The Polyphase Filter Bank Technique

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Version 1 (PDF): Jayanth Chennamangalam

Oct 2016 note: More recent discussion of polyphase filterbanks is given in:


Introduction to the PFB

In digital signal processing, an instrument or software that needs to do Fourier analysis of some input signal performs a Discrete Fourier Transform (DFT). The straightforward application of the DFT on an input signal suffers from two significant drawbacks, namely, leakage and scalloping loss.

DFT leakage is the phenomenon in which, depending on the sampling frequency and the number of points in the transform, an input tone appears in more than one output frequency bin. (For the purpose of this memo, the input signal is a time series and the output is in the frequency domain.) If this tone is not strong enough, this effect can go unnoticed. But in the case of, say, a strong radio frequency interference (RFI) signal, the leakage can drown out astronomical signals of interest in the nearby bins. This effect is shown in Figure 1 and is described in more detail in the following section.

Demonstration of DFT leakage
Figure 1. Demonstration of DFT leakage - a tone at 5.1MHz, sampled at 128MHz, and Fourier-transformed with 64 points, appears to varying levels in all the output frequency bins.

DFT scalloping loss is the loss in energy between frequency bin centres due to the non-flat nature of the single-bin frequency response.

The polyphase filter bank (PFB) technique is a mechanism for alleviating the aforementioned drawbacks of the straightforward DFT. The PFB not only produces a flat response across the channel, but also provides excellent suppression of out-of-band signals, as shown in Figure 2. On FPGAs, a PFB typically consumes about 1.5 times more resources than a direct FFT. In many cases, the data quality advantages outweigh this increase in cost.

Spectrometers and correlators are typical beneficiaries of the PFB technique.

Single-bin response comparison
Figure 2. Comparison of the single-bin frequency response of a PFB with a direct FFT. Here, the length of the polyphase window is 8 times the length of the FFT.

The Math Behind the PFB

The DFT of a sequence of values x(n), sampled at a rate fs, is defined as

Memo pfb eq 1.png

where, for the purpose of this memo, x(n) is a time series and X(k) is in the frequency domain. From this definition it is clear that the DFT operates on a finite length N of time samples. Stated another way, the input to the DFT is equivalent to the product of an infinitely long time series and a rectangular window that fits over our time interval of interest. This implies that the frequency domain response of a complex sinusoidal waveform using the DFT would be the convolution of the Fourier Transform of the sinusoid and that of the rectangular window. (The response would be the sampled convolution of the Discrete Time Fourier Transform (DTFT) of the sinusoid and that of the rectangular window, to be precise.) Since the Fourier Transform of the complex sinusoid is a shifted delta function, the result of the convolution is the Fourier Transform of the window - a sinc function - centred at the location of the delta function. Fortuitous combinations of N, fs, and the input frequency can be such that the zeroes of the sinc function coincide with the bin centres of all other frequencies, in which case the problem is non-existent. But in general, the frequency domain bin centres lie at non-zero locations on the sinc function. That is, a single tone appears to some level in all the frequency bins of the DFT output. Since the energy contained in the input frequency bin 'leaks' into other frequency bins, this effect is called DFT leakage.

The solution to DFT leakage involves suppressing the side-lobes of the aforementioned sinc function by changing the single-bin frequency response of the DFT to approximate a rectangular function. This is achieved as follows.

Instead of taking an N-point transform directly, a block of data of size N x P = M is read, and multiplied point-by-point with a window function (in other words, the data is 'weighted'). As mentioned before, the shape of the window function determines the shape of the single-bin frequency response. Since we wish the single-bin frequency response to resemble a rectangular function as much as possible, we choose its Fourier Transform pair, the sinc function, as our window function. Once the multiplication is done, the block of data is split into P subsets of length N each, and added point-by-point. This array is then passed to a regular DFT routine to get an N-point transform that exhibits less leakage. This method is presented graphically in Figure 3.

Polyphase filtering
Figure 3. Graphical depiction of polyphase filtering. x(i) is a time series of length M = 1024 samples, multiplied point-by-point with the window function w(i) (a sinc function), also of the same length. The product is split into P = 4 blocks of length N = 256 samples each, and summed. This summed array of length N = 256 samples, shown at the bottom, on the right, is then input to a routine that takes a 256-point Fourier Transform. (Image courtesy: Dale Gary)

The weighting/windowing can be thought of as a filtering process in which the elements of the window function are the filter coefficients. Mathematically, this process is given by

Memo pfb eq 2.png

where the sub-filter coefficients h(n + pN) correspond to what are called P-tap 'polyphase sub-filters'. The N such polyphase sub-filters that make up this operation, together with the following DFT stage, are collectively called a 'polyphase filter bank' ('PFB'). A realization of this filter bank is shown in Figure 4.

Polyphase filter bank
Figure 4. The FIR filter structure realization of a polyphase filter bank with P = 3 taps and N sub-filters. The commutator at the left rotates in the clockwise direction, and makes one complete rotation in the duration of one unit delay. The output of this structure is y(n), which is the input to an N-point DFT.

Since h(n+ pN) is a decimated-by-N version of h(n), if the original filter has a pass-band width of fs / N, each sub-filter has a pass-band width of fs. (In other words, the original filter h(n) is designed such that it has a pass-band width of fs / N.) Since complex input data has a bandwidth of fs, each sub-filter is essentially an all-pass filter. The only difference between the sub-filters is their phase response, which is why this structure is called a 'polyphase' filter bank.

This method is also known as 'weighted overlap-add' ('WOLA'), or 'window pre-sum-FFT'.

To suppress the sidelobes of the single-bin frequency response further, the sinc function that makes up the filter coefficients can be weighed with a smooth function, such as the Hanning window.


Various implementations of the PFB are available online. The CASPER library comes with the pfb_fir and pfb_fir_real blocks that can be used with an FFT block. A stand-alone spectrometer program written in C, that reads 8-bit, complex, dual-polarisation data from a file and performs the PFB technique, and a CUDA equivalent, are available for download from the VEGAS git repository.


  1. Gary, D. E., Figures 6 and 7, and associated captions
  2. Lyons, R. P., Understanding Digital Signal Processing, Section 10.4 (Polyphase Filters) and Section 13.20 (A Practical Spectrum Analyzer)
  3. Proakis, J. G., and Manolakis, D. G., Digital Signal Processing, Section 10.5.2 (Polyphase Filter Structures)
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