The goal of the approach is to speed up RRC filtering with (very) large oversampling ratio. To this end I calculate the prime factors of the oversampling ratio and RRC filter the signal with each factor feeding the previous result to the next. To test this approach I compare it, using the same base function rrcosfilter (not the one from commpy), to the case where the filtering is done in one go. The spectrum agrees quite well in-band, but out-of-band I get some aliasing effect with the factorized approach. How can I avoid these in-band copies and still use this factorized approach?

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The main script:

from <custom lib> import *
import numpy as np
from matplotlib import pyplot as plt

def fft(x, sample_rate):
    Y_dB = 20*np.log10(np.abs(np.fft.fftshift(np.fft.fft(x))))
    f = np.fft.fftshift(np.fft.fftfreq(len(Y_dB), 1/sample_rate))
    return f, Y_dB

sample_rate = 10
symbol_rate = 1

pam = PAM(1.0, 1000, symbol_rate, 16)

syms = pam.get_symbols()
# own redefined function
x = rrcosfilter(syms, sample_rate//symbol_rate, 0.3, 10)

y = pam.get_rrc_filtered(sample_rate)

fx, X_dB = fft(x, sample_rate)
fy, Y_dB = fft(y, sample_rate)

fig, ax = plt.subplots()

ax.plot(fx, X_dB, label='direct')
ax.plot(fy, Y_dB, label='factorized')
ax.set_xlabel('Frequency (Hz)')
ax.set_ylabel('Power (dB)')


The functions:

import numpy as np
from numpy.typing import NDArray
from typing import Optional, List
import commpy.filters as cmpfilters

def get_prime_factors(n: int) -> List[int]:
    Gets the prime factors of the given number.

    :param n: the number to factorize
    :type n: int
    :return: a list of factors
    :rtype: list[int]
    i = 2
    factors = []

    while i**2 <= n:
        if n % i:
            i += 1
            n //= i
    if n > 1:

    return factors

def rrcosfilter(x: NDArray, samples_per_symbol: int, alpha: float, N: int) -> NDArray:
    # zero pad the data
    x_padded = np.zeros(samples_per_symbol*len(x))
    x_padded[::samples_per_symbol] = x

    # the filter length in number of samples, i.e. after oversampling of the symbols
    filter_length = 2*N*samples_per_symbol + 1
    psf = cmpfilters.rrcosfilter(filter_length, alpha, 1, samples_per_symbol)[1]

    y = np.convolve(psf, x_padded)
    # remove filter tails
    return y[N*samples_per_symbol:-N*samples_per_symbol]

def rrcosfilter_factorized(x: NDArray, Rsym: int, Rs: int, alpha: float, N: int) -> NDArray:
    Applies a root-raised cosine filter to the given signal. This implementation factorizes the oversampling ratio and does the filtering in multiple steps to speed up the process.
    :param x: symbols
    :type x: NDArray[float]
    :param Rsym: symbol rate (Baud)
    :type Rsym: int
    :param Rs: filtered sample rate (sps)
    :type Rs: int
    :param alpha: roll-off factor
    :type alpha: float
    :param N: one-sided filter length in number of symbols
    :type N: int
    :return: filtered symbol sequence
    :rtype: NDArray[float]
    # the oversampling rate
    samples_per_symbol = Rs//Rsym

    # check if the upsampling is fractional
    if abs(samples_per_symbol - Rs/Rsym) > 0:
        logger.error("Fractional oversampling is not implemented. You tried an upsampling factor of %s.", Rs/Rsym)
        return None

    # pad beginning and ending of data with symbols to make it cyclic
    x_cylcic = np.concatenate((x[-N:], x, x[:N]))

    # decompose the oversampling rate into its prime factors
    factors = get_prime_factors(samples_per_symbol)

    # apply the RRC filter for every factor and pass the filtered signal to the next iteration
    y = x_cylcic
    for factor in factors:
        y = rrcosfilter(y, factor, alpha, N)

    # remove cyclic pre- and post-symbols
    return y[N*samples_per_symbol:-N*samples_per_symbol]
  • $\begingroup$ I'm missing where you actually factorize the filter; can you point me to the function that calculates the taps of the factor filter(s)? $\endgroup$ Commented May 17 at 14:07
  • $\begingroup$ The filter is not factorized, but the oversampling ratio. I might have worded this not well enough. My apologies for this. In rrcosfilter_factorized the prime factors of the oversampling ratio are found through get_prime_factors. For each factor the rrcosfilter function is called which performs the normal root-raised cosine filtering using the given oversampling ratio (factor). $\endgroup$ Commented May 17 at 14:18
  • $\begingroup$ then what you see would seem pretty reasonable. What you've built is not equivalent to your single step RRC, by design (unless I am missing something very fundamental about the RRC, but I don't think I do – your spectrum plot confirms that.) $\endgroup$ Commented May 17 at 14:28
  • $\begingroup$ oh and you're using far too few symbols (if 1000 is the number of symbols) to estimate a spectrum to a sufficient degree. You should, instead of testing your filter with pseudo-white data, simply do an explicit analysis – your filter is deterministic, after all – with freqz $\endgroup$ Commented May 17 at 14:30
  • $\begingroup$ Okay, I assumed filtering the signal over and over with a lower oversampling ratio would have the same effect as filtering it once with the total oversampling ratio or at least not lead to this aliasing effect. $\endgroup$ Commented May 17 at 14:41


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