RF Signal Analysis Techniques

Radio frequency (RF) transmission is electromagnetic radiation that is used to transmit data wirelessly through a range of frequencies. RF allows data to be transferred by measuring modulation of the radio spectrum in frequencies ranging from a few Hz to the lower limit of infrared: 3Hz (ELF) to 300GHz (EHF). From the first experimental radio transmissions in the late 1800s to a society that is now dependent on wireless communication systems: RF technology has come a long way.

Understanding RF fundamentals is crucial to operate and maintain modern wireless systems. If you plan to manage the spectrum and all the technologies exploiting the EMS, it’s important to have measurement tools that are best in class in measuring all the key parameters of RF signals: specifically, measurements in the power, frequency, and time domains.

Electromagnetic Spectrum

Key Concepts in RF Signal Analysis

Several terms are commonly used in radio frequency analysis:

Angle of Arrival (AOA): Used to determine the direction from which a signal is transmitted.
Antenna: A device that transmits or receives radio waves, converting electric current into radio waves and vice versa.
Array: Two or more antennas arranged to create a directional receiving pattern or to measure the phase difference between the antennas.
Center frequency: The specific frequency at which a radio signal is transmitted or received.
Decibels (dB): A unit of measurement used to express the ratio between two values, typically power or amplitude levels.
Demodulation: The process of extracting information from a modulated RF carrier signal.
Dynamic range: The difference between the weakest and strongest signals that a system can detect and monitor.
Hertz (Hz): The unit of frequency for cycles per second.
Instantaneous Bandwidth (IBW): A measure of the range of frequencies that can be transmitted through a communication channel.
Interference: Unwanted signals or noise in the RF spectrum that can disrupt or degrade wireless communication.
In-phase quadrature data (I/Q data): The in-phase and quadrature components of a signal used in many types of digital communication.
Noise floor: The background noise or interference level in the RF spectrum, typically measured in dBm.
Noise figure (NF): The measure of the degradation of a signal-to-noise ratio (SNR) caused by the receiver.
Phase Noise: The random fluctuations in the phase of an RF signal.
Power of Arrival (POA): The strength of the RF signal as it arrives at a receiving antenna.
Preselection: The process of selecting a specific frequency range of interest from a larger range of frequencies.
Spurious signal: An unwanted signal generated outside the frequency band of interest.
Spurious Free Dynamic Range (SFDR): A measure of a communication system or device’s ability to reject unwanted or spurious signals.
Time Difference of Arrival (TDOA): Used to determine the location of a transmitter based on the difference in arrival time of a signal at multiple receivers.

Importance of Sensitivity and Dynamic Range

Key parameters that RF experts always look for first when evaluating an RF receiver are sensitivity and dynamic range. Sensitivity is a measure of the minimum power level of an incoming signal required for the receiver to detect and demodulate it with a specified level of performance. Dynamic range is a measure of a system or component’s ability to handle and distinguish between high and low power levels of signals simultaneously. The sensitivity and dynamic range performance of a particular receiver will be defined by its noise figure (NF).

A low NF is important for several reasons: First, it allows the receiver to detect weak signals in a noisy environment. However, achieving a low noise figure is difficult, especially in high-frequency applications. Thermal noise, caused by the random motion of electrons in the receiver components, is one of the main sources of noise in RF receivers.

The ability to accurately measure a wide range of signal strengths is essential to detect and characterize all the different signal types exploiting the EMS. When hunting for noise or interference, a wide dynamic range is essential. The dynamic range of the RF receiver will then define its coverage range for different types of technology exploiting the EMS. Of course, a wider dynamic range means a larger coverage range.

Achieving a high dynamic range requires careful design and calibration of the RF receiver system, from the antenna, through the receiver amplifier, and signal processing components. It requires sophisticated signal processing techniques, such as automatic gain control and filtering, to ensure that signals within the dynamic range are accurately measured and characterized.

RF Signal Challenges and Mitigation

RF signals can be affected by interference from other sources, such as other wireless devices or environmental factors like buildings and terrain. RF signals can vary in strength depending on the distance between the transmitter and receiver and other factors such as obstacles and interference. RF signals can be affected by noise, which can interfere with the accurate detection and translation of the signal. Given that RF signals vary in both amplitude and phase, it is not simple to apply a linear model to express them in digital format for storage and analysis purposes.

The most commonly used method uses a mathematical expression producing data in what is called the I/Q format. The noise floor is the minimum signal level that can be detected by a receiver. Reducing it can improve the sensitivity and dynamic range of the receiver, allowing it to detect weaker signals and operate in more challenging environments.

Spectrum Analyzers: Tools for RF Analysis

Spectrum analyzers are frequency-domain instruments, showing power versus frequency. Most spectrum analyzers automate certain power versus frequency type measurements, like AM modulation depth or third order intercept. Other measurements like occupied bandwidth or adjacent channel leakage ratio, would be difficult or impossible to manually measure.

Essential Parameters for Spectrum Analyzer Operation

There are four essential parameters needed to operate a spectrum analyzer.

Center and Span Frequencies: Center is the frequency in the middle of the display, and span is the width of the display.
Reference Level: The top edge of display and represents the maximum expected power at the spectrum analyzer input.
Input Attenuation: A variable input attenuator is placed between the RF input and sensitive components to prevent compression and distortion.
Resolution Bandwidth: Resolution bandwidth affects the ability to separate or resolve closely spaced signals.

The main factor determining the sweep time of a spectrum analyzer is the resolution bandwidth. Most analyzers automatically compute sweep time based on resolution bandwidth and span. There is a trade-off between speed and selectivity / noise.

Video Bandwidth

To understand video bandwidth, the term video signal must be explained. Traces are essentially an envelope of the power at individual frequencies, and this envelope is called the video signal. Lowering video bandwidth only reduces noise on the trace, it does not drop the noise floor like resolution bandwidth does. Video bandwidth only changes what the trace looks like, so to a certain extent the correct video bandwidth setting depends on the application. Most modern spectrum analyzers will automatically configure, and update video bandwidth based on other parameters like resolution bandwidth.

Real-Time Spectrum Analysis (RTSA)

A conventional swept-tuned RF spectrum analyzer only provides a snapshot of signal amplitude at a given point in time. This method works well for stable or consistent signals, but is less useful for diagnosing unpredictable, intermittent, or transient signal and noise components. To overcome these limitations, RTSA technology uses digital processing power to increase the sampling rate and continuously store the results, providing a more complete analysis.

Advanced RF Analysis Techniques

As the landscape transforms, the familiar single-function RF spectrum analyzer no longer suffices. Purpose-built RF analysis tools are giving way to versatile, portable test platforms with the capabilities needed to unlock the complexity of 5G and emerging wireless technologies.

Persistence Spectrum Analysis: Utilizes advanced algorithms to precisely measure the presence of a given frequency within the signal over time.
Signal Analysis: Provides detailed information on the amplitude and phase relationships between signals and their modulation characteristics.
Interference Analysis: Converts spectrum data into meaningful information on the source and type of unwanted signals impacting cellular networks.
RF over CPRI (RFoCPRI): Provides a useful method of RF measurement through the fiber fronthaul.
Beam Analysis: A useful handheld RF spectrum analyzer feature developed for over-the-air (OTA) performance testing in 5G beamforming applications.

How to Use a Spectrum Analyzer for RF Signal Analysis

5G Spectrum Analysis

Radio frequency interference is a common 5G RAN issue leading to performance problems including reduced data throughput and compromised voice quality. The interference source can often be transient in time as well as frequency. RTSA is an effective tool for 5G spectrum analysis, along with a persistent spectrum display to present thousands of data points simultaneously.

5G carrier scanning measures up to eight carriers’ power simultaneously while 5G beam analysis characterizes individual beam ID, power level, and SNR.

Applications of RF Spectrum Analysis

RF spectrum analyzers are used in various applications, including:

Cell Tower Operations: Verifying performance KPIs at turn-up.
RF Shielding: Locating and eliminating leakage sources in cable networks.
EMC Compliance: Verifying electronic equipment emissions and conformance to electromagnetic compatibility (EMC) standards.

Software Defined Radio (SDR)

With availability of modern Software Defined Radio (SDR) hardware, it has become more accessible, cheaper, and easier than ever to examine the Radio Frequency (RF) signals that are used by these devices to communicate. This means we can analyse the raw signal being received, however it is up to us to implement the other components in software in order to retrieve the original data.

Because SDR converts the analogue signal received into digital data, something to note here is the concept of samplerate, and bandwidth. Samplerate refers to the number of samples taken of the analogue signal per second, and directly relates to the bandwidth of the frequency spectrum that is visible at any particular point in time.

AI-Driven RF Signal Analysis

Anritsu and Deepsig deliver a solution that integrates the capabilities of the Anritsu MS2090A Field Master Pro Spectrum Analyser with DeepSig’s wireless signal detection and classification software, which is based on its patented Artificial Intelligence (AI) deep learning algorithms. Importantly, RF signals of interest from diverse new sources like drones and IOT devices can be learned quickly and accurately in days rather than months. This solution is based on Artificial Intelligence (AI). It provides a new class of RF sensing built on Anritsu Spectrum Analyzer and DeepSig software.

Table: Comparison of RF Analysis Techniques

Technique	Description	Advantages	Disadvantages	Applications
Swept-Tuned Spectrum Analysis	Provides a snapshot of signal amplitude at a given point in time.	Simple and widely used.	Limited for transient signals.	Basic spectrum measurements.
Real-Time Spectrum Analysis (RTSA)	Continuously stores results, providing a more complete analysis.	Effective for diagnosing intermittent signals.	More complex and resource-intensive.	5G spectrum analysis, interference hunting.
Persistence Spectrum Analysis	Measures the presence of a frequency within the signal over time.	Detects hidden signals.	Requires advanced algorithms.	Identifying transient interference.
AI-Driven Analysis	Uses AI deep learning algorithms for signal detection and classification.	Quickly learns and accurately classifies new signals.	Requires AI expertise and resources.	Spectrum management, identifying unknown signals.