining lock.

  • lock_sustain – Length of the histerysis vector used for lock flagging.

  • ideal – Boolean flag. When true, use an ideal summing node.

  • limits – List of pairs containing min/max values per tap.

  • agc_n_ave – Number of previous slicer sample to keep, for AGC operation.

Raises:

RuntimeError – If the requested modulation type is unknown.

decide(x: float) → tuple[float, list[int]][source]

Make the bit decisions, according to modulation type.

Parameters:

x – The signal value, at the decision time.

Returns:

A pair containing

  • One of:

    • {-1, 1} (NRZ)

    • {-1, 0, +1} (Duo-binary)

    • {-1, -1/3, +1/3, +1} (PAM-4)

  • The list of bits recovered.

Raises:

RuntimeError – If the requested modulation type is unknown.

run_dfe(sample_times: numpy.typing.NDArray.~Real, signal: numpy.typing.NDArray.~Real, use_agc: bool = False, dbg_dict: dict[str, ~typing.Any] | None=None) → tuple[numpy.typing.NDArray.~Real, list[list[float]], numpy.typing.NDArray.~Real, numpy.typing.NDArray.~Real, list[bool], numpy.typing.NDArray.~Real, list[int], numpy.typing.NDArray.~Real, numpy.typing.NDArray.~Real][source]

Run the DFE on the input signal.

Parameters:

  • sample_times – Vector of signal sampling times.

  • signal – Vector of sampled signal values.

Keyword Arguments:

  • use_agc – Perform continuous adjustment of decision_scaler when True. Default: False

  • dbg_dict – Optional dictionary, for stashing debugging information at runtime. Default: None

Returns:

A tuuple containing the following

  • ”dfe_out”: Samples of the summing node output, taken at the times given in sample_times.

  • ”tap_weights”: List of list of tap weights showing how the DFE adapted over time.

  • ”ui_ests”: List of unit interval estimates, showing how the CDR adapted.

  • ”clocks”: List of mostly zeros with ones at the recovered clocking instants.

    Useful for overlaying the clock times on signal waveforms, in plots.

  • ”lockeds”: List of Booleans indicating state of CDR lock.

  • ”clock_times”: List of clocking instants, as recovered by the CDR.

  • ”bits”: List of recovered bits.

  • ”sig_samps”: Samples of the summing node output, taken at the clocking instants.

  • ”decisions”: Symbol decisions made based on voltages sampled at clocking instants.

Raises:

RuntimeError – If the requested modulation type is unknown.

step(decision: float, error: float, update: bool)[source]

Step the DFE, according to the new decision and error inputs.

Parameters:

  • decision – Current slicer output.

  • error – Difference between summing node and slicer outputs.

  • update – If true, update tap weights.

Returns:

New backward filter output value.

Return type:

res

update_thresholds()[source]

Update decision thresholds.

class pybert.models.dfe.LfilterSS(b: numpy.typing.NDArray.numpy.float64, a: numpy.typing.NDArray.numpy.float64)[source]

Bases: object

A single steppable version of scipy.signal.lfilter().

Parameters:

  • b – Coefficients of the numerator of the rational transfer function.

  • a – Coefficients of the denominator of the rational transfer function.

step(x)[source]

Step the filter.

Parameters:

x (float) – Next input value.

Returns:

Next output value.

Return type:

(float)