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5G promises substantial improvements in wireless communications, including higher throughput, lower latency, improved reliability, and better efficiency. Achieving these goals requires a variety of new technologies and techniques: higher frequencies, wider bandwidths, new modulation schemes, massive MIMO, phased-array antennas, and more.


These bring new challenges in the validation of device performance. One of the key measurements is error vector magnitude (EVM), which is an indicator of quality for modulated signal as they pass through a device under test (DUT). In many cases, the EVM value must remain below a specific threshold—and getting an accurate measurement requires that the test system itself be very clean (i.e., have a low EVM itself). This includes all fixtures, cables, adaptors, couplers, filters, pre-amplifiers, splitters, and switches, between the DUT and the measurement system.


At 5G bandwidths and frequencies, the test fixture can impose a significant channel frequency response on the test system and adversely affect EVM results. Hence, the measurement now includes the characteristics of the test fixture and the DUT—and this makes it difficult, if not impossible, to determine the true performance of the DUT.


Calibration can move the test plane from the test instrument connector to the input connector of the DUT (Figures 1, 2). Keysight has created a solution that uses a NIST-traceable reference comb generator to enable complete channel characterization of the test fixture on both sides of a transceiver (or any other component or device).

Figure 1. This uncalibrated test system has unknown signal quality at the input to the DUT (A1’). A common mistake is to simply use equalization in the analyzer (A2), but this occurs after the DUT and it also removes some of the imperfect device performance we’re trying to characterize.

Figure 2. In this calibrated test system, the system and fixturing responses have been removed, enabling a known-quality signal to be incident to the DUT (B1). The analyzer errors can also be removed (B2).


Figure 3 shows the uncalibrated test fixture equalizer response for a 900 MHz BW signal at 28 GHz. The upper trace shows the amplitude response with a significant roll off at the upper end of the bandwidth. The lower trace shows the phase response, which also has considerable variation over the bandwidth. These imperfections would limit EVM to being no better than about 5 percent.

Figure 3. These OFDM frequency response corrections for an uncalibrated system show variations of nearly 7 dB in raw amplitude and 45 degrees of phase across a 900 MHz bandwidth at 28 GHz



Figure 4. Here is the same OFDM response for a calibrated system, showing variations of only 0.2 dB and 2 degrees. The resulting signal EVM dropped to less than 1 percent from more than 5 percent.


Figure 5 shows the demodulation results after calibration for single-carrier 16QAM signal nearly 1 GHz wide. The upper-left trace shows a very clean constellation diagram. The lower-left trace shows the spectrum with a bandwidth of approximately 1 GHz. The upper-left trace shows the equalizer response in both magnitude and phase: both of these are nearly flat, indicating the equalizer is not compensating for any residual channel response in the test fixture. The middle lower trace shows the error summary: EVM is approximately 0.7 percent, which is a very good result. This system would be ideal for determining a device’s characteristics.


Figure 5. Calibration enabled the signal generation of a 1 GHz wide signal with an EVM of less than 0.7 percent at 28 GHz. This EVM occurs at the input plane of the DUT.


In pursuit of tremendous improvements in cellular network capability, 5G is using new technologies that pose many challenges to testing. Fortunately, calibration will help ensure that we’re measuring the true performance of the DUT without the effects of the test fixture.


We can help you learn more about the testing of 5G wireless technologies.


Follow our Next Generation Wireless Communications blog and connect with our industry and solution experts as they share their experiences, opinions and measurement tips on a number of cellular and wireless design and test topics that matter to you.

Once you have taken the steps described in the Simple Steps to Optimize Battery Runtime blog, you still have opportunities to reduce power consumption in your battery-powered device. Be sure to measure the actual current consumption before and after each change, and try to understand why the results are as observed. The more understanding you develop, the better you will be at predicting the effects of future changes. This will help you get future products to market faster with optimized battery runtime.


Hardware optimizations

Consider using a simple analog comparator instead of an analog/digital converter (ADC) to trigger certain functions. The ADC is likely to be more accurate and faster than the comparator, but it has longer startup time and consumes more current. The comparator continuously compares signals against a threshold, and for some tasks, this may be sufficient. For cases where you need the accuracy and versatility of the ADC, turn off internal voltage references on the ADC and use Vcc as the reference voltage if possible.


Use two-speed startup procedures that rely on relatively slow RC timers to clock basic bootup tasks while the microcontroller unit (MCU) waits for the crystal oscillator to stabilize. Be sure to calibrate these internal RC timers or buy factory-trimmed parts.


Firmware optimizations

Use event-driven code to control program flow and wake up the otherwise-idle MCU only as necessary. Plan MCU wakeups to combine several functions into one wakeup cycle. Avoid frequent subroutine and function calls to limit program overhead, and use computed branches with fast table lookups instead of flag polling and long software calculations. Use single-cycle CPU registers for long software routines whenever possible.


Implement decimation, averaging, and other data reduction techniques appropriately to reduce the amount of data transmitted wirelessly. Also, make sure to thoroughly test various wireless handshaking options in an actual usage environment to strike the ideal balance between wasting time on unsuccessful communication attempts and performing excessive retries.


Your oscilloscope will probably be useful in obtaining quick measurements for these current waveforms, and depending on the communication protocol, an oscilloscope may be the only instrument with the necessary bandwidth to make such measurements. However, once you know the bandwidth of your signal, you may be able to use a DC power analyzer or device current waveform analyzer to make these measurements. These devices will make measurements with better precision and to provide more detailed analysis, such as automatic current profiles.


By implementing these strategies and measuring current consumption throughout your development process, you will quickly optimize battery runtime and drive success in IoT and other battery-driven applications for you and your customers.


Learn more about maximizing battery life of IoT smart devices by downloading helpful applications notes and webcasts from Keysight.


Follow our Next Generation Wireless Communications blog and connect with our industry and solution experts as they share their experiences, opinions and measurement tips on a number of cellular and wireless design and test topics that matter to you.

After you have selected your microcontroller unit (MCU), there are several simple steps you can take to optimize battery runtime. Correctly configuring and testing your hardware and firmware can help you to develop the optimal IoT device configuration.


Power budget

Begin by creating a theoretical power budget for your device. Using the MCU’s data sheet and manual, consider a complete cycle of events, such as waking, collecting data, processing data, turning on the radio, transmitting data, turning off the radio, and returning to sleep. Multiply the current by the duration of each step, and add the values to obtain a projected total for a typical operational cycle. Be sure to include the current consumed while the device is in its longest sleep mode; even nanoamps add up over long periods of time. Your MCU vendor should have software that helps you estimate current drain associated with various operational parameters, and you can use a DC power analyzer, digital multimeter (DMM), or device current waveform analyzer to fine tune the estimated values.


Hardware configuration

Begin by optimizing the clock speed at which the MCU runs. The current consumption for many MCUs is specified in units of µA / MHz, which means that a processor with a slow clock consumes less current than a processor with a faster clock. However, a processor working at 100% capacity will consume the same amount of energy at 10 MHz as at 20 MHz, because the 20-MHz processor will consume twice the current for half as long. The conclusion is that for code segments where the processor is largely idle, you can save current by running the MCU more slowly.


Next, optimize the settings associated with data sampling. These settings include the frequency with which the sensor wakes up to collect data, the number of samples taken, and the ADC sampling rate. There is often a tradeoff between measurement accuracy and these sampling parameters, so set the sampling parameters to minimize current drain while delivering acceptable accuracy. Similarly, you may be able to change the rate at which the MCU updates the device display, requests data from sensors, flashes LEDs, or turns on the radio.


Finally, carefully examine the various idle, snooze, sleep, and hibernation modes available on your MCU. For example, some MCUs have sleep modes that disable the real-time clock (RTC), and disabling the RTC may reduce your sleep current consumption by a factor of six or more. Of course, if you do this, you will likely need some mechanism to recover the date and time, perhaps through a base station.


Firmware options

Design your program to finish each task quickly and return the MCU to sleep. Cycle power on sensors and other peripherals so that they are on only when needed. When you cycle sensor power, remember power-on stabilization time to avoid affecting measurement accuracy. For ultra-low-power modes, consider using a precision source/measure unit (SMU) to make very accurate current measurements, especially when you have the option to power the MCU at different voltage levels.


Consider using relatively low-power integrated peripheral modules to replace software functions that would otherwise be executed by the MCU. For example, timer peripherals may be able to automatically generate pulse-width modulation (PWM) and receive external timing signals.


Use good programming practices, such as setting constants outside of loops, avoiding declaring unnecessary variables, unrolling small loops, and shifting bits to replace certain integer math operations. Also, use code analysis tools and turn on all compiler optimizations.


Test and learn

Finally, use your instruments’ software tools to analyze the actual current consumption frequently as you develop the MCU code. These tools may include a complementary cumulative distribution function (CCDF) or automatic current profile, and they will give you information to refine your power budget. Observe and document how your coding decisions affect current consumption to optimize the present program and give you a head start on subsequent projects.


Learn more about maximizing battery life of IoT smart devices by downloading helpful applications notes and webcasts from Keysight.


Follow our Next Generation Wireless Communications blog and connect with our industry and solution experts as they share their experiences, opinions and measurement tips on a number of cellular and wireless design and test topics that matter to you.