Wireless technology has become much a part of everyday business and personal lives, with networks, gaming, entertainment, and communications relying on wireless devices. Wireless users want to eliminate wires and achieve communications with mobility. Digital RF technology is making possible a growing number of affordable wireless products for consumer, commercial, and industrial applications that combine wireless communications capability with memory and processing power. This additional "intelligence" supports the much needed spectrum sharing of these devices. But it also presents new challenges for testing and characterizing these products and the devices within them. As a result, a new form of measurement tool—the real-time spectrum analyzer (RTSA)—has emerged as a means of viewing complex signals not only as a function of frequency but as a function of time.

Digital RF technology commonly makes use of complex modulation formats, and often time-varying RF signals. The time-varying signals may have changes related to the type of modulation used, such as a frequency-hopped or burst transmission. The changes may occur at precise points in time. With such instantaneously changing signals, side effects are also produced, including random transients, interference, and switching anomalies. These signals have one thing in common: they exhibit frequency-domain changes that occur in real time, sometimes during durations of microseconds or less. As a result, signals must be evaluated as a function of time as well as frequency.

Traditionally, swept spectrum analyzers were used to view the spectrum and characterize signals in the frequency domain. Such a spectrum analyzer contains a number of different width resolution-bandwidth filters, which could be swept across a bandwidth of interest. The power passing through the filter as it is swept is detected and used to present a spectrum of the signal. The fact that the resolution-bandwidth filter is swept across the frequency band of interest means these types of instruments are inherently limited when analyzing dynamic time-varying signals.

More than a decade ago, vector signal analyzers (VSAs) were developed to characterize modulated signals, by capturing the in-phase (I) and quadrature (Q) components of digitally modulated signals over a relatively wide analysis bandwidth. A VSA digitizes all of the signal information within its capture bandwidth in order to measure the amplitude and phase information of the digitally modulated signal. Unfortunately, many VSAs are limited in dynamic range, have only limited triggering capabilities, and lack the ability to correlate signal information across multiple measurement domains required to fully characterize time-varying signals.

The RTSA was developed to analyze these time-varying signals. Unlike traditional swept spectrum analyzers, advanced RTSAs utilize robust real time bandwidth and high-speed digital signal processing to fully analyze the signal of interest. As these signals become more complex and less predictable, only RTSAs offer the triggering, capture, and analysis features to help designers understand time-varying signal behavior ranging from frequency-hopping signals to electromagnetic-interference (EMI) transients.

In addition to capturing signal events as a function of time, the RTSA adds unique real-time triggering capability that allows the user to capture RF events in both the frequency and time domains. These triggers provide the ability to capture a seamless time record of RF signals into memory for in-depth analysis. Some offer a power trigger, which enables capture whenever the total power of all signals in an analysis span crosses user-defined threshold. Other analyzers offer a frequency-mask trigger, which produces a signal capture when a discrete change in signal frequency, amplitude, or bandwidth occurs, or when a signal appears or disappears at a given frequency.

An RTSA works with DSP and by processing samples, frames, and blocks. A sample is a discrete time-domain data point. The sampling rate of the analyzer and the selected measurement span determines the time interval between adjacent samples. In an RTSA, each sample is stored in memory as an I/Q pair containing magnitude and phase information. A frame consists of an integer number of contiguous samples. It is the basic unit to which a Fast Fourier Transform (FFT) can be applied to convert time-domain data into the frequency domain. In this process, each frame yields one frequency-domain spectrum. A block is made of many adjacent frames that are captured seamlessly in time. The block length (or acquisition length) is the total amount of time that is represented by one continuous acquisition. Within a block, the input signal is represented with no gaps in time.

In an RTSA's real-time measurement mode, each block is seamlessly acquired and stored into memory. DSP techniques are used to post-processing the blocks for analysis of the signal's frequency, time, and modulation characteristics. Once a block is stored in memory, any real-time measurements can be applied. The (integer) number of frames acquired within a block can be determined by dividing the acquisition length by the frame length. The maximum acquisition length ranges from seconds to days and depends on the selected measurement span and the instrument's amount of waveform memory.

Since problematic signals may only appear once every hour or even once per day, advanced RTSAs provide a continuous trigger mode that constantly monitors the spectrum of interest but only makes an acquisition when user defined triggering criteria are met. Once triggered, the analyzer captures and time stamps the spectrum activity to memory and rearms itself to trigger again if the event reappears. This makes efficient use of capture memory by ensuring it is filled with only relevant information, and allows users to leave the instrument unattended to acquire transient events over time.

The ability to easily trigger on dynamic-and transient signals based on specific events in the time and frequency domains allows engineers using an RTSA to reliably identify and acquire single shot events or complex sequences of events and record (capture) them into the analyzer's memory.

By acquiring a seamless record of real-time signal behavior, these analyzers support numerous powerful analysis tools. RTSAs offer a spectrogram display that plots frequency and power versus time. The frequency, time and modulation domains are all visible in time correlated displays, while the spectrogram itself summarizes the long-term view, enabling an intuitive, three-dimensional look at the time-varying signal behavior, otherwise not seen in traditional frequency-domain displays.

Consider the real-time analysis example shown in Fig. 1. This is a screenshot of an RTSA being used to analyze a situation where a wireless-local-area-network (WLAN) link is exhibiting poor performance. With an RTSA, the user is able to control the analysis point by moving a cursor through the captured time record. The spectrogram provides an overall view with frequency represented on the horizontal axis and time on the vertical axis. Signal power is indicated using color, with brighter colors representing higher power.

In the screen shot on the left, one can see the desired WLAN signal, along with a frequency-hopping Bluetooth signal. The screen shot also shows leakage energy from a nearby microwave oven. With the cursor placed at this point in time, the WLAN link is working properly. The spectrum of the WLAN signal is shown along with a constellation diagram showing proper signal synchronization and good EVM performance.

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In the screen shot on the right, the cursor has been moved slightly forward in time. At this point in time, the Bluetooth signal has hopped right on top of the WLAN signal. This can be seen as a signal peak in the spectrum of the WLAN signal. This Bluetooth signal interferes with the desired WLAN signal such that the WLAN signal can no longer be decoded. The resultant constellation diagram shows the loss of synchronization. The end-user then experiences a loss of WLAN signal and perhaps a loss of the Bluetooth signal as well.

As this example illustrates, a simple view of the frequency spectrum that represents an average over time is no longer sufficient to characterize today's RF signal environment. The time dimension is needed to fully understand what is going on in the signal environment. Increasingly, cordless phones, wireless game controllers and even heating-ventilation air-conditioning (HVAC) control systems will all compete for frequency spectrum with other wireless device. With the move toward wireless everywhere, this scenario will become much more complex in the future.

RTSAs have become the preeminent instrument for testing, measuring and troubleshooting wide bandwidth, dynamic RF signals. With time-correlated views across the frequency, time and modulation domains and a full range of triggering and analysis capabilities, engineers can gain unprecedented insight into RF signal behavior for complete characterization and quick problem solving (Fig. 2).

RTSAs enable comprehensive examination of the time-variant behavior of today's complex RF signals and are invaluable for RF spectral monitoring, interference hunting, signal characterization, and EMI diagnostics. They ease RF device characterization, troubleshooting, and debugging, helping ensure potential system instabilities are eliminated from designs before they reach a broad market and further disrupt an already crowded and chaotic RF spectrum. With the changes occurring in the wireless world today, the time is right for real-time spectrum analyzers.

FOR FURTHER READING Fundamentals of Real-Time Spectrum Analysis (a Primer), Tektronix, Inc., 2004, Internet: www.tektronix.com.