Interference Hunting

IQ Data Extremely Valuable in Ensuring Signal Analysis in Emerging Wireless Networks

December 7, 2021

As more complex wireless signals are introduced to the ever-crowded RF spectrum due to the rollout of 5G and expanded IoT use cases, the ability to effectively capture and analyze those signals is in greater demand. Interference — unintentional and nefarious — threatens the fidelity of communications systems in high frequency and millimeter-wave (mmWave) frequencies.

For these reasons, analysis of IQ data has become an important tool in the signal analysis toolbox used by field engineers and technicians. Using standard software applications, every detail of an RF signal can be analyzed and categorized. Leveraging IQ data capture and streaming capabilities allows network management to ensure key performance indicators (KPIs) are met.

IQ data is gathered using a signal analyzer, such as the Field Master™ Pro MS2090A (figure 1), that processes RF signals. To effectively use the acquired signals for additional insight requires that the test solution has three critical specifications to gather spectrum intelligence:

• Frequency Coverage: Full coverage up to 54 GHz ensures there are no gaps where signals can be missed.
• Bandwidth: 110 MHz of capture bandwidth means more information across more spectrum.
• Real-Time Performance: A minimum detectable signal of 5 ns means even the shortest duration signals cannot hide from detection.

Figure 1: The Field Master Pro is a key tool for gathering IQ data.

IQ Streaming Explained

A signal analyzer captures portions of RF energy hundreds of thousands of times a second then processes that data into the frequency domain using a fast Fourier transform (FFT). The analyzer can translate the information into the graphic displays of spectrum that are typically associated with the instruments.

IQ streaming is the ability of the analyzer to take that data before it is processed, break it down into the I and Q parts, and store it to an external device for later processing. This enables greater insights into the signal, such as checking modulation quality, slowing the playback of the signal to see changes over fractions of time — down to nanoseconds of time resolution — or analyzing other signal characteristics that can help identify and track the signal.

Data Stream Options

Several options for configuring the data stream can be used, depending on application:

Capture Bandwidth and Sample Rate – The capture bandwidth and sample rate are tied together to determine:

• frequency span of the capture from the center frequency
• rate at which the analyzer samples the IQ data (see figure 2)

Figure 2: Capture bandwidth and sample rate selection

The Field Master Pro offers up to 110 MHz of capture bandwidth at 200 MS/s (option dependent), but more bandwidth is not always desirable. Additional bandwidth and faster sample rates mean more data must be stored into memory and analyzed in post-processing. Streams of 110 MHz fill up storage much faster than a 1 MHz capture bandwidth. So, the key is to select as narrow a bandwidth as possible for a particular application.

A regulator conducting spectrum monitoring may want to see spectrum activity on as wide a span as possible. Compare this to a radar transmitter that may only care about kilohertz worth of spectrum where their signal resides. Setting the right bandwidth will make it much easier to process at a later time.

Timestamp – Users can choose whether to capture a timestamp with each IQ sample. A timestamp enables precise correlation between the time and any spectral phenomena, which is beneficial. There is a tradeoff, though. A timestamp takes up bits within the capture. So, the user must give up some resolution in the capture, resulting in a rise in the noise floor of the playback, or capture a greater bit depth. While the latter option may help offset the loss in resolution of the signal, capturing more bits equates to more data and more memory used.

Sample Format (Bit Depth) – Given unlimited bit depth and sample rate, an IQ capture can perfectly recreate an RF signal from any period in time. More bit depth and additional samples come at a cost of more bits that can quickly consume gigabytes of storage and tens of gigabits per second of streaming bandwidth.

Every capture contains noise. At some point, more bits only serve to better quantify the noise and no longer provide additional insight into the signal. More details on selecting proper bit depth for a given application are provided in Anritsu’s Choosing the Best Bit Depth for IQ Captures or Streams white paper.

Selecting a Storage Location for the Stream

There are many possible applications that require IQ data analysis, different use cases will have different requirements. Some cases might call for large data amounts to be stored and analyzed at a time. For other applications, portability may be the most important factor. Choosing the right device will depend greatly on the application.

One important consideration is the possible trade-off between capture bandwidth and gaps in the IQ data. A good analogy is to think of the data like water flowing through pipes. Different storage devices have different size pipes, therefore, support the transfer of different data amounts. When more data is being acquired that can be transferred, gaps will occur in the final IQ file. Streaming the data without gaps requires fast enough transfer speeds, so the data is cleared from the spectrum analyzer RAM and into the target storage device.

IQ data can be streamed to various conventional storage devices. Examples include USB 3.0, PC via PCIe, PC via Ethernet, or X-COM’s IQC5000B RF Record and Playback System. Some options are more portable but are limited in the amount of data that can be streamed. Others offer very wide bandwidth streaming but require extra equipment and/or cost. Table 1 shows each option with special considerations and is followed by more detailed explanations in the next several sections.

Table 1: IQ data storage device options.

You can learn more about the importance of IQ streaming by downloading the IQ Streaming with Anritsu’s Field Master™ Pro Real-Time Spectrum Analyzer application note.

Greater Insight into Pulsed Radar Measurements with Real Time Spectrum Analyzer

June 23, 2021

Radar is used in a wide range of military, commercial, and research environments. Many radars are expected to operate uninterrupted for decades. To ensure that their performance does not degrade over time, it is necessary to measure their key performance metrics at regular intervals.

One prime performance area monitored is the characteristics of the transmitted RF pulses. The best test tool to measure this signal is a spectrum analyzer with real time capability (RTSA), such as the Field Master™ Pro MS2090A. When combined with a pulse analysis option, this combination provides the accuracy and insight necessary to ensure performance by capturing the pulsed signals in both frequency and time domain.

An important element of the pulsed measurements is that they should be traceable and compliant with IEEE Std 181-2011. This standard specifies precisely what parameters need to be measured and how those measurements should be calculated.

Two prominent radar systems that are safety critical and require routine maintenance are airport surveillance systems and weather radar. Each has particular elements but testing their performance requires a similar approach.

Airport Surveillance Systems

The United States Federal Aviation Administration (FAA) rolled out Airport Surveillance Radar (ASR-11) for airport surveillance. Currently, there are more than 400 ASR-11 radars (figure 1) deployed across the U.S. ASR-11 is an integrated primary and secondary radar system that has been deployed at terminal air traffic control sites.

Figure 1: Example of FAA ASR-11 radar covering San Francisco Bay area.

Primary Surveillance Radar – The primary radar uses a continually rotating antenna mounted on a tower to transmit RF pulses that reflect, or backscatter, from the surface of aircraft up to 60 nautical miles from the radar. The system measures the time required for radar to echo to return and the direction of the signal to determine the distance of the aircraft from the radar antenna and the azimuth of the aircraft in relation to the antenna.

Primary radar operates in the range of 2700 MHz to 2900 MHz. The transmitter generates a peak effective power of 25 kW and an average power of 2.1 kW.

Secondary Surveillance Radar – A second radar beacon antenna is attached to the top of the primary radar antenna to transmit and receive area aircraft data for barometric altitude, identification code, and emergency conditions. The air traffic control centers use this system data to verify the location of aircraft within a 60-mile radius of the radar site. The secondary radar also provides rapid identification of aircraft in distress. Secondary radar operates in the 1030 MHz to 1090 MHz range and has transmitting power from 160 W to 1500 W.

Weather Radar Systems

In the U.S., a network of over 150 ground-based weather radars, known as the Next Generation Weather Radar (NEXRAD) system, is operated by the national weather center. Weather radar characteristics are similar to the airport surveillance radars. Both use the same frequency band and high-power pulses. Frequency planning is important to ensure there is no interference between the technologies, for this reason.

The larger the object, the greater the energy returned to the NEXRAD radar. That creates the ability to "see" rain drops in the atmosphere. In addition, the time it takes for the energy beam to be transmitted and returned to the radar provides the distance to that object. Doppler radar pulses have an average transmitted power of about 450,000 W.

Advantage of RTSA for Pulsed Signal Viewing

The short duration of the pulses and the rotation of the transmitting antenna make it impossible for a spectrum analyzer in frequency sweep mode to capture the pulsed signals associated with these radar systems. This is true even when the trace maximum hold feature is applied to the instrument.  

Conversely, a spectrum analyzer with an RTSA, such as the Field Master Pro, can display the RF spectrum in the frequency domain and the full pulse characteristics in the time domain in the pulse analyzer mode. The pulse analyzer provides detailed numeric results for all the common radar parameters. This allows the radar signal to be always captured (figure 2). The result is field measurements of airport surveillance and weather radars can be completed accurately and quickly without interrupting operation.

Figure 2: Spectrum characteristics of common radar parameters captured with an RTSA.

The test set up is similar for both radar systems. A waveguide horn antenna is connected to the analyzer and pointed towards the radar. Measurements can be made miles from the radar location with this configuration.

To view the pulses in time domain, users can put the spectrum analyzer into zero span mode. Analyzing the pulse trains in zero span mode highlights the change in pulse repetition frequency used by the radar in different modes to analyze changes in atmospheric conditions. To view the individual 1 µs pulses, the time scale must be reduced to a few milliseconds. With the pulse analyzer option, the Field Master Pro can measure pulse rise times as low as 30 ns using a 40 MHz RBW, with sweep times as short as 60 ns.

Enabling the pulse analyzer option provides measurements on pulses and pulse streams in full compliance with the IEEE standard. For measurements of pulse repetition frequency, duty cycle and off time, at least two pulses must be captured and displayed. Typically, it is better to capture more for greater detail as shown in figure 3. Depending on the pulse train and individual pulse characteristics, it may not be possible to perform all measurements, such as rise time, in the same span.

Figure 3: Pulse analyzer capture of multiple pulses enables PRF and duty cycle measurements.

To display a single pulse, the sweep time is reduced. Vertical marker lines are automatically placed on the 10% and 90% linear power points for rising and falling edges, as well as horizontal markers for the 50% reference level used to measure pulse duration. The IEEE default values can be manually overridden, if required, with the MS2090A, as well.

To learn more about conducting measurements on pulsed radar signals, download the application note from Anritsu.

How to Locate and Mitigate Common Satellite Interference Issues

February 2, 2021

The biggest effect on robust satellite operation performance is RF interference. A growing problem in wireless communications, it significantly increases costs for satellite operators. A survey by the Satellite Interference Reduction Group (now known as the Satcoms Innovation Group) found 93% of satellite operator respondents suffer satellite interference at least once a year. More than half experience it monthly, and 17% see interference continuously in their day-to-day operations.

All these factors make it imperative for satellite operators to understand RF interference causes. Selecting appropriate test solutions is one critical step, as are implementing the proper evaluation and maintenance processes. By doing so, signal interruption can be reduced and debugging can be done more efficiently, for reduced associated expenses and downtime.

Interference Issues

Satellite earth stations (figure 1) form the ground segment of satellite communications. They contain one or more satellite antennas tuned to various frequency bands, making them susceptible to a variety of interference.

Figure 1: Typical satellite earth station.

Let’s outline a list of the potential causes of interference in satellite communication systems:

• Adjacent frequency emissions from other signals – Due to the extreme distance between satellite and earth station, the incoming power flux density of the satellite signal at the earth station is very low and susceptible to interference signals with higher power levels.
  
  This is particularly prevalent with the rollout of 5G. Figure 2 illustrates the band separation between a 5G deployment and satellite frequencies. The top shows the range of legacy low-noise block downconverters (LNBs) while the lower image is a bandpass filter blocking the 5G carriers.
  
  
  

  Figure 2: Existing Frequency Allocation for 5G and Legacy C-Band Satellite Downlinks
• Aircraft – Airplane altimeters generate signals in the 4200 MHz - 4400 MHz band, adjacent to the C-band downlink frequency of 3400 MHz - 4200 MHz for fixed satellite service stations (FSS). Even though there is no frequency overlap, aircrafts are much closer to the earth station than the satellite. Therefore, altimeter signals can overdrive the satellite feed system’s low noise amplifier (LNA), distorting the signal. Also, the LNA and LNB generally operate at frequencies wider than the authorized band, making them susceptible to out-of-band signals.
• High-power radar – S-band radars are used in high-power weather, air traffic control, surface ship surveillance, and similar applications. Given the intermittent (pulsed) nature of radar signals, interference can appear irregular. For example, in the case of surface ships, the interfering ship may disappear as it passes along the coast. Satellite ground stations near airports or coastal ports are especially prone to interference from these radar signals.
• Broadcast FM transmitters – Signals from nearby transmitters can overload the LNA at the ground station. Due to low power levels of the satellite signals, LNAs are optimized for gain. Satellite signals can be easily compressed by the LNA due to other signals present in the LNA bandwidth.
• Adjacent satellite interference (ASI) – As more satellites launch, slot spacing becomes more crowded, with only 2 degrees between many geostationary satellites. At the same time, the number of portable and mobile terminals is growing rapidly. These two conditions increase ASI.
• Satellite ground stations – Examples include unwanted emissions from other satellite ground stations which may either be spurious or out-of-band, as well as unauthorized transmissions (piracy) and intentional jammers

Real-time Spectrum Analysis  

Satellite operators require a portfolio of test instruments to mitigate interference problems. Some solutions are designed for long-term spectrum monitoring. Others address issues associated with equipment upgrades and interference from RF services operating in spectrum adjacent to satellite frequencies.

For example, a real-time spectrum analyzer (RTSA) with frequency range of 9 kHz to 54 GHz, such as the Field Master Pro™ MS2090A (figure 3), is ideal for monitoring downlink signals to search for interference and noise. Wide frequency coverage is necessary because there are more than 2,000 active satellites orbiting the earth that communicate with the ground through dedicated earth stations utilizing sub-6 GHz and up to 50 GHz bands.¹

Figure 3: The Field Master Pro MS2090A has wide frequency coverage to support satellite signal analysis.

Additionally, interfering signals can be intermittent in nature and difficult to track.  An RTSA samples spectrum continuously, ensuring that no interferer will go undetected

Spectrum Clearing Importance

The United States Federal Communication Commission (FCC) has launched an accelerated spectrum clearing program for the C-band for 5G. The clearing will take place over the lower 300 MHz range (3.7 GHz – 4.0 GHz) with the top 20 MHz used as a guard band. Similar efforts for C-band clearance are taking place at many other international locations.

While the repurposed spectrum is being cleared of legacy signals, field engineers and technicians must ensure that remaining satellite frequency bands are free of 5G interference. A Mobile Interference Hunter (MIH) with a spectrum clearing mode allows field engineers and technicians to set a go/no-go threshold. This number, in combination with a minimum hold capability, allows for efficient localization of good areas and those that need assistance.

Another initiative that requires a MIH is signal compression. For earth station operators, efforts are underway to adopt advanced signal compression technology to pack content from a 500 MHz band into just 200 MHz. Ground infrastructure, such as antennas and filters, will need to be deployed to protect the remaining bandwidth from interference.

Time Difference of Arrival

Signals generated by unauthorized satellite operators are another common interference source. The satellite signal may be generated without proper authorization (piracy) or by experiments being conducted by nearby research labs and private enterprise. Jamming may also be due to hostile transmissions designed to intentionally interfere with communications.

One method traditionally used by satellite operators for localization of interference signals is Time Difference of Arrival (TDOA). Operators use the satellites themselves to triangulate an interference signal, however, there are many sources of error associated with TDOA when pinpointing the interferer position. A MIH is a preferred alternative, as it uses audio signals and mapping for the driver, so it can make hundreds of measurements per second to guide the user to the signal.

Spectrum Monitoring

Satellite operators often require round the clock spectrum monitoring. A Remote Spectrum Monitor (RSM) can provide up to 24 RF inputs to monitor multiple earth station dishes simultaneously (figure 4). A high-speed multiplexer can be integrated into the RSM to switch between each dish RF signal.  

Figure 4: Single remote spectrum monitor in a control center rack monitoring multiple earth station dishes.

To learn more about improving satellite communications, download the Anritsu application note – Resolving Interference Issues at Satellite Ground Stations.

Note:
¹United Nations Office for Outer Space Affairs (UNOOSA) 

Importance of Spectrum Analysis in 5G Network Deployment and Installation

December 1, 2020

The rollout of 5G New Radio (5G NR) introduces many new design and test considerations for mobile operators, as well as engineers and field technicians responsible for the deployment and installation of the new network. To accommodate the use cases outlined in 3GPP Release 16 and their associated bandwidth, speed, and latency specifications, millimeter wave (mmWave) spectrum is being accessed. The higher frequencies, among other factors, place greater importance on test instruments that can conduct real-time spectrum analysis and support wide frequency coverage.

Enhanced Mobile Broadband (eMBB), Massive Machine Type Communications (mMTC) and Ultra Reliable/Low Latency Communications (UR/LLC) require high bandwidth that can be best found in the mmWave spectrum. While the high frequencies help deliver on the promise of up to 20 Gbps transmission, 1 ms latency and other 5G network key performance indicators (KPIs), there are inherent transmission issues associated with commercial environments. Examples include propagation loss and network timing. Beam forming and Time Division Duplex (TDD) technologies are being utilized in mmWave 5G to help overcome those challenges.

Beam Forming

Beam forming improves propagation loss inherent with high-frequency mmWave signals. It significantly increases transmission power by concentrating RF energy into a directional beam toward the UE.

To determine the UE location for directing the data beam, synchronization signal beams (SSBs) are transmitted in the 5G radio sector. All cell information is contained in the SSB and sets up the connection with the UE. This includes primary and secondary synching and physical broadcast channel (PBCH). SSB is always positioned before the data and is necessary for timing and reference. It is a key element of the TDD technology.

TDD Method

5G NR utilizes Frequency Range 1 (FR1), which incorporates sub-6 GHz spectrum, and Frequency Range 2 (FR2), which covers the mmWave frequencies. All FR2 and most FR1 transmissions will utilize TDD.

TDD creates dramatic changes in signal transmission and associated testing. As noted above, TDD introduces time slots into the signal, which are split among the SSB, uplink, and downlink.  Figure 1 shows a typical configuration where the yellow and orange represent the SSB and the blue and green represent the downlink and uplink, respectively.

Figure 1

The split of uplink and downlink in the subframes can be configured to one of 62 different options. Figure 2 shows an example of the first ten defined formats. Red indicates download (DL) slots, blue references upload (UL) slots and the X is designated “flexible.” While not relevant in the initial 5G rollout, flexible slots will be important as 5G evolves, as it allows for more dynamic applications.

Eventually, UE will be adaptive and communicate with the base station to determine the number of frames allocated to DL and UL based upon application. For example, there will be many more UL slots in stadiums and arenas during an event while there will be primarily DL slots at airports as travelers download music, movies, et al as they board flights.

Figure 2

Need for Real-Time Spectrum Analysis

Because 5G NR transmissions are TDD signals, the radio will be constantly switching between SSB, downlink, and uplink. The necessary switches occur rapidly, as the 5G NR frame is only 10 ms, meaning the test instrument used must be able to capture signals with no gaps. It is one reason why a real-time spectrum analyzer (RTSA) is best suited for 5G network installation.

A traditional swept spectrum analyzer conducts signal sweeps; it captures energy then processes it for a plot that is displayed on the instrument screen. An RTSA acquires the entire signal chunk and processes it simultaneously, eliminating coverage gaps associated with swept instruments that result in the possibility that the SSB or data will be missed.

OTA Measurements

Much of 5G testing will be done over the air (OTA), especially power measurements.  The main reason is that the multi element antennas required to support beamforming do not have a single RF test port. Power and modulation cannot be measured in the near field, but must be done in the far field, several meters from the transmitter (note: far field distance depends on transmitter frequency).

An RTSA provides the best view of the overall 5G spectrum. Without an RTSA, a swept tuned analyzer must be used in a MAX HOLD mode to build an image of the TDD signals. This is a relatively slow process and misses detection of intermittent interferers.

For detailed analysis of the 5G signal using post processing software, an IQ capture capability is required. A capture bandwidth of over 100 MHz is necessary for most 5G spectrum allocations, especially in the mm wave bands. In addition to the bandwidth, the capture time must ensure at least one full 10 ms 5G frame is captured. Post processing software can then be used to view the individual resource elements of the framed and the SSB position.

For accurate OTA measurements, field technicians need to understand four aspects:

• The RTSA with an antenna needs to be in far field to get fully formed beams for power measurements
• The RTSA with an antenna needs to be in the boresight (highest power zone) of the beam being measured
• Total radiated power of beamforming antennas must be measured with the Equivalent Isotropic Radiated Power (EIRP)
• To accurately measure EIRP, distance to antenna and all cable and antenna gains/losses must be considered

An RTSA needs wide frequency coverage for 5G network test to handle current and future FR1 and FR2 needs. The Field Master Pro™ MS2090A (figure 3) has integrated and continuous frequency coverage from 9 kHz to 54 GHz, which allows users to view the RF spectrum and measure all transmissions. In addition to the frequency coverage, a spectrogram display is beneficial, as it helps locate interference quickly and accurately. Spectrograms are views of how the signal amplitude changes with time, making them especially useful when monitoring for intermittent or interfering signals.

Figure 3

Key 5G Measurements

Table 1 provides a list of key measurements to be made on 5G networks to ensure KPIs are met.

Adjacent Channel Power (ACPR)	Modulation Quality
Cell/sector ID Verification	Occupied Bandwidth
Equivalent Isotropic Radiated Power (EIRP)	SSB Power and Position
Frequency Error	Time Offset

Table 1

EIRP – In 5G networks, EIRP needs to be measured directly from a base station. The analyzer used by the field technicians must have sufficient bandwidth to make accurate measurements on signals occupying 100 MHz or more, as well as enough sensitivity and low noise floor to record EIRP at realistic distances from an active base station. The ability to gate sweeps on the downlink or SSB portions of the 5G frame is also critical to making a stable measurement.

SSB – Decoding the SSB can reveal important cell characteristics, such as cell ID, frequency error, and beam power. SSB measurements allow transmitter testing on a live gNB. To properly decode the signal, the field technician must know center frequency, bandwidth, and subcarrier spacing of the signal under test. This can be entered manually or by using a 3GPP-defined band and Absolute Radio-Frequency Channel Number (ARFCN).

It is also critical to know the frequency position of the SSB relative to the center frequency of the signal. This can also be entered manually as an offset from center or by entering the Global Synchronization Channel Number (GSCN). In cases where the SSB location is unknown, an analyzer with an auto SSB detect feature can automatically search the 3GPP-defined raster of potential SSB positions to locate it.

Time Gating

When making measurements on the 5G base station, the analyzer is typically only receiving the SSB and downlink power, so the trace will rise and fall rapidly as the radio switches between transmitting and receiving over the 5G frame. This makes it very difficult to conduct accurate power or bandwidth measurements on the signal because of the drastic power fluctuations. To stabilize these measurements, time gating can be used. It allows users to focus the spectrum analyzer results on a specific section of time within the frame.

To learn more about conducting these measurements, watch this 5G webinar. You can learn about how to select an RTSA for 5G by downloading a white paper, as well.

Proper Spectrum Monitoring Backhaul Configurations for Drone Detection and Mitigation

March 23, 2020

Spectrum monitoring is a vital network management tool, not only because of existing RF spectrum crowding and the added network density associated with the rollout of 5G. The need to monitor transmissions, particularly at government locations such as airports, seaports, and other points of entry, as well as at utility grids, military facilities, and other sensitive sites, is necessitated by the increasing use of drones.

Spectrum monitoring systems can help facilitate the identification and removal of illegal or unlicensed interference signals, including drone detection and mitigation. Designing the proper backhaul will help efficiently and effectively process the signals so that appropriate action can be taken in a timely manner and security can be maintained.

RF Signal Identification Methods

There are three primary methods for geo-locating RF signals of interest – Power of Arrival (POA), Time Difference of Arrival (TDOA), and Angle of Arrival (AOA). In most situations where modulated signal bandwidths are greater than 25 kHz, including those associated with drone detection and mitigation, TDOA is the most accurate process. 

TDOA can provide a very precise location estimate of < 100 meters in a short period of time. To successfully use TDOA, it is essential to understand the type of signals that can be used; how the results depend on the geometry of the measurement based upon the probes and source locations; the sources of uncertainty and how to mitigate them; and how to detect if the answer is meaningful.

Each measurement updates the signal position to provide tracking points for the path taken, so the faster the data can be processed the greater the success. TDOA geo-positioning is performed using I/Q measurements. I/Q captures can be quite large, gathering many Mbits of data as a function of both capture bandwidth and bit resolution. The time to send all I/Q measurements back to a central location for analysis significantly impacts the tracker’s ability to update new measurement positions and follow fast moving objects such as drones.

Anritsu provides TDOA measurements with its MS2710xA Remote Spectrum Monitors (figure 1) and Vision software suite. The remote spectrum monitor probes have the necessary I/Q capture and precision timing features to collect and transmit the timestamped I/Q data used in TDOA measurements. Remote spectrum monitor probes can monitor and measure frequencies up to 6 GHz, with a maximum capture bandwidth of 20 MHz. Remote spectrum monitor probes also come in a number of form factors and packages to satisfy a variety of scenarios.

Figure 1

Backhaul Channel Options

Once a drone is detected, its video signal is captured, processed, and transmitted in an I/Q format. There are several methods to implement the backhaul channel to transfer this valuable measurement data back to a central server. Some are better suited for transmitting I/Q data than others.

Optical Fiber – A preferred choice is to use fiber in a closed system. Each deployed remote spectrum monitor will need a pair of Ethernet-to-fiber converters or media converters that manipulate the signal so it can be transferred via fiber to a PC/server location for processing. A second converter at the PC location translates the fiber signal back to Ethernet.

There are several important parameters that must be considered when using a media converter, including:

• Ethernet conversion speed (use only a converter capable of 10/100/1000 Mbit)
• Fiber compatibility with the converter (typically 1310/1550 nm)
• Distance over which the converter can communicate (20 km, typical)

At the receiving end of each IQ backhaul, an Ethernet switch is required to combine every signal for input into the PC/server. A Gigabit plug-and-play switch should be used.  

If the media converter and switch are deployed outdoors, they should be placed in a waterproof enclosure. Depending on the environment and signal power, remote spectrum monitors for drone  detection can be deployed at approximately 500 meters on a triangle side. If one remote spectrum monitor is co-located near the PC/server, two fiber rolls should be used; otherwise, three rolls are required.

Microwave Point-to-Point (P2P) – Another option for drone detection and mitigation applications is microwave P2P. Many P2P systems (transmitter and receiver pair) will use 2.4 GHz or 5 GHz as a backup in the event of adverse conditions. When used at the lower frequencies, Wi-Fi connectivity can be established to further extend the link, if needed. For P2P to operate effectively, line of sight between transmitter and receiver must be established. Figure 2 shows an example of a microwave P2P backhaul using radio links.

Figure 2

Considerations for choosing a P2P system include:

• Weather resistance – the antenna must be able to survive harsh environments
• Ease of alignment – each transmitting antenna must point directly at its receiver counterpart. Antenna vendors should offer alignment tools to facilitate this process.
• Power – distance over which the transmitter/receiver pair may be placed. To transmit over a 500 m distance, a vendor rating of a 1 km transmission is suitable to account for weather and RF environment impediments.
• Ports – If transmissions via Wi-Fi might be necessary, ports for 2.4 GHz antennas will be required

Ethernet – A remote spectrum monitor can be connected via Ethernet to a larger hub, such as a corporate network, where transmissions can be done. A typical scenario would be to place a remote spectrum monitor at a building rooftop serviced by an internal LAN connection with other monitoring solutions spaced across multiple buildings with an internal LAN connection. When configuring this backhaul, the bandwidth required by the I/Q transmissions must be considered so as to not disrupt normal network traffic.

Wireless Modems – 4G LTE modem tests have shown that tracking updates can be 2-3 times slower compared to fiber or P2P, making them an unsuitable option for a commercial drone tracking operation. A minimum of 5 MB/sec data rates on the uplink is necessary if a wireless modem is used for I/Q data transmission.

If there is no other option, a router should be used so Dynamic Host Configuration Protocol (DHCP) configured addresses match with Domain Name Server (DNS) servers. Routers can be programmed to automatically use a specific DNS service, allowing the use of names (rather than numeric IP labels) for accessing the remote spectrum monitor.

Learn More

To learn more about how to configure a backhaul for remote spectrum monitoring that may include drone detection, download a new application note entitled Backhaul Requirements for Anritsu Remote Spectrum Monitors Use for RF Signal Tracking Applications.

Benefits of Real-time Spectrum Analysis When Testing Today’s Wireless Networks for Interference

January 24, 2020

RF field engineers have relied on spectrum analyzers to detect transient interferers and unwanted hidden signals for each wireless communications generation. As we enter a new era where millimeter wave (mmWave) frequencies are becoming more commonplace due to the rollout of 5G and advanced aerospace/military systems, selecting the proper spectrum analyzer becomes more important. In fact, it can be the difference between quickly and efficiently locating and removing an interfering signal or missing it entirely and having network performance fail to meet key performance indicators (KPIs). 

Spectrum analyzers can be segmented into three types – real-time spectrum analyzers (RTSA), swept-tuned models and vector signal analyzers (VSA). Each has advantages and limitations in testing scenarios, but when it comes to interference testing, the RTSA should be the first tool out of the bag.

Advantages of Real Time Spectrum Analyzers

An RTSA can continuously acquire samples of elusive and transient signals and perform Fourier transform (FFT) analysis on them. Swept-tune spectrum analyzers and VSAs use fast FFT analysis but follow a serial process flow to acquire samples and calculate the FFT.

The RTSA process flow can acquire a new sample frame while simultaneously performing the FFT on the previous sample frame. This parallel processing requires fast digital hardware and a large memory buffer. RTSA spectrum analyzers perform FFTs at an extremely fast rate.  For example, Anritsu’s Field Master Pro™ MS2090A (figure 1), can perform 527,000 FFTs per second for a 512-point FFT.

Most RTSA instruments display the FFT data in a colorful density display, which summarizes the amount of time RF energy is present at any given frequency/amplitude during the capture. This is especially useful in interference analysis for two reasons. First, signals of very short duration will still be displayed on the screen for the user to see. Second, when an interfering signal is hidden inside an intended modulated signal, it will still stand out as a different color and be apparent to anyone monitoring the spectrum.

Figure 1

Swept-tuned Spectrum Analyzer

A swept-tuned spectrum analyzer uses an architecture that sweeps its local oscillator (LO) to down-convert the input frequency range to a fixed intermediate frequency (IF). It is then filtered by the resolution bandwidth (RBW) filter and detected. This type of spectrum analyzer can only see a small portion of the frequency span at any moment. It is blind to transient signals that appear when the sweep is scanning a different part of the input frequency range.

Vector Signal Analysis Process

A VSA down-converts the signal of interest within a certain bandwidth to a fixed frequency IF. The IF analog signal is sampled by an analog-to-digital converter (ADC) and a stream of time domain samples is used for modulation domain analysis. For spectrum analysis, the time domain samples are transformed into a frequency domain spectrum using the FFT. When the FFT calculation is finished and the results are transferred to the display, the next sample frame is acquired. Although the LO is stationary, the VSA is blind to signal events occurring between sample frames.

Other RTSA factors

The key performance metric when evaluating spectrum analyzers, especially for finding transient interference, is the probability of intercept (POI).  POI is defined as the minimum signal duration necessary to accurately measure the amplitude of a continuous wave (CW) signal. The smaller the POI, the higher the chance of catching intermittent interference. 

POI is affected by several factors, including FFT processing speed, sample rate, window overlap, RBW, and span. Let’s examine a few of these considerations.

Windowing

When a sample block is acquired for FFT analysis, the mathematics of the FFT presumes the time domain signal is periodic with a segment equal to the sample frame duration. The samples at the beginning and end of the sample frame create an unwanted discontinuity in the time domain. The frequency domain energy is spread out, rather than concentrated at the original sine wave’s frequency.

Spectral leakage is undesirable in FFT spectrum analysis because the ability to resolve closely spaced frequency components with different amplitude levels is lost. Signal amplitude is no longer an accurate representation of the true signal level because the spectrum energy has spread out.

To eliminate these effects, the segments in the sample frame are multiplied by a window function that smoothly tapers them near the start and end of the frame to zero. Thus, when these modified samples are presented for FFT analysis, the periodic extension does not have a sharp discontinuity and the spectrum leakage is reduced.

RBW and Span Interdependence

Windowing also serves to implement the RBW filter in FFT spectrum analysis. When the FFT analysis is performed with a window function applied to the input signal sample record, the effect is equivalent to a bank of parallel RBW filters. Figure 2 shows a simplified representation of the RBW filter band frequency response, separated by their -3 dB bandwidth, across the span of one FFT. You will see that the RBW filter response is incomplete at the start and stop frequencies of the FFT span. Because of this, the usable bandwidth is only about 80% of the full analysis bandwidth of the FFT, which is set by the input sample rate (Fs) times the number of points (N) in the FFT. 

Figure 2

Window Overlap

Since windowing tapers the time samples at the beginning and end of the sample frame to zero, transient signal events at the edges are lost. Overlapping is used to ensure the capture of these signal events. Each sample frame for the FFT is partially filled with samples that were captured in the previous frame.

For total amplitude accuracy, the signal must occupy the entire area under the window function. Meeting this condition will ensure that at least one sample frame and its FFT will capture the full signal amplitude since the start of the signal event can occur at any time relative to the start of any sample frame (Figure 3).

Figure 3

If FFT analysis is performed using overlapping sample frames, the required signal duration for 100% POI is shorter. If the window length is smaller than the FFT size, the required signal duration will be shorter, thereby improving POI. A wider RBW corresponds to shorter window length and shorter POI.

Density Display Resolution

Selecting the appropriate point FFT size depends on the situation. A 512 point FFT size allows for the lowest POI, however the FFT frequency bin resolution is coarser and can cause the display to appear more granular at low RBW settings. The 1024 point FFT size allows a smaller RBW setting for a given span with less display granularity at the cost of a higher POI.

Because a 512 point FFT updates 527,000 times per second, it can show the measurement results in a more meaningful way than an LCD display. A density display shows the color graded intensity of a signal event. The warmer the color, the more frequent the signal event. The density display also can simultaneously show up to six spectrum traces, which represent the detection result for all the FFTs calculated during the acquisition interval.

Spectrograms Locate Intermittent Signals

A spectrogram displays spectrum vs. time, making it important in detecting intermittent interferers. After every acquisition interval, a new line is added to the bottom of the spectrogram and the oldest spectrogram line is discarded from the top. The spectrogram has 142 lines and the maximum displayed time record is 142x acquisition time. The spectrogram differs from the density display in that it does not indicate how many signal events happened during the acquisition time.

To learn more about spectrum analyzer architectures and the benefits and limitations of each, download the Understanding Key Real-Time Spectrum Analyzer Specifications white paper.

How to Mitigate Interference in Spectrum Reallocation Environments

November 19, 2019

Spectrum reallocation has become a necessary approach used by government regulators to address the growing bandwidth issue associated with the increasing use of wireless devices. While it carves up RF frequencies to meet consumer demand, it can create challenges when it comes to interference. That’s why mobile operators need the proper test tools to effectively conduct spectrum clearing and geo-locate legacy signals that may hinder transmission of new signals.

A prime example is the repurposing of the 600 MHz frequency band (previously the domain for broadcast television) in the U.S. for LTE and 5G networks. As mobile networks begin to use this spectrum, new license owners will no doubt detect unwanted legacy signals. Therefore, spectrum clearing sweeps and signal geolocations must be done to determine the interference signal source to ensure networks meet key performance indicators (KPIs).

In most cases, the uplink channels for the 600 MHz band (663 MHz - 698 MHz) are surveyed for unwanted signals. The reason is that uplink signal power levels are lower than the downlink and, therefore, more susceptible to interference problems. In areas where time domain duplexed (TDD) signals are used, downlink and uplink signals share the same frequency spectrum and are separated only by time. Spectrum clearance becomes essential in these scenarios. Even low levels of interference can result in poor cellular performance, particularly slow data throughput.

As part of the 600 MHz band reallocation, 1,000+ TV stations must move or cease operations in Band 71, so cellular service can be deployed (Figure 1). The uplink cellular band is in the 35 MHz range of spectrum located between 663 MHz - 698 MHz; downlink is 35 MHz wide and is in the 617 MHz - 652 MHz range. Each band is divided into seven 5 MHz blocks, from A through G. Under the new regulations, broadcast television stations will remain on channels 36 and below. Channel 37 and the gap between uplink and downlink bands will be used for other applications.

Figure 1: 600 MHz Deployment Spectrum

Conducting Spectrum Clearing

The first step field technicians must take when clearing spectrum is to set-up a handheld real-time spectrum analyzer (RTSA), such as the Anritsu Field Master Pro™ MS2090A, for the frequency channel to be analyzed. Other parameters of interest include RBW/VBW settings and reference levels. The technician must also determine if a preamp is required. To make the process more efficient, specialized software, such as the Anritsu Mobile InterferenceHunter MX280007A (MIH) system, can be loaded on a tablet or similar device that is compatible with the RTSA.

Technicians can select the Spectrum Clearing option from the dropdown menu on the MIH software, which displays a pop-up window (Figure 2) to set the threshold power at a level where a signal would start to be considered problematic. Appropriately setting the threshold limit means the proper signals will be below the threshold and not trigger unnecessary alarms. They will also be ignored during data collection, for more efficient and accurate analysis.

Figure 2: Spectrum clearing pop-up window.

Threshold value is typically a few dB above the instrument noise floor. Spectrum traces can also be saved each time a signal is measured above the threshold setting. If needed, the value can be changed after collecting measurements in the spectrum clearance drive operation. Done during post-processing, this capability is useful for locating unwanted signals at unanticipated power levels.

With a RTSA such as the Field Master Pro MS2090A, technicians can set a go/no-go limit based on the spectrum analyzer’s channel power measurements. Other spectrum analyzer options, such as min and max hold, can be used for the drive. Min hold is generally for measuring signals that appear underneath larger fluctuating signals, such as those in LTE uplink bands. Max hold can be used to find intermittent or bursty signals, which are often encountered in radar applications.

Value of Spectrum Traces

Spectrum traces for each measurement point where unwanted or interfering signals are present can be saved with this type of solution. This facilitates discovery of the type of signal(s) present by viewing spectrum profile, center frequency, and bandwidth. For example, broadcast TV previously occupied the 600 MHz band. If the spectrum shape captured and recorded at the point of interference has the characteristics of a broadcast TV transmitter, it is likely that the transmitter has not been shut down.

Another benefit of saving spectrum traces is that it allows other signals in the band to be recorded. Typically, this is caused by intermodulation products mixing at out-of-band frequencies or by illegal signals. Additionally, faulty or degraded cable TV networks can leak radiation into both the UHF and VHF bands. Figure 3 shows a RTSA display of a signal present in the 600 MHz band. Such signals can disrupt the cellular signals to be deployed in the area.

Figure 3: Potentially interfering signal in 600 MHz band.

Tracking the Interference Source

A field technician’s job is not finished when the spectrum clearing operation is complete. All detected signals should be geo-located so the source can be determined. Software such as MIH can continuously measure the signal(s) of interest and provide signal position estimates on a map, as well as voice prompts to guide the user to the signal source of the interfering signal(s).

Spectrum clearing is only one benefit of interference hunting software. For example, field technicians can use MIH to quickly locate single and multiple emitters. Channel power measurement capabilities in MIH also allow users to track and locate signals that fluctuate in frequency, such as amplifiers and repeaters driven into oscillation.

Pinpointing Interference Source

With dedicated software such as described here, field technicians can get close to the interference source. In some cases, however, it will not be able to locate the exact signal position. In these scenarios, field technicians will have to find the precise interferer position by using a Yagi or other directional antenna connected to the RTSA.

For this final step, the field instrument can be configured to emit an audio tone that changes in pitch and volume as the power of the interfering signal changes. Connecting a Yagi antenna and sweeping it in a systematic pattern in the direction of the interfering signal, then noting the antenna direction when the tone is at a maximum, makes it possible to quickly identify the precise direction in which the signal is coming.

To learn more about spectrum clearing and geo-locating signals in a spectrum reallocation environment, download a free application note.

 

Why Real-time Spectrum Analysis is Needed to Verify Wi-Fi and Bluetooth Performance in Ever-crowded ISM Band

July 30, 2019

IEEE 802.11 – a.k.a. Wi-Fi – and Bluetooth^® have now been in use for 20 years. Their widespread adoption in everything from cell phones, headsets, and PCs to credit card readers and children’s toys has led market analysts to estimate that there are 10+ billion Bluetooth and Wi-Fi devices active in the world today. The number of connected devices in the 2.4 GHz ISM band will continue to rise as new applications are established in Internet of Things (IoT) and Machine-to-Machine (M2M) environments.¹

As all these use cases overcrowd the ISM band, spectrum monitoring becomes more imperative to create an optimal user experience for an ever-demanding consumer base. This is no easy task. A typical public space, such as an airport, entertainment venue, or your favorite shopping center, may have thousands of consumers accessing the 2.4 GHz spectrum at any given moment. Even people in their homes may see Wi-Fi access points from a number of neighboring properties. In these situations, user density can restrict throughput and ultimately impede the user experience.

Understanding Wi-Fi and Bluetooth Technologies

The IEEE 802.11 standard defines 14 channels in the 2.4 GHz band with each channel typically being about 20 MHz wide (Figure 1). In the real world, though, only three or four non-overlapping channels are possible. In the U.S., channels 1, 6, and 11 are the most common. The access points for these channels continuously transmit beacon signals so that users can find and connect to their network.

Figure 1

While the Wi-Fi standard allows for some flexibility, these beacon packets are typically transmitted every 102.4 ms and have a duration of a few milliseconds. Beacon signals are transmitted using the lowest modulation scheme supported, which in the 2.4 GHz ISM band is differential binary phase shift keying (DBPSK) or differential quadrature phase shift keying (DQPSK). When transmitting data between a user device and access point, the modulation standard changes to orthogonal frequency-division multiplexing (OFDM) to increase data throughput.

Bluetooth Basic Rate (BR) and Enhanced Data Rate (EDR) radios use a 79 channel, pseudo-random frequency hopping radio with 1 MHz channel spacing. The rate is 1,600 hops a second, giving an interval of 625 µs, but again real world scenarios are much different – a 366 µs packet rate is typical. When Bluetooth Low Energy (BLE) rolled out, it used fewer channels and shorter packets. This helped reduce battery consumption and enabled watches and low-power headsets to incorporate the technology.

Bluetooth Revision 1.2 uses adaptive frequency hopping (AFH), which temporarily removes channels from the hopping plan that were experiencing high levels of interference. This technique improves the Bluetooth user experience in the presence of high levels of Wi-Fi activity.

Analysis Tools for Today’s Spectrum

As Wi-Fi and Bluetooth evolve and become even more prominent, different test tools are needed to ensure networks match Key Performance Indicators (KPIs). Traditionally, field technicians and engineers used swept frequency spectrum analyzers to monitor activity in the 2.4 GHz ISM band. Swept-tuned instruments, however, are not able to give a comprehensive view because most signals in the band are short in duration and/or frequency hop at very fast rates.

Monitoring up to three Wi-Fi access points and their connected devices along with Bluetooth devices and other potential users becomes impossible with a swept frequency spectrum analyzer. Using a trace maximum hold feature is not effective because it does not reveal the real-time activity in the spectrum, and signals that appear for short durations may go undetected.

Field personnel responsible for network performance need a real-time spectrum analyzer (RTSA), like the Anritsu Field Master Pro™ MS2090A (figure 2). It provides a continuous stream of FFT measurements over a 110 MHz analysis bandwidth to cover the entire 2.4 GHz ISM band in a single display. Within this bandwidth, all signals of duration greater than 2.055 µs are captured and displayed at their full amplitude.

Figure 2

Better Insight with a RTSA

When a BLE device is active, the three advertising channels can be seen at 2.402 MHz, 2.426 MHz, and 2.480 MHz (Figure 3). The same packet is transmitted on each of these channels advertising sequentially and listening devices monitor them to establish a connection. The advertising packets are transmitted every 20 ms to 10.24 seconds and are typically 2 ms in duration.

Figure 3

With the RTSA spanning the full 2.4 GHz ISM band, three Wi-Fi access points can also be seen. Making measurements on these environments are necessary in facilities that provide full site Wi-Fi coverage, with geographically adjacent access points separated in frequency. When the access points are transmitting beacons with phase-shift keying (PSK) modulation rather than OFDM, the occupied spectrum has a curved shape.

Field engineers and technicians can view the spectrogram and power density simultaneously so a record can be kept of all signals transmitted over the spectrogram time interval. Each spectrogram line represents the max hold of all FFTs over a 60 ms interval. As the access points are beaconing 4 ms packets at a 102.4 ms rate, a signal is always detected in the spectrogram and spectral density displays, as shown in figure 4.

Figure 4

When the access points transmit data, OFDM modulation creates a flat top signal spectrum, while maintaining 20 MHz bandwidth. If a Bluetooth device is activated to find other nearby BR/EDR devices, it initiates a fast scanning raster of defined frequencies.

When the 2.4 GHz ISM spectrum is crowded, the Bluetooth adaptive frequency hopping algorithm reduces frequencies from the standard 79 channel hopping pattern that are not regularly receiving acknowledge packets from the paired product. A RTSA spectral density plot can clearly show when a Bluetooth AFH algorithm has stopped using the channels due to an active Wi-Fi access point.

We’ve only briefly discussed why a RTSA is necessary to measure the ISM band in today’s environments. To learn more, download a new application note entitled Monitoring Interference in the 2.4 GHz ISM Band with a Real-Time Spectrum Analyzer.

 ¹ Wi-Fi Alliance®; 2018 Wi-Fi® predictions, Wi-Fi Alliance.

RF Coverage Mapping Simplified with New Test Tools

March 12, 2019

Integration of multiple technologies across indoor and outdoor networks has placed tremendous pressure on mobile operators to maintain high-quality service. This applies to both public and private networks, as consumers have great expectations when streaming or surfing the Web, while public safety professionals must have reliable communications in mission critical scenarios. In many of these applications, RF coverage must be verified for multiple channels across a very wide frequency range.

A good example is how various commercial cellular operators must verify reliable service and acceptable throughput rates of indoor distributed antenna systems (DAS) found in many public spaces, such as shopping malls, airports, or sport stadiums. In many cases, numerous channels on multiple frequencies are used. Ideally, field technicians would test all these frequencies in parallel to maximize efficiency. Another example would be verifying the interaction of outdoor-to-indoor coverage for TETRA-based, in-building coverage systems and the resulting interferences.

Remote Spectrum Monitoring is Effective Solution

To address these network issues and preserve high-quality coverage in all areas, public and private operators are relying on remote spectrum monitoring tools, such as the Anritsu MS27101A Remote Spectrum Monitor shown in figure 1. The reason is simple – an effective test system can efficiently facilitate the identification and removal of illegal or unlicensed interference signals. The key is to select a system that effectively tracks multiple frequencies while being simple for field technicians to use.

Figure 1: Anritsu MS27101A Remote Spectrum Monitor

Monitoring spectrum on a continual basis allows management to identify problem signals as they occur in real time. With additional software, a remote spectrum monitoring solution can also serve as a signal analysis platform for communications systems, such as TETRA, including broadband measurements showing the incoming direction of a signal or as a SatCom monitoring platform.

Spectrum Monitoring – Need for Sweep Speed

For coverage mapping applications, it is important that the scanning speed be fast enough to capture all the desired channels while seamlessly following the movements of the field technician conducting the measurements. Outstanding sweep speeds – even for smaller resolution bandwidth (RBW) or video bandwidth (VBW) – are particularly important for narrowband communications standards, such as analog FM, P25, TETRA, DMR, and dPMR.

The need for fast sweep speed can be seen when evaluating the various technologies. For example, dPMR uses 6.25 kHz channels, P25 and DMR use 12.5 kHz channels, and TETRA channels are usually separated into 25 kHz blocks within a 5 MHz frequency segment. Therefore, the remote monitoring solution must enable field technicians to acquire channel power and signal quality data by simply walking through a building, arena, mall or other area of interest, as well as see colored breadcrumb trails that indicate the power level and walking path (figure 2). Another feature to look for is if the application can designate multiple frequency sets that are individually defined by center frequency, SPAN, RBW, and desired reference level.

Figure 2: Display of multi-frequency coverage mapping measurement

Control Via Mobile Device

The remote spectrum monitor shown in figure 1 can be controlled by technicians in the field using an Android-based mobile device and dedicated NEON Signal Mapper Software. A connection between the remote spectrum monitor and Android device can be established using a conventional Wi-Fi router. A communication test to verify that the device and monitoring solution are “speaking” to each other can be done at the beginning of the project by opening a web browser and entering the IP address of the remote spectrum monitor.

Before measurements can be taken, the NEON tracking unit must be calibrated. Once the calibration is complete, the technician can begin conducting tests. The software allows the measurements to be made using the remote spectrum monitor and displayed on the Android device. When the measurements are completed, the data can be stored locally on the mobile phone or tablet or uploaded to the cloud. It is recommended that sensitive and/or confidential data be saved on the device for security purposes.

Outdoor Coverage Mapping Option

Anritsu offers a software platform that is compatible with its spectrum monitoring hardware to automate data collection and for GPS-enabled coverage mapping, when outdoor monitoring needs to be done. The software allows users to view live data, record data related to given limit lines, or to record GPS-assisted measurement results.

Once settings have been verified, the field technician can begin recording and travel the designated route. All data is saved on the laptop’s hard drive. Because the data is gathered continuously, measurements need to be stopped as soon as the project is completed.  

Multi-frequency radio coverage mapping is necessary to maintain coverage in both indoor and outdoor environments. This post outlines a process and solution to achieve the goal and ensure public and private networks meet KPIs. To learn more, download a new application note entitled Radio Frequency Coverage Mapping with Anritsu’s Remote Spectrum Monitor MS27101A, Vision™ MX280001A Software, and TRX NEON^® Signal Mapper MA8100A.

How RF Technology Locates Public Safety Drone Threats

January 7, 2019

Anyone who has looked up in the sky the past few years knows drone use has increased significantly. So much so that the global market revenue for commercial and personal drone production is expected to be north of $11.2 billion by 2020, according to a Gartner study.

In many cases, drones can be used by public safety and broadcast professionals for advantageous processes, such as aerial-based spectrum analysis. For example, the compact (size of a mobile device), lightweight (255 g; 9 oz.) Spectrum Master™ MS2760A from Anritsu easily mounts under a commercial drone. Coupled with a small form factor computer stick to run the application, it is now possible to make coverage mapping measurements and search for interfering signals over a wide area in 3D.

Drones (figure 1), however, are becoming a greater safety concern in high-security areas, such as penitentiaries, seaports, airports, and government buildings. Identifying the location of the controller provides law enforcement with the ability to react in real-time to prevent nefarious acts and capture those who attempt them. The right system can identify key elements such as drone model, as well as the course, speed and altitude of a drone.

Figure 1

RF Technology Offers Best Solution

Several methods of drone detection can be used. Among the technologies are audio detection, jammers, video and radar detection and geofencing. The most effective tool, however, is RF technology. By intercepting video and control signals telemetry data sent between the drone and the remote controller, calculations can be made to detect, track and identify drone activity.

The data that drones detect can vary, but the most advanced systems on the market can detect:

• Location of the drone pilot
• Drone direction
• UAV ID, position and altitude  
• Speed
• Make / model

What to Look for in RF Solution

Many spectrum monitoring tools are available today, which makes selecting the best one for a specific application difficult. Here are a few guidelines for selecting a spectrum monitoring instrument, whether it be a receiver or a spectrum analyzer.

High Dynamic Range - A large dynamic range allows a small signal to be seen in the presence of a large one. This is essential if the signal of interest is small and close in frequency to a large signal. Dynamic range for a spectrum analyzer can be specified in many different ways with wildly different numbers. One example of a very useful dynamic range specification is greater than 106 dB in a 1 Hz RBW, using the method 2/3 (TOI-DANL).

Third Order Intercept (TOI) - Instruments with poor TOI will be more susceptible to generating spurious responses (spurs) internally that may be confused with real, external signals. In other words, the effect of an overload – or near-overload – appears and is more pronounced. A good TOI figure is 15-20 dBm, with two –20 dBm tones and no attenuation. Be wary of instruments that specify TOI with >0 dB attenuation, because this makes the TOI number artificially high.

Overload Indicator - A front-end overload is possible when signals are received through an antenna. If an overload occurs, add attenuation, external band-pass filters, or move to a different location. If the field user does not know that an overload is happening, a lot of time will be wasted investigating signals that are generated inside the instrument!

Reliability (Hardware Watchdog Timer) - A remote spectrum analyzer (figure 2) should include a watchdog timer to insure long-term stability for remotely deployed monitors. Watchdog timers are used to detect and recover from computer malfunctions, where accessibility is limited.

Figure 2

Fast Sweep Speed – Ideally, a minimum 20 MHz instantaneous FFT bandwidth is necessary to allow near real-time sweeps across a wide spectrum to be made. Conducting sweeps at rates up to 24 GHz/s will allow for the capture of multiple signal types, including periodic or transient transmissions and short “bursty” signals.

Displayed Average Noise Level (DANL) - Instruments with a poor DANL may not be able to see a low-level signal due to a high noise floor. In any given RBW, their noise floor will be higher than an instrument with a lower DANL. This tends to mask low-level signals. A good DANL for drone detection systems is generally less than –160 dBm in a 1 Hz RBW. When setting up a monitoring network, having a noise floor or DANL that is 3 dB better may mean half as many monitoring stations are necessary.

Low Spurious - RF and microwave transmitters often require extensive spur searches to ensure they are not causing potential interfering signals or radiating unintended signals outside of their designated signal parameters. A spectrum monitor with low self-generated spurious signals allows reliable detection of low-level signal for these transmitters under test.

Remote Firmware Update Capability - In the event of an application error or power fluctuation which causes an ongoing interruption in monitor communication, a re-boot policy will bring the remote probe back to its previous state. Under these conditions, the current firmware should automatically reload, and online operation restored. Instrument settings will then be restored to the previous state.

If, for any reason, the firmware in the unit becomes corrupted, a warning such as a “Golden” firmware image is used to return full operation of the probe. This feature is particularly useful for remote firmware updates.

Low Power Consumption - Having low power consumption enables remote placements of the drone detection system for longer periods of time.

Video Bandwidth Control (VBW) - A VBW filter is a fast way to average the displayed data and lower the noise floor. A user-adjustable VBW filter is an important feature when selecting a tool to monitor in the current RF environment.

To learn more about RF technology for drone detection, visit a dedicated spectrum monitoring page that includes how to select the best spectrum monitoring tool for your application.

 

600 MHz LTE, 5GNR and FirstNet Deployments Emphasize Need for Interference Tools

November 14, 2018

These are busy times in the wireless world. LTE is being actively rolled out at 600 MHz, the FirstNet public safety network is being deployed and the race to launch 5G New Radio (NR) is underway. While each brings long-awaited services, there are other unwanted aspects, including the potential for interference. For carriers and those contracted to maintain network operations, understanding how to locate and prevent interference that can degrade performance is becoming increasingly challenging.  

The influx of these new technologies, cell sites, and antennas will undoubtedly cause spectrum crowding. For some sites, the result can be one of the most difficult types of interference to discover and eliminate – intermodulation, which is generated when two or more transmitters’ signals are mixed together. Interference can be caused by many other factors – from rusty fences and tin roofs near a tower to faulty components and systems – as well. For engineers and field technicians, having the right test tools to mitigate interference of all kinds is even more necessary as new networks are rolled out. 

600 MHz LTE

One of the biggest challenges right now is the deployment of LTE over the 600 MHz band. Currently, a major carrier is installing more than 30,000 cell sites throughout the country to launch its 600 MHz LTE network. Other purchasers of spectrum during the 600 MHz auction include a leading satellite company and cable operator. While neither has announced definitive plans for how or when they will use the spectrum, they spent too much money not to eventually use up their blocks, creating a more congested band.  

The Federal Communications Commission (FCC) recognized the potential for interference and stated that licensees operating in the 600 MHz band must not cause harmful interference to reception of full power and Class A broadcast stations transmitting on a co-channel or adjacent channel. The FCC rules include a 5 MHz adjacent channel separation requirement when a 600 MHz LTE device operates within the protected service contour of a full-power or Class A television station. Adhering to this rule will require the proper test tools.

5G NR

Since the 5G NR standard was approved at the end of last year, mobile carriers have been preparing to deploy their next generation networks. In the past few months, each of the leading mobile operators has announced 5G sites in key cities, notably Atlanta, Chicago, Denver, Los Angeles, New York, and San Francisco.

The strain of 5G has not been felt yet by any network, as the service footprint is very small. There are also no 5G devices for consumers to purchase, let alone use. With full-scale deployment scheduled in 2019, though, carriers and those contracted to install 5G networks must have a test plan in place. Without one, the technology may not fulfill its speed, latency, and other performance promises at launch.

FirstNet

Band 14 deployments are underway in all 50 states and Puerto Rico, with the initial network buildout scheduled to be completed by year-end. While more than 2,500 cell sites have had Band 14 capability installed, a more telling number is that an additional 10,000+ sites are in the process of Band 14 upgrades throughout the country. On-air testing of Band 14 is ready in more than 40 states, including California, Florida, Illinois, North Carolina, Oklahoma and Texas.

Ensuring interconnectivity between FirstNet subscribers and users of the current public-safety broadband network will be imperative. Another key factor will be to ensure operation of FirstNet in any scenario, given the mission critical nature of the communications. For these reasons, having the proper interference tools will be essential.

Interference Test Tools

Deployment of all these new cell sites in an already crowded spectrum means that test tools will play a major role in all networks meeting their established KPIs. Therefore, field engineers and technicians need a comprehensive tool box with solutions that can monitor, track and locate interference and its sources. Anritsu has a portfolio to address all environments:

The Mobile InterferenceHunter MX280007A (figure 1) is a reliable tool to find single or multiple sources of interference. It works with the broad array of Anritsu’s handheld spectrum analyzers, providing interference hunting and spectrum clearing capability from 9 kHz to 110 GHz. The InterferenceHunter also has post-capture analysis capability so users can modify search parameters without re-driving the route. This allows re-analysis of the captured data as well as consulting with experts.

Figure 1

Remote spectrum monitoring tools are also available. These solutions are designed to mitigate interference problems and to identify illegal or unlicensed signal activity. These signals can be periodic or present at different frequencies over time, making the discovery and removal of these sources of interference a significant challenge. By using these tools to monitor spectrum on a continual basis, problem signals can be identified as they occur in real time.

The ergonomically designed MA2700A Handheld InterferenceHunter is equipped with a GPS receiver and antenna, an electronic compass, and a user-selectable preamplifier to help locate interference sources. It is designed to be used with Anritsu’s handheld field instruments that can be used to locate interference, which include the Spectrum Master™ handheld spectrum analyzers.

To learn more about interference and tools to mitigate it, visit this interference technology page.