Abstract
Digital filters provide a meaningful way of controlling the input spectra of communication systems over a wide range of applications. They can filter harmonics or isolate frequency bands to prevent intersymbol interference while saving you from the hassle of part procurement, PCB layout, and variations that come with their analog counterpart. Of course, digital filters are not without flaw, but their elegance and ease of use in increasingly common mixed-signal environments make them a great choice for your systems’ filtering needs. Fear not the complexities of digital design; this article demonstrates a quick and easy method for implementing simple yet powerful digital filters for RF systems.
Digital Filter Basics
Both digital filters and analog filters serve the same purpose—to ideally allow certain frequency components to pass through undistorted while completely attenuating all other frequencies. Digital filters accomplish this by summing and weighting discrete signal samples and performing this operation over the length of the input array.
The discrete implementation is shown in Equation 1 and is referred to as a finite impulse response (FIR) filter. More taps, N, in an FIR filter means sharper responses, flatter pass bands, and steeper transition bands. The main drawback of increased tap count is resources. Each tap represents time delay and computational resources, so when N grows large, so does the time delay and power consumption. FIR filters are inherently stable because there is no feedback used, and therefore no risk of driving an input that causes an output to compound and grow unbounded. FIR filters can also have a linear phase response, which makes them especially useful in RF applications where timing and group delay are important.
Let’s take a look at what implementing a digital filter would look like on a high speed data acquisition platform. I will introduce the lab setup and how the results were verified, as well as go over the specifications of the system used. We’ll see what a real and practical digital filter produces for results when filtering both single tones and their harmonics, as well as multitone test vectors that demonstrate the filter profile over a larger band of frequencies.
The scope of this article will not extend to applications of infinite impulse response (IIR) filters and will stay constrained to 192-tap filters with a sample rate of 1500 MSPS.
Lab Setup
The platform used to demonstrate a real digital filter is Analog Devices’ AD9082mixed-signal front end (MxFE®). The data and results from the filter implementations are verified using the platform’s loopback mode connected to a spectrum analyzer. The AD9082 MxFE was set up for testing by interfacing with ADI’s ADS9 development platform for controlling the analog-to-digital converters (ADCs) and digital-to-analog converters (DACs), and to process the output data. The user guide for this configuration can be found here. A Rohde & Schwarz SMW200A vector signal generator was used to generate 5G-NR test vectors as well as single and multitone vectors, and a Rohde & Schwarz FSW was used to measure the output spectrum from the DAC.
The 192-tap FIR digital filter block (PFILT) is located directly after the ADC cores. To keep things simple, all tests shown in this article are run with one ADC channel being driven single-ended with all 192 taps enabled. The sampling rate of the system was set to 1500 MSPS on both the transmit side and receive side; therefore, all spectra plotted will cover up to Nyquist, or (1500 MHz)/2 = 750 MHz.
Verification Method
Figures 3 and 4 show a comparison between the ADC data and a spectrum analyzer capture from the DAC outputs using an internal loopback. The spectral representation of these two signals is nearly identical, with a small variation in the noise floor due to the resolution bandwidth of the analyzer. This step was done to confirm that the ADC data after the PFILT matches the output signal from the loopback path.
A 5G-NR test vector was also used to test the accuracy of indirect loopback by using a signal with a more complex spectrum. Figure 5 shows the power spectrum of the test vector from the Rohde & Schwarz SMW200A vector signal generator compared to the DAC output with loopback.
Results
Filter coefficients for the profiles shown in the results were generated using the MATLAB® Filter Designer and retrieved using a Python script that captured trace data from the spectrum analyzer.
| Filter type | Low-Pass Chebyshev Window |
| Taps | 191 |
| Center Frequency | – |
| Bandwidth (6 dB Cutoff) | 100 MHz |
| Sidelobe Attenuation | 60 dB |
Two plots were generated from each MATLAB Filter Designer output. The first output is the ideal filter profile that shows the digital filter response, which is possible with a 192-tap FIR filter with double-precision floating-point values. Because the FIR filter takes four hex value codewords as register inputs, some precision is lost while converting to this format from the double values in MATLAB. The expected effects of the datatype conversion on the filter response are shown using a PFILT model and are compared to the MATLAB Filter Designer output.
Figure 7 shows the results of a 100 MHz test tone-filtered and looped back to the DAC of MxFE. The harmonics generated by nonlinearities in the ADC buffers have been filtered by the PFILT, bringing the spurious free dynamic range (SFDR) from 55.9 dB to 81.9 dB. The implemented filter shows a slower roll-off to 60 dB attenuation than the simulated filter. The group delay was shown to remain flat in the pass band at (N-1)/2 = 95.5 samples for 192 taps.
A multitone test vector was generated using the R&S SMW200A. This train of tones will conform to the shape of a filter over a broad range of frequencies. The power level of each tone was kept to approximately –40 dBm to avoid intermodulation distortion. As such, the DAC output response with and without filtering is shown with a reference level of –40 dBm.
| Filter type | Band-Pass Chebyshev Window |
| Taps | 191 |
| Center Frequency | 200 MHz |
| Bandwidth (6 dB Cutoff) | 100 MHz |
| Sidelobe Attenuation | 80 dB |
Figures 10 and 11 show a comparison between a 200 MHz CW at –15 dBm. The signal was run through the digital data path and looped back indirectly to the DAC cores. Without the programmable filters active in Figure 10, the harmonic at 2fc measured at –73.88 dBm. With the PFILT active in Figure 11, not only is the harmonic filtered out but also the noise floor of the data path is reduced and displays the typical Chebyshev out-of-band ripple. Group delay also remained flat in the pass band for the band-pass filter.
Finally, Figure 12 shows the results of the band-pass filter applied to the DAC outputs using the same multitone test vector. The pass band increases the noise floor by 4.2 dB but reduces the noise floor in the stop band by 2 dB to 3 dB following the common Chebyshev out-of-band ripple.
| Lane Rate (Gbps) | 12.375 | |
| Min JESD Latency (#DAC Clock) | 230 | |
| Dpath Latency | 1038 | |
| Minimum Latency | #DAC Clocks | 1268 |
| ns @ DAC Rate | 211.333 | |
| Nom JESD Latency (#DAC Clock) | 864 | |
| Nominal Latency | #DAC Clocks | 1902 |
| ns @ DAC Rate | 317,000 | |
| Max JESD Latency (#DAC Clock) | 1500 | |
| Maximum Latency | #DAC Clocks | 2538 |
| ns @ DAC Rate | 423.000 | |
Latency
Latency through the loopback configuration was measured using a hardware test bench with equal length coax cables. Total latency measured was 500 ns.
| ADC Rate (GSPS) | Total DCM | Lane Rate (Gbps) | Nominal Latency | |
| #ADC Clocks | ns @ ADC Rate | |||
| 1.5 | 1× | 12.375 | 373 | 248.667 |
Tables 3 and 4 show the expected latency for the configuration in which the AD9082 was run. The sum of ADC and DAC rates gives minimum to maximum value. 500 ns is observed to lie within this range.
Keeping the propagation delay in wireless systems below 1 µs is adequate for ensuring negligible impact on overall network latency and maintaining coherency between link partners. This can apply to 802.11b/g, 4G LTE, and even 5G-NR cellphone synchronization. Therefore, demonstrating a latency of 500 ns ensures that even with digital filter delay, the system remains interoperable as a wireless receiver platform for your designs.
Conclusion
RF signal chains perform the necessary analog processing to get your signal from waves to bits. However, hardware-side issues like parasitics and power amplifier nonlinearities as well as wireless challenges like multipathing and fading degrade the quality of the signal and turn the signal chain into a nonideal transfer function. Compensating for attenuation and spectral losses is an important step to ensure your data is accurate and reliable. Using ADI’s AD9082 MxFE with programmable f iltering allows the user the ability to easily design and implement useful filter profiles with sharp transition bands over a wide range of frequencies.
