DSP Algorithms for RF Systems

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Figure 8: The upconverted BPF is the summation of two complex-upconverted LPFs which interfere with one another when summed. The BPF upconversion in Figure 8 is the result of the summation of these two responses.

Designing an FIR Band Pass Filter with Remez

Band pass filters are characterized by having attenuation at both high frequency and low frequency with a pass-band in between. Band-pass filters can designed to have real coefficients which have an even-symmetric response or they can be upconverted to complex band-pass to have non-symmetric response which is useful in channelization or in applying a Hilbert transform.

Figure 4: The impulse responses of the LPF and HPF filters.

Designing an FIR High Pass Filter with Remez

While low-pass filtering (LPF) is ubiquitous high pass filters (HPF) can be needed depending on the RF environment or for specific algorithms. The previous post FIR Low Pass Filter Design with Remez demonstrated how to use the remez() function in the SciPy package to design LPF filter weights. The following blog will demonstrate how to use remez() to design HPFs as well as designing LPF filters and upconverting them.

FIR Low Pass Filter Design with Remez

The focus of this blog is to describe the low pass filter design process with the remez() function, which is one of many ways to create FIR filter weights. The filter parameters of:

  • filter length,
  • pass-band,
  • stop-band,
  • cut-off frequency,
  • transition bandwidth,
  • and sidelobe attenuation

will be discussed, including their impact on the frequency response and how their trade-offs effect the design.

The Stone Carver mindset: create a masterpiece one chip at a time.

Engineering Mindset: Be a Stone Carver, not a Brick Mason

Maintaining the proper mindset is crucial to proper mental health and the longevity of your career. Many come to believe that engineering is linear and procedural, like a brick mason building a brick wall. The brick mason operates by adding more and more material; bigger is better, more expensive and more profitable.

A more effective mindset is that of the stone carver. Quality and craftsmanship are key for the stone carver. A stone carver designs one chisel stroke at a time. The stone carver removes what does not belong until the final design is revealed. For the stone carver, less is more: removing more material allows for more detail, more polish and represents an overall a higher quality piece of art.

The xylophone is struck by a mallet

Why are Sinusoids used in DSP and RF?

The goal of this post is to shed some light on why sinusoids are useful in DSP and RF communication.

Sinusoids are used by humans to describe the cyclical nature of the universe. They are a useful mathematical abstraction. Each year is 365 days and then a new year begins. Spring is followed by summer, fall, winter and then spring begins again. Low tide, high tide and then low tide again. A spinning ceiling fan. The rotation of the tires on your automobile. Reeling your fishing rod.

Figure 5: An example of angles between 0 and 2pi.

Basic Rules for Complex Numbers

The mathematics of DSP and complex numbers can be confounding.

Understanding how j = \sqrt{-1} was difficult when I first started my DSP education and it’s still not something I fully grasp. When starting out in your DSP education sometimes it is enough to simply understanding how the tools and procedures are applied, rather than how they are derived.

Figure 3: An example of a time invariant IIR filter. Note that an IIR filter can be transformed into an FIR filter by setting all feedback coefficients to zero.

Time Invariant and Time Varying Filters

The response of time invariant filters is independent of time and have filter weights which do not change over time. Time invariance (TI) is a nice quality for filters to have when analyzing them mathematically and have many applications in which adaptation is not needed. Time-varying filters (TV) are common in radio receivers such as equalizers, automatic gain control and polyphase filters. For example an equalizer is time-varying because the filter weights are dependent on previous input samples. Understanding TI filters is necessary for having a proper DSP foundation before moving onto TV filters.

Figure 1: A moving average filter is implemented by delaying the input signal x[n] and then scaling by 1/3 and summing all results.

Digital Signal Processing through the Lens of the FIR Filter

Digital signal processing has two components: signals and filters.

A signal is a time-series which has information (RF vocabulary) and filters are useful in applying a desired affect to a signal. These affects can be:

  • enhancing information elements of a signal,
  • attenuating or minimizing noise,
  • or some other modification.

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