Fig. 5: A table of dummy frequency data, which is what’s required to run a slope/regression analysis to conclude an optimal frequency range.
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Using a table like the one in figure 5, you can find the point at which serving more ads does not increase or support performance outcomes and use this as your frequency cap, which typically will be set at a higher level in our campaign hierarchy so the cap manages exposures across campaigns. This is the point in the bell curve where the line is closest to the x‑axis after the curve in Figure 2.
Pro tip: Frequency settings can be managed at many levels using different audience seeds (e.g., first-party data groups). Seeds are a fantastic way to engage different audiences with bespoke messaging at varied frequencies to drive incremental performance. You can use this process to help find optimal frequency across campaigns, ad groups, and audiences, with the ultimate goal of driving maximum performance.
Next, analyze where minimum exposures are being made. (This typically happens in inventory-scarce environments.) You can use this as an opportunity to assess when a specific frequency correlates with higher performance. In this phase, CPMs should also be evaluated to ensure you’re being competitive. The end goal of this phase is to determine a target minimum frequency and make sure the campaign can deliver close to that minimum across all supply. For most campaigns, frequency minimums can be set to “lifetime” timelines.
By studying the time between impressions served, you can identify how close impression exposures need to be to drive maximum engagement, ad recall, and bottom-line performance. Delivering impressions too close together or too far apart can have an adverse impact on performance and customer experience. In this advanced step, savvy programmatic practitioners can pull in impression time-stamp data, cross-referencing the frequency buckets shown in Figure 2. This way, you can work toward finding the ideal distribution of impressions within frequency settings to bolster performance. While we won’t dive into details on how to determine optimal impression distance, we have provided an illustrative analysis that can be replicated.
Total N impressions (N>=2) and there are (N‑1) spaces. Then, avg. spacing = T/n or T/(N‑1).
Total N impressions (N>=2) and there are (N‑1) spaces. Then, avg. spacing = T/n or T/(N‑1).
The document offers a detailed overview of Real-Time Spectrum Analyzers (RTSAs), discussing their development, functionality, and various applications. It begins with a historical perspective on RF signal technology and its evolution, leading to the complexities in modern radar and communication networks. The core focus is on the architecture and technical capabilities of RTSAs, particularly their ability to analyze dynamic RF signals effectively. It addresses contemporary challenges in spectrum analysis and outlines the use of RTSAs in different scenarios, making it a useful resource for professionals engaged in RF technology.
Engineers and scientists have been exploring innovative uses for RF technology since the s, following James Clerk Maxwell's mathematical prediction of electromagnetic waves capable of transporting energy across empty space. Heinrich Hertz's physical demonstration of "radio waves" in laid the groundwork for pioneers like Nikola Tesla and Guglielmo Marconi, who developed long-distance communication methods. By the turn of the century, the radio emerged as the first practical application of RF signals.
Throughout the next three decades, research focused on transmitting and receiving signals for object detection and location. By World War II, radar (radio detection and ranging) became a significant application of RF technology. Continued growth in the military and communications sectors spurred rapid technological advancements in RF throughout the 20th century. Modern radar systems and communication networks now employ complex RF techniques, including adaptive modulation and frequency hopping, to improve efficiency and avoid detection. The prevalence of RF signals has grown significantly, leading to challenges in avoiding interference among devices. Cellular phones and other devices operating in licensed spectrum must avoid transmitting RF power into adjacent frequency channels. The development of digital RF technologies, such as wireless LANs, cellular phones, and digital TV, has led to more efficient spectrum allocation methodologies.
Today's engineers and scientists face the challenge of detecting and characterizing RF signals that change over time. Traditional measurement tools are often inadequate for this task. Tektronix's Real-Time Spectrum Analyzer (RSA) is designed to discover elusive effects in RF signals, trigger on those effects, capture them into memory, and analyze them across various domains. This document explains the workings of the RSA and its applications in modern RF signal analysis.
Characterizing the behavior of contemporary RF devices requires understanding how frequency, amplitude, and modulation parameters behave over time. Traditional tools like Swept Spectrum Analyzers (SA) and Vector Signal Analyzers (VSA) offer snapshots of signals, insufficient for analyzing the dynamic nature of modern RF signals. Key challenges include discovering rare, short-duration events, observing signals masked by noise, capturing burst transmissions, and characterizing time-variant modulation schemes. To address these challenges, the Real-Time Spectrum Analyzer (RTSA) was developed, using real-time digital signal processing (DSP) to analyze transient and dynamic RF signals more effectively. The RTSA's architecture allows it to discover events missed by traditional analyzers, trigger on these events, and capture them for in-depth analysis.
To understand the RTSA's capabilities, it is helpful to compare it with traditional RF signal analyzers: the Swept Spectrum Analyzer (SA) and the Vector Signal Analyzer (VSA). The SA, with its swept-tuned, superheterodyne architecture, is well-suited for static signals but may miss transient events. The VSA, capable of vector measurements, can store waveforms in memory but is limited in analyzing transient events. The RSA, with its real-time processing engine, overcomes these limitations. It continuously processes samples, allowing for real-time correction of signal path imperfections, advanced triggering mechanisms, and simultaneous analysis in multiple domains. This architecture enables more effective analysis of modern, dynamic RF signals.
The Real-Time Spectrum Analyzer (RSA) from Tektronix uses an RF downconverter followed by a wideband intermediate frequency (IF) section. Key characteristics of its architecture include:
The RSA's RF/IF block diagram demonstrates its signal conditioning process, which includes variable attenuation, multi-stage frequency conversion, and analog filtering. The last IF is digitized, and subsequent processing is done using DSP techniques. Optional baseband modes in some RSA models allow direct digitization of the input signal.
This section in the RSA handles the distribution of input waveforms to various signal paths within the instrument. It includes features like a DC coupled baseband path for analyzing low-frequency signals and internal alignment sources for self-calibration.
These sections contain broadband circuitry for conditioning input signals for optimal downstream processing. This includes elements like step attenuators, image reject filters, and optional preamplifiers.
The RSA can analyze a broad band of frequencies by converting the band of interest to a fixed IF for digitization and analysis. This involves multi-stage frequency conversion and is integral to the RSA's performance.
The RSA uses a combination of analog and digital signal processing to convert RF signals into calibrated, multi-domain measurements. The DSP path includes bandpass filtering, digitization, and correction for amplitude flatness and phase linearity. Techniques like digital down conversion (DDC) and decimation are employed for efficient signal representation and processing.
Spectrum analysis in the RSA is achieved through repetitive Discrete Fourier Transforms (DFTs), ensuring that signal processing keeps up with the input signal. This enables the discovery and analysis of transient events in the frequency domain.
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The RSA uses Finite Impulse Response (FIR) filters for frequency selection and adjustment of analog signal path imperfections. Numerical convolution is employed to filter the signals mathematically.
The RSA is capable of analyzing various modulation formats, including amplitude, frequency, phase modulation, and complex schemes like QAM and OFDM. This involves reconstructing ideal signals, comparing them with actual signals, and performing detailed modulation analysis.
Real-Time Spectrum Analyzers (RTSAs) like the Tektronix RSA integrate time and frequency domain measurements effectively. They acquire data in the time domain and convert it to the frequency domain using the discrete Fourier transform. This allows for the precise location of events in both domains.
Spectrograms are valuable for observing spectral features as a function of time, displaying complete spectra over a continuously updated timeline. Two common display methods are the waterfall plot and the color-coded representation. The time resolution of the spectrogram is influenced by the sampling rate of the ADC and the length of the DFT used.
For bandwidths exceeding the real-time bandwidth of the spectrum analyzer, multiple real-time spectra can be stitched together. While this process is slower than real-time analysis, it still offers substantial time-saving compared to traditional swept spectrum analyzers. The swept FFT technique can also be applied to DPX analysis, although there are limitations in capturing events during the sweep.
RTSAs utilize a "Time Overview" window for controlling acquisition and analysis timing. This feature is crucial for analyzing signals like frequency hopping signals, where amplitude vs. time information is key. The window can be adjusted to include multiple pulses or focus on specific segments of the signal.
Tektronix RTSAs are equipped to make various time-based measurements on RF signals, including frequency vs. time, amplitude or power vs. time, I and Q vs. time, phase vs. time, and various modulation parameters. These measurements are useful for diagnosing transmitter issues and examining how modulation varies over time.
RTSAs are particularly well-suited for pulse measurements, offering automatic pulse measurement software and extensive analysis options. They are specified for system rise/fall time, minimum pulse duration, and modulation bandwidths. For faster rise times and wider bandwidths, SignalVu-PC software can be used with Tektronix DPO Series oscilloscopes.
In summary, Chapter 3 highlights the capability of RTSAs in effectively correlating time and frequency domain measurements, offering advanced features for comprehensive analysis of RF signals.
Real-time spectrum analyzers (RTSAs) are essential in various application areas. Laboratory benchtop analyzers have evolved over two decades, offering superior real-time bandwidth and RF performance. In contrast, handheld RTSAs offer portability at the cost of some RF performance, suitable for field applications.
Wireless Local Area Networks (WLANs), particularly Wi-Fi, have seen significant advancements. Standards like 802.11g, 802.11n, and 802.11ac have evolved, offering various channel bandwidths, spatial streams, and data rates. Testing WLAN transmission with these standards requires spectrum analyzers with adequate real-time bandwidth and linearity.
Wireless Personal Area Networks (WPANs), such as Bluetooth and Zigbee, are designed for short-range connections. Bluetooth, for instance, operates in the 2.4 GHz ISM band, with various versions supporting different data rates and modulation schemes. Spectrum analyzers used for testing Bluetooth devices must have low noise and high stability.
Cellular radio, particularly LTE, demands rigorous RF transmission testing. LTE operates on various frequencies and offers different spectrum widths and data rates. Testing LTE transmitters involves verifying power levels and ensuring no unwanted emissions outside the used band.
Modern radios, increasingly controlled by software, change modulation, power, and frequency dynamically. Tektronix RTSAs offer features like DPX Spectrum and Frequency Mask Trigger, ideal for debugging and analyzing radio communications.
Digital RF in video broadcast, such as DVB-S and DVB-T formats, requires specific testing for modulation types and data rates. RTSAs like the Tektronix RSA306 can monitor video transmission effectively.
RTSAs are crucial in identifying interference in spectrum management. The capability to detect low-level and intermittent interference is essential, and RTSAs with features like Frequency Mask Trigger and DPX display are highly effective.
RF testing is essential for verifying the performance and reliability of wireless communication systems. It involves measuring and analyzing electromagnetic signals to detect issues like signal integrity and interference. Tektronix offers advanced RF testing tools, such as real-time spectrum analyzers, oscilloscopes, and arbitrary waveform generators, to help engineers ensure their RF components meet industry standards. Effective RF testing minimizes operational failures, enhances system efficiency, and ensures compliance with regulatory requirements.
In mobile communications, challenges like spectral regrowth and power efficiency in amplifiers necessitate advanced testing tools. RTSAs provide critical insights into amplifier designs, offering analysis of modulation quality and adjacent channel power.
RTSAs simplify radar testing by integrating pulse measurements for both time and frequency domain analysis. They replace multiple traditional tools and can reveal interference components and non-linearities in radar signals.
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