Overcome 5G mmWave measurement issues

Signal analyzers and best measurement practices can minimize errors that result from losses at mmWave frequencies.

Deployment of 5G Frequency Range 2 (FR2) has only just begun. As mmWave deployments come online, consumers will reap the benefits of faster data rates though wider bandwidth. The higher frequencies of mmWave signals bring with them signal losses, both in deployment and in test setups. You can, however, minimize signal losses and improve measurements.

Most, but not all FR2 resides in the mmWave spectrum, currently defined as 24.25 GHz to 52.6 GHz. As Figure 1 shows, mmWave spectrum is generally considered to be the band of spectrum that resides between 30 GHz and 300 GHz.

The amount of bandwidth available in the mmWave frequency range enables tremendous uplink and downlink speeds, and the relatively small size of mmWave transmissions make mmWave ideal for operating in dense urban environments with many devices. In short, FR2 is where the bulk of 5G’s promised benefits reside in terms of speed, bandwidth, and latency for both standard wireless communications as we know it today and for enabling entirely new use cases.

The promise of mmWave comes with tradeoffs that include path loss (due to the poor propagation of mmWave signals), increased signal noise (due to the inherent high noise level of wideband signals), and poor frequency responses (due to the small margin for error on wideband signals). Further complicating matters, components designed for mmWave devices are so compact and tightly integrated that they leave no place to probe, which creates the requirement for radiated tests, also known as over the air-the-air (OTA) tests. These challenges can make measuring mmWave signals arduous, preventing you from understanding the true performance of the device under test.

This article will discuss these specific challenges to mmWave device testing in further detail. It will also present strategies for overcoming these challenges with a signal analyzer using modifications to signal path, signal conditions, and the reference plane, enabling accurate, repeatable measurement of 5G mmWave signals.
Path loss
One of the most significant challenges to overcome for FR2 transmission is that mmWave signals do not propagate as far as microwave frequencies. The atmosphere can easily absorb mmWave signal energy, as can rain attenuation and diffraction. Moreover, mmWave signals barely penetrate trees, foliage, building walls, freeway overpasses, and other infrastructure.

When making measurements on mmWave components and systems, you must content with finicky propagation characteristics of mmWave signals in the test setup. For example, RF cables and accessories can hamper signal strength. Also, any skew in a flange connection (Figure 2) on the test and measurement equipment can cause unwanted reflections that degrade signal quality and power. The OTA testing requirement also complicates matters because electromagnetic field behavior and characteristics vary depending on the distance from the antenna.

Excessive path loss at mmWave frequencies between a measurement instrument and device under test (DUT) results in a lower signal-to-noise ratio (SNR) for signal analysis. A lower SNR leads to less accurate transmitter measurements such as error vector magnitude (EVM), adjacent channel power ratio (ACPR), and spurious emissions measurements. To compensate for path loss, engineers typically reduce the signal analyzer’s attenuation. Reducing the input attenuation of the signal analyzer to 0 dB may not, however, be enough to compensate for the low SNR adequately to result in accurate measurements.

Signal analyzers let engineers optimize for specific types of measurements. For example, signal analyzers offer a choice of multiple RF signal paths to help overcome path-loss issues related to signal propagation and other factors. A signal analyzer can, for example, apply attenuation at higher power levels or a preamplifier at lower power levels to measure a variety of input signals.

The types of RF signal paths typically available include:

• The default signal path is ideal for measuring low-level signals with a bandwidth of less than 45 MHz. In this path, the input signal travels through the RF attenuator, preamplifier, and preselector before reaching the mixer.
• A microwave preselector bypass path is better suited for analyzing wideband vector signals such as mmWave signals because it allows wide-bandwidth signals to pass unimpeded through the RF chain.
• Making EVM measurements and other measurements that test transmitter modulation quality at higher power levels is generally best done using a low-noise signal path. The gain of the amplifier, frequency response, and insertion loss are compounded at higher frequencies. This path reduces path loss and the frequency responses and noise created by the preamplifiers and switches, improving signal fidelity and increasing measurement sensitivity.
• A full-bypass signal path — which avoids multiple switches in the low-band switch circuitry and bypasses the microwave preselector — can reduce loss at mmWave frequencies by up to 10 dB compared with the default signal path. The full bypass path offers the advantages of lower path loss, higher signal fidelity, and increased measurement sensitivity, but does have some downsides including in-band imaging and low SNR for testing lower power levels.

External mixers extend a signal analyzer’s the frequency range and eliminate insertion loss caused by the test setup cables and accessories between the signal analyzer and the DUT. The cable loss can be up to 5 dB/m and can reduce the test system’s SNR. Adding an external mixer, which you can place closer to the DUT, shortens the mmWave signal path, which reduces the path loss and increases the SNR.

Wideband noise
Wideband signals inherently have higher noise and a lower SNR because energy spreads across the signal’s entire bandwidth. Hence, the wider the bandwidth, the lower the inherent signal integrity, the more vulnerable it is to noise from the test setup and other factors, and thus the lower the SNR.

Noise is part of all communications channels. A transmit signal needs to compete with the channel’s noise floor to get better sensitivity at a receiver. The Shannon-Hartley Theorem specifies the maximum rate at which information can be transmitted over a communication channel within a specified bandwidth with the presence of noise.

Increasing analysis bandwidth introduces more noise to a signal analyzer, which reduces SNR. The low SNR can result in poor EVM and adjacent channel power ratio (ACPR) measurements that do not accurately reflect the performance of the DUT.

To improve EVM measurement accuracy, you should choose the optimum levels for the signal analyzer’s mixer and digitizer. For best results, choose the optimum phase noise configuration of the local oscillator (LO) to achieve the best results.

Wireless standards specify transmitter measurements at the maximum output power. You can, however, attenuate the power level at the first mixer of a signal analyzer to ensure that the high-power input signal won’t distort in the signal analyzer. The input signal level can be lower than the optimum mixer level in OTA tests and test setups with a significant insertion loss, for example. Using a built-in preamplifier can be useful for low-input-level test scenarios. A built-in preamplifier provides a better noise figure, but a poorer intermodulation-distortion-to-noise-floor dynamic range.

The input mixer-level setting is a trade-off between distortion performance and noise sensitivity. A higher input mixer level yields better SNR, while a lower input mixer level offers better distortion performance. The measurement hardware, characteristics of the input signal, and specification test requirements combine to dictate the optimum mixer-level setting. As shown in Figure 3, applying an external low-noise amplifier (LNA) at the front end can also reduce the system noise figure — with or without the internal preamplifier — can also help optimize the input level of the mixer.

Signal analyzers also bring choices for phase noise optimization. The optimum phase noise performance of a signal analyzer for modulation analysis is dictated by the analyzer’s phase-noise profile as well as the operation frequency, bandwidth, and subcarrier spacing (OFDM signal) of the input signal. A wide offset phase noise setting is generally for better 5G NR mmWave modulation analysis.

Frequency response
The components in the path between the test instrument and the DUT will impact the test setup’s accuracy. With wide bandwidths and mmWave signals, small margins for error force RF engineers to look for new ways to reduce frequency response errors. These errors occur at different frequencies, affecting phase and amplitude responses. A signal analyzer provides an internal calibration routine to correct its frequency responses.

Correcting for frequency response errors is required to extend the measurement accuracy from the signal analyzer’s input port to the DUT’s test port. It is possible to configure corrections to both amplitude and phase with a signal analyzer to remove frequency responses. Correcting for magnitude and phase errors in the test network also improves the accuracy of measurements. There are many instruments and accessories available to help correct frequency responses.

Conclusion
In summary, the use of mmWave signals in 5G enables dramatic improvements in speed and latency, offering the performance to significantly boost traditional wireless data applications and enable entirely new use cases such as ultra-low-latency communications (URLLC), cellular vehicle-to-everything (C-V2X) communications, and massive machine-type communications (mMTC). The characteristics of mmWave signals, however, introduce new measurement challenges to accurate, repeatable measurement. Modern signal analyzers provide the flexibility in hardware and software to offset these challenges, including reducing path loss, improving signal condition, and correcting for frequency response errors.

Dylan McGrath is a veteran technology journalist and former editor in chief of EE Times. He is now a senior industry solutions manager at Keysight Technologies.