New RF Metrics for the Smartphone-centered World

A decade ago, "cell phones" were all about voice communication, an extension of the home phone for those who needed to be on the go. Global roaming was minimal at best, with multiple communications standards worldwide: GSM, TDMA and CDMA, primarily, with many devices carrying backward compatibility to AMPS. To match these applications, RF power amplifier (PA) performance was all about peak power capability. Performance at 3.4 V was the key metric as it drove output power capability and, as a result, a handful of parameters based on modulations characteristics were evaluated along with the key product differentiator, power added efficiency (PAE). Essentially, suppliers and customers alike utilized "static" measurements and kept evaluations very straightforward.

Fast-forward to today's Smartphone-centered world and one will quickly find that these "static" measurements are not nearly as relevant; they are evaluating more of the exception than the rule. The implication is that these simple metrics are driving incorrect tradeoffs in cellular front-end and PA design. Today's Smartphone-centered world deserves Smartphone-centered front-ends. These front-ends operate in a dynamic world and need new, "dynamic," Smartphone-centered metrics to succeed.

Smartphones = Mobile Data

First and foremost, while a Smartphone can simply be described as a new generation of mobile devices, the mobility attribute has expanded dramatically. Rather than simply being an extension of voice communication when someone is away from their home or desk phone, mobile phones are quickly becoming the only phone owned by many individuals. And, with the growing coverage of GSM and W-CDMA networks, mobile now also means global roaming—across multiple countries and multiple mobile operators. Secondarily, while still a tool for voice communications, Smartphones are predominately data-driven devices—whether users are surfing the Internet, downloading the latest video or song, or twittering about their latest favorite restaurant.

The RF implications of these new usage models are quite dramatic. Smartphone users seek the longest operating time between charges, given their heavy data usage during the day, so making the most of a battery's full range of operation is increasingly important. Next, the form factor of these devices has decreased dramatically despite increased semiconductor content. The net result is an extremely compact design that is unable to mask the heat generated by inefficient operation; thus, thermal performance has jumped to the forefront of consideration in new components. The use of DC-DC converters to address these thermal concerns is also on the rise. Third, an understanding of the usage models of the global cellular communication standards indicate a much more dynamic output power model for mobile devices, with peak power being more of the exception than the rule. Combining these factors with global roaming across various network operators' infrastructure deployment strategies creates a need for the RF to be the most efficient for any given power level the network dictates. Last but not least, Smartphones are data devices by design. With the multitude of data modulations driving various linearity and power requirements, dynamic assessments of performance are mandatory to fully evaluate an RF front-end's or a PA's fit for the application.

Figure 1 Li Ion battery discharge curve.

It Is All about Battery Current Draw These Days

In the past, efficiency was used as the primary metric for a PA's performance in a cellular application, particularly at peak power and a single voltage, traditionally 3.4 V. In reality, however, a mobile phone's battery does not stay at a constant voltage; in fact, as illustrated in Figure 1, a lithium-ion battery's voltage changes based on the percent charge. The battery voltage typically ranges from around 4.2 to 3 V, depending on the amount of charge available. When one examines the battery discharge curve in detail, data reveals that for over 80 percent of the operating time the battery voltage is above 3.4 V—the first evidence that the traditional measurement point is the exception not the rule.

Looking back to the use of efficiency as an indicator of performance, efficiency has been the staple for comparing RF front-ends, although they require a subjective choice of a single voltage point. This metric is convenient to measure and evaluate, but does not represent real-world usage models. For example, the calculation for efficiency can be used to demonstrate the delta in performance between traditional GSM PA solutions that do not incorporate DC-DC converters because these PAs utilize a fixed collector voltage for operation.

As a result, at a battery voltage of 3.4 V while delivering 31 dBm of power at the PA output, the efficiency is ~35 percent, which is typical for solutions today. By changing the battery voltage to 3.8 V the calculation for efficiency shows a degradation of over 4 percent, which is a result of the energy being dissipated thermally. While this shows a reasonable indication of the potential thermal implications of the solution, efficiency's dependence on battery voltage does not give a good indication of the solution's effect on battery life and the resulting end user's talk-time. Specifying RF front-ends in terms of current consumption (mA) is the only true way to compare solutions as to their impact on talk time since batteries are specified by their capacity in mA/hours. Therefore, a better comparison, which more closely resembles real-world usage models, is to calculate average current consumption across the discharge curve. Additionally, this will take into account the possibility of a solution that utilizes a DC-DC converter to adjust PA collector voltage with output power—a common theme in today's Smartphone RF solutions—and provide a way to better compare the benefits of the two types of implementations.

In terms of measuring current consumption, a PA designer could, in a straightforward fashion, measure current over the operating voltage range. In a traditional GSM PA not connected to a DC-DC converter, we find, intuitively, that current consumption remains constant over the battery voltage range (see Figure 2).

However, in a solution that utilizes a DC-DC converter to supply the PA collector voltage, we find that current consumption varies with battery voltage. Thus, since the battery voltage varies with charge level, we can measure the current consumption relative to the battery charge level (see Table 1).

Next we graph the current consumption over the charge level (see Figure 3.). By taking the polynomial and integrating the curve, it gives us Equation 2

Computing the equations reveals that this solution will give the designer an average current of Ibattavg=0.665A.

Comparing the two GSM PA solutions delivers a calculated, non-subjective measurement of relative performance, along with metrics that are directly and easily correlated to battery life and talk-time. In this case, the solution that utilizes a DC-DC converter can more efficiently utilize current for the given, backed-off power level (ECTEL power of 29 dBm), resulting in ~400 mA less average current consumption. A Smartphone RF architecture that values GSM current consumption at backed-off power levels would quickly see a significant advantage in implementing a DC-DC converter.

Conversely, if we stayed with a traditional measurement of peak power at 3.4 V, the solution with a DC-DC converter would show much worse current consumption. However, again, this single measurement point does not adequately depict the situation. As shown in Figure 4 (similar to the backed-off power example), when one considers the average current over the battery voltage and weight that given the percentage of time a battery is at the voltages, one can intuitively see that, given normal mobile phone operation, the better performing solution is one that utilizes a DC-DC converter.

Thermal Concerns Are on the Rise

Taking the data from the above example, similar methodologies are used to evaluate the impact of varying current consumption on thermal performance (see Figure 5). Based on the conservation of power we quickly see that over the majority of the battery discharge curve of a non-DC-DC converter-based solution will dissipate dramatically higher thermal energy. Approximately 1.5 to 2 W of heat being dissipated in the Smartphone is easily noticed by the end user depending on the form factor and thermal properties of the Smartphone. Since W-CDMA is full duplex (meaning the system transmits and receives simultaneously), the PA is powered on and transmitting for the full duration of the data call. GSM, on the other hand, is time-based, resulting in 1/8 duty cycle of the PA up to 4/8 for GPRS multi-slot operation. In a contrasting case, if the W-CDMA PA consumes 400 mA of current, this is a constant requirement from the battery. In a GSM system, if the PA consumes 1.5 A of current and the duty cycle effect is applied, this would result in less than 200 mA of average current. Examining these two types of systems, it is easy to understand why DC-DC converters have been broadly adopted in full duplex systems—to help minimize this thermal dissipation.

With these examples, it is clear that measuring the thermal impact of a PA solution—no matter the communications standard—is best done by evaluating an average thermal dissipation over intended use range of the battery discharge curve.

Moving People + Fixed Infrastructure = Varying Output Power

While this concept of output power varying as a mobile phone moves with its user seems intuitive, still it remains that for the last decade the dominant majority of metrics have been related to peak power levels. The CDG curve developed for CDMA-based mobile operators was the first attempt to more adequately depict what is really happening with a mobile phone's output power during typical operation. GSM Association quickly came up with a similar metric, as the expansion of W-CDMA modulation showed similar attributes, and the DG.09 curve, which estimates the probability density of varying output powers for W-CDMA voice modulation, was created (see Figure 6).

Despite the fact that both the CDG and DG.09 curves have been around for a while, there remains a large focus on single power level metrics, which can quickly drive the wrong decision points. For example, let us compare two completely different W-CDMA PA control architectures: a single-ended W-CDMA PA that has three distinct power modes (PA "A"); and a quadrature W-CDMA PA that utilizes continuous analog bias control and collector voltage adjustment coming from a buck DC-DC converter (PA "B"). Often these two types of solutions would be measured at 0 and 24 dBm (peak) output power to see which has the best performance. Table 2 shows that the difference is performance, and, at quick glance, one can see the detriment of using a "static" metric. If 0 dBm or peak current consumption was used, PA "A" might be chosen. If the metric for decision making was DG.09, which is a better representation of W-CDMA voice performance, the decision maker chooses PA "B," while also gaining the benefits of a quadrature PA solution—VSWR tolerance and broadband capability.

Smartphones = Data

Thus far we have centered the discussion of metrics on voice-centric performance. Smartphones, however, spend the majority of their time in a data mode. With each new Smartphone rollout we are seeing an increase in the number of data modes—basic W-CDMA data, then HSPA and now HSPA+. Each mode carries different performance requirements and demands on the PA solutions. In short, as the data rate goes up, the implication on power output is on the rise in the center point of the output power probability density function (PDF). For example, although little to no formal data has been published, in speaking with many customers and cellular platform providers there is general consensus that, on average, where DG.09 is "centered" around 0 dBm, higher order data modulations are centered between 10 to 15 dB higher based on the needs to ensure sufficient data throughput. This requirement is logical as the higher order modulation formats place more symbols in the constellation. As more symbols are packed in the constellation there is less error in phase and amplitude between the symbols resulting in higher bit error rates (BER). This requires the transmit (TX) system to increase power, resulting in higher signal to noise ratio (SNR) at the receiver, thus limiting the BER and error correction. This results in faster data rates. To begin measuring average current consumption with these data rate implications in mind, a 3G data PDF is shown in Figure 7 and Table 3 to measure the dynamic performance of a W-CDMA PA.

Looking to the future and 4G LTE adoption, it would be prudent not to make the same mistake again by assuming "static" measurement points as history tells us that this is a highly unlikely scenario. As such, we have evaluated the systems implications of LTE's QPSK modulation schemes and see a shift, yet again, for real-world implementation in mobile devices. Table 4 outlines the evaluation points for this higher order data modulation scheme and Figure 8 serves as a composite of the three different PDFs presented.

A quick analysis of Figure 8 shows the current and upcoming complexity of Smartphone RF, and hopefully it highlights the need to abandon "static" measurements of performance. Even if there is disagreement as to the exact set points for the creation of PDF curves, we believe that utilizing a set of PDFs, however defined, is far better than utilizing a few discrete points to make key architectural and component selection decisions.

No More Excuses for Doing it the Right Way

Batteries do not stay at the same voltage during the discharge cycle, thus a dynamic environment exists in the mobile phone. By definition, mobile phone users are mobile, providing another, simultaneously dynamic environmental variable. Although we have spent so many years using static measurement points to determine the "goodness" of a front-end solution, one quickly sees that these assumptions are not in line with how the real cellular-based mobile world works.

There might be some who say, "But the measurement equipment and time to evaluate such dynamic profiles did not exist." Perhaps not at that time, but they certainly do now. At RFMD®, there are component characterization systems that can evaluate a multi-mode, multi-band environment over all temperature, load and power level conditions in a matter of days; the same amount of information collection would have taken a matter of months previously. The technology is available to allow us to be more exacting in our analysis.

Perhaps most important is that we are at a huge inflection point in our industry. For an industry that has been driven largely by voice, under a minimum number of cellular modulation schemes, the world is changing fast. The growth of Smartphone volume is of the kind of segment growth we have not seen in a decade. And with these new devices comes an extremely dynamic, complex and demanding operating environment. Static, single voltage, single power level measurements to determine the goodness of a solution are archaic at best. Neglect is perhaps a more appropriate term to use considering the millions of dollars of research and development extended each year to develop these RF components.

Should we challenge our industry and RF component suppliers to move quickly to these more dynamic metrics? It seems the prudent choice. The first step is a straightforward one—change the datasheets and show the performance under these new metrics. Only then will original equipment manufacturers (OEM) see where improvements can be made, which will enhance the operational quality of the handsets and mobile devices they create. We invite our fellow suppliers to join us as we send the message—Welcome to the next decade of RF.

Ben Thomas is the director of marketing for 3G/4G Cellular Front Ends at RFMD.

Jackie Johnson is the manager of Cellular Front End Applications Engineering at RFMD.