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A couple of months ago, I was driving in New York City with two of my favorite friends. The first was a real person, and the second was a trusty GPS navigation system. Unfortunately, it was on that day that my trust in GPS faltered slightly. If you have been to New York City, you know that Manhattan and Brooklyn are on different sides of the East River, connected by the famous Brooklyn Bridge. As an RF engineer, you might also know that the city is an intensely urban environment that poses significant challenges to wireless communication systems. On that particular afternoon, the GPS unit had such difficulty tracking satellites that it mistakenly identified our location as Brooklyn when we were actually in Manhattan, more than a half-mile away. For the first time, my friend the GPS navigation system was lost.

As my New York City story illustrates, the urban environment introduces significant impairments into wireless communications channels. With many tall buildings and wireless transmitters, urban settings introduce significant multipath propagation and interference. Moreover, these characteristics are dynamic factors that vary according to time of day, temperature, and even weather conditions. As a result, today's engineers face a difficult task when validating that each new receiver design will operate effectively in a wide range of wireless environments.

While RF engineers can model many of the impairments common in a wireless communications channel through channel emulation, modeling alone is not sufficient. Typically, receiver validation involves a series of field tests in the deployment environment. Unfortunately, testing a device in many types of physical environments can be costly. Moreover, it can produce inconsistent results because of the difficulty of re-creating particular environmental conditions. This is problematic during the design validation phase, where it is important to maintain consistency between each revision of a receiver design.

As a result, engineers are increasingly turning to RF record and playback systems as a tool for design validation. With these systems, a portion of the RF spectrum can be recorded for up to several hours and stored as a baseband waveform. It can then be re-played in the laboratory with a vector signal generator. RF record and playback systems provide many advantages over the traditional approach by allowing engineers to test receivers with more realistic impairments.

In order to understand why receiver validation in the deployment environment is crucial, we will first examine the effects of impairments on a wireless communications channel. In general, engineers evaluate receiver quality according to two measurements: bit error rate (BER) and error vector magnitude (EVM). BER is probably the most universal measurement of receiver quality as it describes the probability of incorrectly receiving a bit. Because measuring an extremely small BER can be time consuming, EVM can also be used to predict BER. This is illustrated in Figure 1.

In Figure 1 we see that the probability of a bit error decreases dramatically as the EVM decreases below a particular threshold. For illustration purposes, we have assumed that error is due purely to white Gaussian noise. In this scenario, we can approximate the BER from the following equation for error rate in an uncoded coherent QPSK channel:

*erfc(x) is the complementary error function, a metric of estimating probability.

Because EVM can be used to calculate BER, we will use EVM as a metric of comparison as we evaluate various types of channel impairments. The specific impairments we will describe includenoise, adjacent-channel interference and multipath distortion. In the following sections, all impairments are created with the NI modulation toolkit, an NI LabVIEW add-on used to create baseband waveforms.

In an ideal world, a transmit signal's power would be significantly greater than the noise floor of the receiver. However, signal strength decreases with the distance from the transmitter; and this is not always the case. Signal-to-noise ratio (SNR) is often used to estimate the quality of a communications signal.

To illustrate this affect, we can introduce additive white Gaussian noise (AWGN) to a modulated baseband waveform to reduce the SNR. Noise can be visualized on a constellation plot and results in the spreading of each symbol.

As Figure 2 illustrates, reducing the SNR adds increasing levels of uncertainty to the demodulated signal. At a specific threshold (30 dB for our simulated receiver), the data becomes almost completely unreliable. In fact, the spinning constellation plot in Figure 2 indicates loss of carrier lock. At this point, the EVM dramatically increases.

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