Est. read time: 3 minutes | Last updated: July 17, 2024 by John Gentile


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Open In Colab

import numpy as np
import matplotlib.pyplot as plt

from rfproto import measurements, multirate, plot, sig_gen

Benefits:

  • Multiplier-free implementation for economical (light resource utilization) design in digital HW systems which can handle arbitrary, and large, rate changes.
  • High interpolation CIC filters can push very narrowband signals (e.x. TT&C ~2MS/s max) go to sufficient ADC/DAC sample rates (e.x. 125MS/s)
  • Data reduction to not have to pass as much data to/from the front end (e.g. having to push full sample bandwidth data to/from a remote radio head)

CIC filters can be used as decimation (decrease sample rate) and interpolation (increase sample rate) multirate filters:

The basic building blocks are NN integrator and NN comb sections (hence the name), along with an interpolator (e.g. zero-stuffing expander) or decimator, increasing or decreasing the output sample rate by RR times (respectively).

Integrator Section

An integrator is simply a single-pole IIR filter with unity feedback coefficient:

y[n]=y[n1]+x[n]y[n] = y[n-1] + x[n]

This is also commonly known as an accumulator, which has the zz-transform transfer function of:

HI(z)=11z1H_{I}(z)=\frac{1}{1 - z^{-1}}

The power response is basically a low-pass filter with a −20 dB per decade (−6 dB per octave) rolloff, but with infinite gain at DC. This is due to the single pole at z = 1; the output can grow without bound for a bounded input. In other words, a single integrator by itself is unstable. - CIC Filter Introduction - Matthew P. Donadio (DSP Guru)

Note the pipelined version of the integrator which allows for more efficient digital hardware implementation (only one added between register stages):

Comb Section

The comb filter runs at the highest sample rate, fC=RfIf_{C} = R*f_{I}, in either decimation or interpolation filter form. It looks opposite of an integrator section as it subtracts the current sample value from a value MM sample periods prior; MM is the differential delay design parameter, and is often limited to M=1M = 1 or M=2M = 2:

y[n]=x[n]x[nRM]y[n] = x[n] - x[n - RM]

The corresponding transfer function at fsf_{s} is:

HC(z)=1zRMH_{C}(z) = 1 - z^{-RM}

When R=1R=1 and M=1M=1, the power response is a high-pass function with 20dB per decade gain (inverse of the integrator response). When RM1RM \neq 1, the power response looks like a familiar raised cosince form with RMRM cycles from 02π0 \rightarrow 2\pi.

Bit Growth

Due to the cascaded adders/subtracters in the CIC filter, each fixed-point, two’s complement arithmetic operation requires an additional bit of output than input, to prevent loss of precision. Given an input sample bitwidth of BinB_{in}, the output bitwidth required can be found to be:

Bout=Nlog2RM+BinB_{out} = \lceil N\log_{2}RM + B_{in} \rceil

At the expense of added quantization noise, bit growth can be controlled by rounding/scaling at some points within the CIC stages.

#TODO:

  • Look at the fred harris paper on multiplier-less CIC with sharpening that doesn’t need compensation FIR
    • Also mentioned in https://www.dsprelated.com/showarticle/1337.php
  • For bit growth, look at CIC filter register pruning
    • Also shown in http://www.jks.com/cic/cic.html and https://github.com/jks-prv/cic_prune

Discrete-Time Test of CIC

f_start = 0
f_end = 5e3
fs = 100e3
num_samples = 100000
bit_width = 16

lfm_chirp_sig = np.real(sig_gen.cmplx_dt_lfm_chirp(2**(bit_width-5), f_start, f_end, fs, num_samples))
freq, y_PSD = measurements.PSD(lfm_chirp_sig, fs, real=True, norm=True)
plot.freq_sig(freq, y_PSD, "LFM Chirp Input Spectrum", scale_noise=False)
plt.show()

png

N = 3 # number of stages
R = 8 # interp/decim factor
M = 1 # differential delay in comb stages

cic_bit_width = 21 # np.ceil(N * np.log2(R*M) + bit_width)

integ_stages = [multirate.integrator(cic_bit_width) for i in range(N)]
comb_stages = [multirate.comb(M) for i in range(N)]

integ_out = np.zeros(len(lfm_chirp_sig))
for i in range(len(integ_out)):
    temp_val = int(lfm_chirp_sig[i])
    for j in range(N):
        temp_val = integ_stages[j].step(temp_val)
    integ_out[i] = temp_val

decimate_out = multirate.decimate(integ_out, R)

cic_out = np.zeros(len(decimate_out))
for i in range(len(cic_out)):
    temp_val = decimate_out[i]
    for j in range(N):
        temp_val = comb_stages[j].step(temp_val)
    cic_out[i] = temp_val

freq, y_PSD = measurements.PSD(cic_out, fs / R, real=True, norm=True)
plot.freq_sig(freq, y_PSD, "CIC Output Spectrum", scale_noise=False)
plt.show()

png

References