Interference Hunting

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) 

Remote Spectrum Monitoring Importance Grows as Networks Densify

August 2, 2017

Full spectrum utilization is a growing concern for operators, broadcasters, and public safety officials. Spectrum monitoring, signal geo-location apps, and interference hunting systems all help create and maintain communications networks. Remote spectrum monitoring is of particular importance to help ensure today’s dense networks operate with minimal interference.

The significance of remote spectrum monitoring has been a topic of conversation throughout the industry. Here are answers to some important questions to better understand the need, operation, and importance of remote tools for spectrum assurance.

Q: How has the evolution of wireless networks created a greater need for spectrum monitoring?

A: As small cells (figure 1), C-RAN and similar network elements proliferate the landscape, a single technician who originally handled 30 sites is now responsible for as many as 120. So, as the networks densify with nodes, it’s almost impossible for someone to go out and visit all of them. Because of this market condition, tools that allow wireless professionals to look at the spectrum remotely are essential, so that if there is an alarm on a switch, they can examine what is happening in real time from wherever they are. In doing so, they have remote connectivity, which is the definition of spectrum monitoring. The other question is, “What happens if something goes awry and nobody is there to log in?” This is why recording is important. A remote spectrum monitor really needs to be looking 24/7 and recording at predefined intervals, so that when an alarm occurs, technicians can go back in time and analyze it.

 

Figure 1

 

Q: Is remote spectrum monitoring primarily for carriers or also for government regulators and larger enterprises?

A: Ultimately, it will be across the board. As for regulators, the government is already looking at white space frequency sharing and reallocation of frequency. We essentially monitor this for six months and scale it back if it isn’t used because the Federal Communications Commission (FCC) needs to reissue it. Connected cars, smart manufacturing facilities and other applications with wireless connectivity or wireless reliance will eventually be covered, as well.  

Q: How will you have a better sense of where problems might be coming from with spectrum monitoring?

A: Spectrum monitoring gives a better sense of where to start looking for a problem. By utilizing power of arrival or angle of arrival for triangulation and time difference of arrival (TDOA), you can approximate where a certain problem originates. Cell sites are relatively close in today’s networks, so adding this capability at every location might not be cost-effective, meaning it’d have to be strategically done. In public safety, there can be six or seven broadcast or simulcast towers covering up to 80 square miles. In areas this large, having at least a general idea of where the problems are coming from would be a huge impact. This relates to how spectrum monitoring enables more proactive than reactive methods of procedure, as it involves setting up a site in advance to make detecting and resolving interference issues much easier.

Q: As people are looking at more dynamic spectrum environments, such as cognitive radios or software-defined radios, are mistakes being made in the context of spectrum sharing?

A: Yes, absolutely. Cognitive and software-defined radios are trending type applications for spectrum monitoring. For example, if a person travels from Los Angeles to New York with a software-defined radio device, there is a potential problem. If it operates at its known frequencies in New York, it can actually interfere with other radios. Most people don’t even know that they are causing an interference problem.

Q: Are these errors more pronounced in LTE networks?

A: Even now, we are still facing issues with certain devices since 700 MHz LTE rolled out. As we re-band and squeeze things between bands, we will continue to run into these types of problems. Higher modulation schemes require better signal-to-noise ratios and improved RF environments. Some new technologies will utilize beam forming and similar transmission technologies. If there are other devices that are broadcasting very broadly simultaneously, then they can still interfere on the beam form.

Q: What role does the spectrum monitoring play in the context of IoT security?

A: There is an expectation that most of the RF frequencies used in IoT will be encrypted at some point but we are not there yet. Because IoT is such a blanket term, some devices in these systems may operate on clear channels, so it is possible that these issues of outside sources interfering with IoT frequencies will be a problem. If that is the case, spectrum monitoring will be key given the critical nature of many IoT applications.  

Q: What do Anritsu’s remote spectrum monitoring tools detect and what are the capabilities?

A: Three different models – the MS27101A, MS27102A, and MS27103A – are available. They essentially measure the channels and record that information. These measurements are sent to a remote database or saved locally. From this data, reports are generated that detail occupancy, white space, power of arrival and TDOA. High-speed channel scanning is available, so multiple channels can be viewed on a single display.

Anritsu spectrum monitoring solutions (figure 2) collect both RF signal data, as well as IQ captures. If a technician needs to identify a particular signal, IQ data can be extracted from the signal and stored as a signature.

 

Figure 2

 

The Anritsu toolset can be integrated to cover the entire spectrum monitoring requirements. By having all the necessary spectrum assurance resources, Anritsu has established an entire spectrum monitoring ecosystem that can improve network operation efficiently.

To learn more about remote spectrum monitoring and how it can be used in a variety of applications, download this free application note.

Tips to Manage Interference in Densified Networks

May 23, 2017

Mobile operators continue to densify their networks to meet the ever-growing consumer demand for high-bandwidth services. They have deployed DAS, small cells and similar solutions to maximize the RF spectrum and maintain customer satisfaction as mobile usage continues to rise.

The integration of these solutions helps to alleviate bandwidth pressure points on mobile networks, helping them achieve stated KPIs set forth by operators. For field technicians and engineers responsible for the performance of these networks, test equipment remains a priority. To meet the verification processes associated with DAS, macro cells, and small cells, test instruments need to evolve and offer new capabilities.

In today’s post, we will answer questions about network densification and how test solutions are meeting the challenges they have created.

Q: As networks densify with DAS, small cells and other solutions, how is testing evolving?

A: Densification makes capacity increasingly important, especially with LTE. Because of this, the focus is on received signal quality and spectrum assurance. There are a variety of ways to ensure a clean uplink to significantly improve capacity. This focus on capacity also means, in some cases, new measurement techniques are required. DAS systems, such as the one shown in figure 1, are a prime example of shifting measurement techniques.

Figure 1

Q: What makes DAS testing different from other network elements?

A: The number of tests drastically multiplies in DAS. Typical tower installation tests consist of antennas, splitters and other passive RF components. These tests can have up to 150 sweeps to determine return loss, distance-to-fault, cable loss, and PIM. Some additional fiber tests may also be necessary. These tests can be done with traditional processes.

There are considerably more tests associated with DAS, as procedures may require testing three or four frequency bands on every cable. Even a medium-sized DAS install could require several thousand tests. For example, a football stadium can have as many as 15,000 tests, which means that the testing, troubleshooting, and reporting process can take months.

Q: How will testing transform to address the increased complexity of DAS?

A: Because of the large number of tests needed, existing processes based on tower techniques don’t scale, so new procedures need to be developed. Automation of field tests, as well as reporting, is currently possible, and is one big step towards making DAS testing more effective.

Q: How do you deal with the increased amount of interference opportunities brought by densification?

A: The first step is to understand that interference is a receive issue. Signals that reach a radio’s receiver will affect the radio’s front end, even if these signals aren’t wanted. These unwanted signals create a reduction in the radio’s sensitivity, causing increases in bit error rate (BER), frame rate, dropped calls and other negative effects. This occurrence is called receiver desense or desensitization. If severe enough, it’s known as receiver blocking.

Interference does not have to be on a receive channel. It just must arrive at the input of a receiver, and it can be on a different frequency than the receive channel. In other words, it can be in band, but it doesn’t have to be on channel. This makes the receive pre-filter very important, as it can be selected to block out-of-channel, in-band, signals. If an unwanted signal does reach the receiver’s input, however it can cause desense.

Q: How do interference sources change from macro cells to small cells?

A: The biggest difference between macro cells and small cells is that small cells are likely closer to the source of interference. Since RF signals exhibit path loss, the closer a receiver is to the interference, the stronger the interfering signal will be. Small cells, because of their multiple locations, will be closer to some RF sources. They will be affected by interference that would not affect macro cells.

Anritsu offers various solutions for conducting more efficient interference hunts. Starting with the simple methods, there are traditional direction-finding tools. Using a spectrum analyzer and a Yagi antenna (figure 2), the user can look for the strongest signal and triangulate. There are also tools that can actually put a map on a spectrum analyzer. A higher level is car-based signal location, where a user can drive around and seek the strongest signal. This method tends to lessen the effect of multi-path, an issue in dense urban areas where small cells are likely to be deployed.

Figure 2

Another solution is remote monitoring, because interfering signals aren’t always present. A signal’s occurrence times can be characterized through short-term monitoring. All Anritsu spectrum analyzers are web-enabled, meaning they can be controlled from a distance via web browser. So, a user can configure one at a site and leave it for weeks. They can check the site remotely to save time and money, and maintain network performance.

There is also long-term monitoring. A set of headless spectrum analyzers can be either temporarily or permanently put in place. Software for these devices allows users to monitor and maintain signal quality through their networks as aspects change. The devices can be comfortably monitored remotely.

Q: What impact does densification have on spectrum assurance?

A: Spectrum assurance is an umbrella term for a family of spectrum monitoring, interference hunting and signal mapping tools that can be used in-building and outdoors. Anritsu is developing these in response to our customers’ requirements for the capacity associated with LTE and densification.

Q: Where do you see testing in the future?

A: We’re going to be moving closer to real time. Right now, cloud-based solutions are tremendously efficient. The test automation for DAS is cloud-based and that will continue. There will be more control, remote expertise and remote dispatch in the near future, as well.

Q: Monitoring allows you to identify and fix a problem. Can it also allow operators to optimize the use of the network resources?

A: Yes. One example is a service-assurance solution such as Master Claw, a big-iron software application. Another is a software application that works with long-term RF monitoring probes to help characterize when interfering signals occur and how they appear. Through triangulation, this application helps determine the interfering signal’s location and provides all the necessary information to find it.

There is much to learn about DAS, interference and the test tools necessary to address network densification. Anritsu has published an application note that explains DAS testing procedures, as well as an interference hunting application note. Anritsu has also designed a poster that provides examples of interference sources. All are helpful resources for field engineers and technicians.

Deregulation Trends Emphasize Need for RF Spectrum Testing

March 9, 2017

The first few months of 2017 have seen sweeping changes and policy reversals at the Federal Communications Commission (FCC) and other government agencies. It’s likely that more modifications are coming as Congress confirms new White House cabinet members. Notable in these amendments has been the trend towards deregulation. This approach will affect every industry, including wireless communications, which means engineers, field technicians, contractors, and other professionals responsible for wireless networks must be aware of the potential implications.

Take, for example, the FCC’s authorization, just before Mobile World Congress held February 27 – March 2 in Barcelona, of LTE Unlicensed (LTE-U) for use in cellular base station equipment by Nokia and Ericsson. Previously discussed in this blog, LTE-U will be deployed in the unlicensed U-NII bands to augment licensed services. Very little interoperability testing has been done, and major players in the Wi-Fi market are claiming that LTE-U will harm Wi-Fi networks – especially those in public spaces like New York City, which provides free Wi-Fi across a large portion of Manhattan (figure 1). Yet the LTE-U technology was approved, signaling the FCC’s intention to deregulate.

Figure 1: An aerial view of the Wi-Fi network in Manhattan.

The Effect on Test

We should expect the deregulation trend to continue. This movement will create a need for testing of air interfaces and protocols, as well as training to help interpret results and make best use of available tools. Solutions will run the gamut from mobile interference hunting solutions, such as this system developed by Anritsu, to cloud-based tools, including SkyBridge Tools™, and remote spectrum monitoring systems that safeguard wireless networks continuously. Anritsu also offers a portfolio of handheld analyzers that provide diverse measurement capabilities, such as the S331P Site Master™ Ultraportable Cable and Antenna Analyzer. A combination of all these tools will most likely be necessary to ensure compliance in the ever-crowding RF spectrum.

IoT Influence

Another element is the emergence of technology sectors like Internet of Things (IoT), which are already very diverse across a matrix of applications that balance useful bandwidth versus useful range. Figure 2 shows a typical IoT landscape. Add into that dimensions for battery life, cost per unit of data, and other variables, and the wireless environment becomes quite complex. Mobile Experts LLC, based in Campbell, CA, reports that there are 47 different technologies applicable to IoT use alone! It’s worth noting that many of these technologies will operate in unlicensed bands, including those that are already crowded with incumbents.

Figure 2: Typical IoT landscape diagram.

What happens when Wi-Fi, IoT, and LTE-U collide in the unlicensed band? It’s too soon to tell. What we do know is that making sense of it will require test equipment and people who know how to use it.

Anritsu provides white papers, application notes, webinars, and certified training courses to help engineers, field technicians, and other wireless professionals meet the ever-changing needs of ensuring network performance. One example is a new interference hunting poster that is available for free download.

The Value of TDOA Measurements in Maintaining 4G Network Performance

February 9, 2017

Ensuring 4G networks perform to carrier specifications is an everyday challenge for field technicians. To achieve high-speed operation and reach established KPIs, interference must be mitigated. Time Difference of Arrival (TDOA), a technique for geo-locating RF sources, is a highly effective method to efficiently achieve this goal by making it easier to locate – and subsequently correct – interference.

TDOA can provide a very accurate location estimate (< 100 m) in a short period. 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, what the sources of uncertainty are and how to mitigate them, and how to know if the answer is meaningful.

To most accurately locate the interference source using TDOA, wireless field technicians must consider multiple factors. Of course, high-accuracy spectrum monitoring tools and advanced software are paramount. There are other key variables, which we will now explain.

Modulated Signals – To time-align the received signal at each probe used, the signal must contain a non-random, non-repeating structure. Many interference sources emit white noise, which lacks the structure necessary to align the signal. Simple sources, such as a sine wave generator, produce a repeating pattern that has structure, but does not allow for a unique time-alignment.

Modulated signals, such as FM radio broadcasts and cell phone signals, contain internal structures that can be aligned in time. For this reason, TDOA is typically used to locate rogue broadcast signals, and other communication signals that are out-of-band or otherwise unexpected.

Reflections and Signal Strength – Usually, signal strength is not an issue, if the signal is clearly present. Field technicians need to get a discernable I/Q diagram. Typically, 10 dB to 15 dB above background is required to eliminate any noise. The capture bandwidth also needs to be narrow enough so that other modulated signals are excluded. Be aware that higher signal strength will not necessarily produce a better answer.

Multipath signals will tend to broaden the I/Q pattern and make it more difficult to find the proper time-alignment. If the strongest component of the signal is a reflection off a nearby surface, then the distance calculation will be the reflected signal path, not the direct straight-line path between the probe and source. The test software cannot know this, so the result is a systematic offset to the distance calculation, equal in magnitude to the reflected distance of the signal.

Probe Synchronization – To calculate the TDOA wherein each data stream is shifted in time to find the optimal signal alignment, the same signal must be captured at each probe location. To make this as efficient as possible, it is best to have short capture times, perhaps 10s of milliseconds. To capture the same signal at each location, however, the probes must start collecting signals simultaneously. Further, the precise timing of each I/Q pair is required in the data stream so that once aligned, the time shift can be accurately calculated.

A good TDOA system will produce location estimates within 100 meters of the source location. An RF signal travels 100 meters in about 300 nanoseconds. Therefore, the timing of the signal must be known with very high precision.

Hyperbolic Lines – A TDOA measurement reveals a time difference in the arrival of the interested signal at two or more probes. The time difference is multiplied by the speed of light to give a change in distance between each set of probes and the RF source. For a set of two probes, the distance does not specify a specific location, but rather a hyperbolic line centered between the two probes (figure 1). Therefore, a successful TDOA measurement on two probes does not give a unique solution, but a continuous set of solutions along a hyperbolic curve.

Figure 1

Triangulation – A three-probe TDOA measurement will produce three hyperbola, the intersections of which yield possible locations for the RF emitter, as shown in figure 1. If the source is inside the triangle formed by the three probes, the result may be a single intersection point. It is not unusual to have a few intersection points for the three curved lines, especially if the source is very far outside the probe triangle. It is usually possible to use the relative receive powers at the probes to determine which intersection is the right location.

Sample Rate – The number of I/Q data pairs collected per second is the sample rate. The time separation is multiplied by the speed of light to get the distance separation, so the spatial resolution is directly proportional to the temporal resolution. It is tempting to just increase the sample rate to increase the spatial resolution, however, there is a practical limit to how much the spatial resolution can be increased by raising the sampling rate.

Figure 2 shows data from two probes, one slightly ahead of the other. The dots represent sample points. There are not enough data points to accurately reproduce the shape of the curve, so the green and blue lines represent the best guess at the peak position for each set. Because the sample rate is too low, a large error is introduced. In Figure 3, the sample rate has increased and the peak positions are accurately found. Increasing the sampling rate further is not necessarily going to increase the accuracy.

Figure 2

Figure 3

Cable Length - The typical transmission speed in a coaxial cable is about 2/3 the speed of light. For every 20 meters difference in cable length, a 30-meter offset in position is introduced. Because this error in location cannot be reduced by averaging, it may be necessary to enter cable lengths into the geo-location algorithm for each probe. Cable length may not be a significant concern if the cables are uniform, or if the differences are small compared with the expected spatial resolution.

Time Delays – Another concern is internal delay in the probe hardware and the analog and digital paths used to capture, process, and store the I/Q data stream. This internal delay adds a systematic error that cannot be removed with signal averaging. The I/Q data pairs must be time stamped in the data stream to do time-alignment. Most remote probes will use a GPS signal to synchronize both the start time of the captures and the clocks used to add the time stamps. GPS chips produce a pulse-per-second (PPS) signal that is very reliable and precise. An internal clock ticks at a very high and stable frequency, and time stamps are inserted into the data stream at regular intervals based the clock tick count.

Emitter Location – If the RF source is inside the triangle formed by the three probes, the accuracy is expected to be high. When the emitter is behind one of the probes, even if still close to the probe triangle, the uncertainty is higher. Also, for probes far away, the angle of incidence of the hyperbolic lines makes the area of uncertainty larger.

Signal Averaging – A good way to reduce random uncertainties in measurements is through averaging. With TDOA this is accomplished by repeating the measurement several times and averaging the distance differences found for each probe pair. This number will vary from measurement to measurement due to statistical noise and uncertainty in the measurement data.

Intermittent Signals – With TDOA, intermittent signals are a particular problem. The probes must begin capturing at the same time, and each must capture a long enough data set that the overlap of the signal will be significant. To accomplish this, Anritsu implements proprietary algorithms to capture and time align each bursty signal.

An application note entitled Time Difference of Arrival (TDOA) has just been published and is available for a free download.

RF Interference Hunting – Why Remote Spectrum Monitoring is Becoming a Must

October 14, 2016

The continual rise of accessing the Internet via mobile devices and consumers’ desire to be connected at all times has maximized 4G network capacity. Consumer demand for mobile data is exponential and shows no signs of slowing. According to a report by Mobile Experts, LLC, we consumed mobile data at a rate of over 10 Exabytes per month in 2014, and by 2020 this rate is projected to increase to over 70 Exabytes per month (table 1).

Table 1: Projected growth of mobile data traffic

The overcrowded RF spectrum and use of unlicensed and licensed signals make interference a major issue that is also trending upward. With the number of possible interference sources increasing, it’s not a question of if, but when it will happen to a network under your responsibility.

For years, field technicians out on an RF interference hunt were armed with a handheld spectrum analyzer, appropriate antennas, maybe some mapping software and other tools. These solutions are excellent for signals causing interference on a regular or constant basis. If the signal is only present occasionally, or if a very fast response time is necessary, a short-term or long-term spectrum monitoring solution has proven to be more effective.

With the rapid expansion of wireless communications, the need for robust networks relatively free of interference is a necessity. Capacity can be degraded by the presence of illegal or unlicensed signals that interfere with legitimate transmissions. These interfering signals can be periodic or present at different frequencies over time, making their discovery and removal a significant challenge. Spectrum monitoring tools can help correct this problem.

Spectrum monitoring can also serve to enforce compliance with government regulations. Public safety, air traffic control, railroads using positive train control, and military must all have access to communications free of impediments and distortion. Compliance with spectrum regulations is often enforced by spectrum monitoring. Selecting the Proper Remote Spectrum Monitoring Tool Remote monitoring can be as simple as setting up a handheld spectrum analyzer to record traces and checking back in a week, or as complex as using dedicated rack mount spectrum monitors (figure 1) in combination with networked software to continually characterize the spectrum and locate interference sources. Selecting the best remote monitoring tool is dependent on how it will be used. Here are four common scenarios:

Figure 1: Rack-mountable remote spectrum monitoring analyzer

• Real-time Remote: Traveling to and from an area can exceed the time spent doing spectrum monitoring and interference location. Often the source of interference is gone by the time the engineer arrives. Real-time remote monitoring allows for instantaneous “on site” analysis when the need arises, and for engineers to make measurements in several places at once.
• Spectrum Recording Network: Interference affecting public safety, critical communications, and cellular telephony can occur at all hours. Unusual propagation caused by fog and atmospheric temperature inversions often occurs at night, when engineers are off the clock. Deliberate interferers (aka “jammers”) will often operate at night or on weekends, when they can maximize effect without being detected. Spectrum recording networks allow engineers to “go back in time” and make measurements on events that occur during off-hours.
• Alert on Event: In cases of critical interference, or when interference from a suspected source is transient, it helps to provide engineers with an immediate alert when pre-programmed conditions are met. This is typically done by activating a limit line and generating an email or text message when the signal level exceeds or drops below the limit.
• Location by Triangulation: By coordinating the on-board clock of at least three spectrum monitoring stations and knowing their GPS coordinates, the location of an interference source may be automatically estimated by mathematical methods. Location estimation provides an optimized starting point for engineers when beginning their search for an interference source.

To learn more about remote spectrum monitoring tools and their importance in tracking down interfering signals, visit a new technology page.

Ease of Use and Movement Critical for RF Signal Hunting

September 27, 2016

The recipe for locating RF signal sources is one part science, one part art form, one part visual observation, and a sprinkle of luck. The best way to learn signal hunting is to track signals, as this gives field technicians a sense for how RF propagates and reflects, as well as how to determine what’s real and what’s not.

Locating RF signal sources is often challenging, because it involves a lot of physical activity as you change location to build evidence that points to the source’s location. It’s rarely comfortable, requiring hours of work – often times in less than perfect conditions. RF signal hunters must contend with heat, cold, wind, rain/snow, dust, and insects. One day they are atop a hill and the next they are standing on a rooftop. These scenarios can make each kilogram of equipment weight feel like five kilograms at the end of a long day. For these reasons, RF signal hunters want their equipment to be rugged, easily portable, and lightweight.

Keeping notes during an RF signal hunt is critical because after a while, it’s easy to forget what you’ve already looked at. The key tools for RF signal hunting are a map and a compass. A GPS is also sometimes used. Using a directional antenna, bearings from various locations plotted on a map can give a good sense for the approximate location of an RF signal. Paper maps have the advantage of being lightweight, but they lack the ability to change scale – marks on a map covering dozen square miles don’t help when you’ve narrowed the location down to a city block. Also, paper maps blow around in the wind, and are damaged by precipitation. Laptops and tablets offer maps that allow scaling, but they’re not typically very rugged and, like paper, they don’t like to get wet.

Need for Minimization

Minimizing the amount of equipment and tools is important from both a weight and complexity perspective. It’s impossible to simultaneously support a laptop, type on its keyboard, and manipulate the controls of a radio receiver. Much of the time, the solution is to set some or all of the equipment on a flat surface, but some locations encountered during an RF signal hunt might not be perfect for this. Ideally, you want something that can be body-worn and operated with only two hands.

Ease of movement is critical during an RF signal hunt. Getting into position to make measurements can involve squeezing through tight spaces, climbing onto a railing or ledge, or crawling under obstacles. Manipulating several pieces of equipment in these locations can be next to impossible.

A Better Way

To address these environmental issues, Anritsu’s Handheld InterferenceHunter MA2700A (figure 1) is a preferred option for RF signal hunting. When combined with an Anritsu handheld instrument (such as the Spectrum Master™, Site Master™, Cell Master™, and BTS Master™) the InterferenceHunter has a built-in GPS and compass. Maps are prepared for loading into the instrument using the easyMap software tool. Using the Interference Mapping tool, the GPS location and compass bearing of the InterferenceHunter is displayed directly on the map.

Figure 1

InterferenceHunter can be fitted with a variety of directional antennas and filters depending on the signal being hunted. No laptop is necessary and both the handheld instrument and the InterferenceHunter can be operated with two hands, improving mobility and reducing weight. Using the InterferenceHunter is a simple point-and-click operation – each time the trigger is pulled, a bearing line is created on the instrument’s map display. The RF signal source will be located near where several bearing lines converge, as shown in figure 2.

Figure 2

Once you’re in the general area of the RF signal source, mapping becomes less useful. At this point, most hunters use methods where the RF signal strength is indicated by the pitch of an audio tone. The aforementioned “Master” instruments offer this in the Signal Strength meter tool. Headphones with a standard 3.5 mm stereo plug are recommended, especially for outdoor use or high ambient noise situations.

Using these tips and the tools described can reduce the amount of time needed to successfully locate an RF signal source. For more information, check out a new white paper.

Successful DAS, Small Cell Installation and Operation Requires Well-trained Staff

August 5, 2016

As service providers and venue owners race to deploy solutions in a way that provides added value to consumers, DAS and small cell installations have skyrocketed. Analyst firm IHS Infonetics reported in May that the DAS market alone grew 11% from 2013 to 2014, reaching $2.2 billion. Similarly, research firm Markets and Markets estimates the global small cell market will be worth $3.92 billion by 2020.

One reason for the need for DAS and small cells is the fact that an estimated 80% of mobile usage is done by consumers when they are in their offices, shopping at malls or sitting in an arena or similar venue. Previous blog posts have discussed the challenges – most notably interference – associated with this and how to overcome them when installing DAS and small cells.

A key thing to remember about DAS (figure 1) is that the need for documentation of antenna systems is increasing. A typical tower-based antenna system may require 50-150 measurements, traces, and photos to show that the installation meets quality standards. DAS may require 1,000-15,000 traces, photos, and other deliverables to show that the installation meets performance standards. Each of these deliverables needs to be inspected, renamed, and perhaps have the markers and limit lines set and judged.

That kind of explosive growth and documentation requirement places a tremendous burden on the individuals responsible for installing and maintaining DAS. We have spent many a post explaining how the proper test solutions can help these field professionals locate and eliminate interference. One other tool that is invaluable when ensuring optimum network operation is knowledge. Given the pace at which the market is evolving, one way to acquire that valuable asset known as “smarts” is through educational courses.

Training is Key

Proper training can do more than help the technicians doing the work. Service providers are constantly looking for methods to reduce operational expense while maintaining KPIs. A well-trained workforce makes for more efficient small cell and DAS installations that are completed correctly the first time to avoid expensive reworking. Because of all the potential interference sources associated with these deployments, this should not be underestimated. Properly trained and certified technicians will help expedite the provisioning of services that can make or break customer retention, as well.

Another factor as to why training is becoming imperative is that the DAS and small cell segments lack standardization. Installers work in a very convoluted industry with very few authorities that agree on best practices.

What to look for in DAS Training

The question of if training is necessary has an obvious answer. A more difficult query is what makes a DAS training course effective. Here are some key attributes to look for.

• Instructors – Remember back in high school when there was that one teacher who knew how to bring out the best in you? Instructors can do the same thing when it comes to training. Make sure the instructor is well-versed on the technologies and is on the cutting-edge of telecom workforce needs.
• Hands-on – You can only learn so much from reading and listening so make sure the course you take is more than 50% hands-on. Time needs to be dedicated to constructing and testing simulated DAS branches. Ask if attendees make measurements, interpret and analyze information to find problems and correct them, and develop a sound strategy for consistent documentation and efficient, effective workflow.
• Proper Equipment – Selecting a course that puts industry leading solutions in your hands so you can conduct measurements to verify what you learn is most effective. Anritsu handheld instruments, such as the Site Master™ and PIM Master™ analyzers, as well as the ACCESS Master OTDR are specified into many scopes of work published by operators for a project.
• Curriculum – Many programs sponsored by DAS manufacturers focus on specific hardware, limiting what is taught and learned. Make sure you select training courses that have an emphasis on DAS theory, so you learn how a system works, and can recognize components, their functions and performance ranges. You should also learn correct cleaning procedures, as well as how to test and troubleshoot components and DAS assemblies and sub-assemblies for return loss, insertion loss and PIM. If you are focused on active DAS, you need to ensure that the training is anchored in fiber optics. Better courses will have exercises on testing fiber and system components, splicing fiber and include state-of-the-art simulation of a fiber system.
• Certfication – The course that is taken should conclude with a comprehensive examination that tests how well attendees comprehended the subject matter. Upon passing, attendees should receive a certificate that verifies knowledge and expertise so operators are sure they are hiring the best.

Anritsu offers the gold standard in certification training for the wireless industry, including DAS courses. You can learn more and register by visiting Anritsu Training and Education.

Proper Sensitivity in Mobile Interference Hunting Systems Aid in Finding Sources of Interference

February 25, 2016

Wireless networks are being impacted by interference that degrades performance. For wireless service providers, issues such as dropped calls, blocked calls, and data throughput are all affected negatively by harmful interference. Operators need to utilize the full capacity of their networks to keep up with consumer demand. One solution is a mobile interference hunting system that can deliver a fast and simple method to locate and correct interfering signals.

Using the proper hunting system, field technicians and engineers can locate many interferers over a wide range of power levels in a variety of RF environments. Adding mobility to direction finding enables more measurements to be collected in a shorter time span. This enables special averaging techniques to be employed to mitigate multipath and quickly identify the location of the interferer.

One fundamental idea used in a mobile interference hunter system is that when a measurement is being taken at a fixed position of a CW emitter, the measured power should not fluctuate. If it does, there are variations in the path loss. By averaging several measurements, the change in power level generally will be reduced. Selecting the right amount of averaging can sufficiently reduce the variation to make interference hunting fruitful. Labels on the sensitivity numbers are intended to guide the user in selecting the proper amount of averaging for different usage situations, but they are just guides, not fixed usage rules.

Sensitivity Settings

For example, Anritsu has developed a Mobile InterferenceHunter (MIH), shown in figure 1, that has software with a Sensitivity setting with values that range from 0 to 5 and are labeled.

The number is shown in the status bar at the bottom of the display.

Figure 1. Status Bar 

The purpose of this setting is to reduce the sensitivity of the software to rapid power variations caused by multipath propagation. This is done by taking multiple measurements for each data point and averaging the measurements.

There may be situations in which the sensitivity setting isn’t sufficient to smooth out variations. In such cases, settings in the spectrum analyzer are employed to further clean up the measurements. Generally, the first thing a field technician should do is to go to a narrower VBW. That is the suggested initial step because it slows down the measurement the least. A good starting point is a video bandwidth of 300 Hz. Narrower bandwidth can be used, if needed, to obtain a reasonably stable reading. Be aware that sweep speed can be significantly impacted with VBW less than 100 Hz.

Trace averaging in the handheld spectrum analyzer can be used to reduce variations. This isn’t the first choice since measurement speed is significantly reduced, but it can be employed, if necessary. Field technicians should experiment with the number of traces to be averaged to get a reasonably stable value while not slowing down the measurement process too much.

Changing resolution bandwidth (RBW) doesn’t reduce variations in signal level, it just changes the noise floor for measurements. You may say, “Wait a minute, when I reduce RBW on my spectrum analyzer the noise variation goes down.” That happens because, by default, the VBW is coupled to the RBW and as the RBW is reduced, the VBW is also lowered to maintain a 3:1 RBW/VBW ratio, or whatever ratio you have chosen. If you decouple the VBW from the RBW by turning Auto VBW off, you will see that changing RBW by itself doesn’t affect the signal level variations.

To learn more on interference hunting, you can download an application note that outlines a variety of techniques to help locate interference sources.

Pros and Cons of Different Map Formats When Hunting Interference

January 20, 2016

Locating interference sources has traditionally been labor intensive and time consuming. Thankfully, traditional methods such as manually making numerous measurements from multiple locations with a directional antenna and then using triangulation to approximate the signal location have been replaced by automated tools.

The more advanced process saves valuable time and money by automatically taking and processing several measurements using designated software that resides on a laptop or tablet. Using this data, along with a handheld spectrum analyzer and an omnidirectional antenna, the system guides the driver to the source of interference.

One element that helps make this process so efficient is the integration of maps into the solution. For example, the Mobile InterferenceHunter (figure 1) allows field technicians to easily install and use maps when trying to locate a rogue signal to ensure wireless networks perform according to specification. One question that is sometimes asked is which is the preferred map format – Google maps or those created using OpenStreetMaps™. In this post, we will discuss the pros and cons of each.

Figure 1

Google Maps

Advantages: With Google maps you always have the latest map since it is being fetched from Google. It also covers everywhere in the world for which Google has maps, which is practically anywhere there are roads. Another benefit is you don’t need to predefine your search area before starting. When you begin a hunt using Google maps, the right location is centered on screen once the GPS receiver in the spectrum analyzer is locked to the GPS satellites, whether you are in Moose Jaw, Saskatchewan, or Key West, Florida or anywhere else.

You can play back a hunt that was done with Google maps regardless of the map source used during the hunt. This could be advantageous if you have to change maps in the middle of a hunt because you drive off the edge of a map. The log file of the data from the hunt gives signal strength and location information, so it can be shown on either map format after the hunt.

Disadvantages: Depending on where you are conducting your hunt, Google maps may not be accurate or unavailable. For example, in China Google maps is often blocked without notice and can be off by as much as ¼ km.

You also need a live connection to Google during the hunt. If you are searching in an area where you don’t have a reliable connection to the web, Google maps isn’t the right choice. Also, because you are probably using a mobile device for Internet access, its signal may interfere with your hunt, either by being at a nearby frequency or by causing fundamental overload to the spectrum analyzer. There are ways to minimize both, but you should still be aware of each.

OpenStreetMaps

Advantages: When using maps created using OpenStreetMaps, you can save many maps onto the laptop or tablet and choose the one to use at the start of or during a hunt, as shown in figure 2. Maps available from Anritsu were created using OpenStreetMaps. Users can select maps of North America, Europe, the Middle East, Africa, Latin America, and Asia-Pacific.

Figure 2
 

Ease of installation is another plus of OpenStreetMap. Once you have finished downloading a file, simply drag the ZIP file onto the map area of compatible solutions, such as the Mobile InterferenceHunter. The ZIP file will be automatically converted to the proper format and the process only takes about 10 to 30 seconds, depending on the file size. It is then displayed and saved for future use in the Installed Maps directory. The conversion process happens only once for each map file.

Disadvantage: There are practical limits as to the map size. One that is very large can take an impractically long time to convert to the format that the interference hunting solution uses and it may be too large to be used on many tablets. For example, a map that covers about 175 miles is 1.18 GB and is too large to convert.

Importance of Map Flexibility

The flexibility of map selection is particularly important for field technicians and engineers over a wide geographic area and who may need to have may different maps available. You can learn more about mobile interference hunting tools and techniques, by downloading free application notes.

The Top Features You Need in a Spectrum Monitoring Tool

October 13, 2015

Spectrum monitoring is often used as the first part of an interference hunting process. The current move towards small cells, as well as the interference and cell edge issues inherent in LTE, only increases the need for a clean spectrum and greater use of advanced spectrum monitoring tools.

There are many spectrum monitoring tools available today, which makes deciding on the proper solution a challenge. In this post, we offer a few guidelines for selecting a spectrum monitoring instrument. It is important to pay attention to specifications and features. Missing or sparse specifications are a warning of a low-quality instrument.

Specifications that are present, but poor, can be harder to interpret. To save time, money, and aggravation, here are some features that your spectrum monitoring tools should have:

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. A good TOI figure is in the 15 to 20 dBm range, 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.

Phase Noise - Excessive phase noise appears as an elevated noise floor near any strong signal, and may mask a weak signal at a frequency near a strong signal. A good phase noise specification, such as –100 dBc/Hz at 1 GHz, means that this effect will be minimized.

DANL - Instruments with a poor Displayed Average Noise Level (DANL) may not see a low-level signal due to a high noise floor. A good DANL is quite helpful when setting up a spectrum monitoring station or network, ensuring that you can effectively monitor signals over a wider area. An example of a good DANL specification is less than –160 dBm in a 1 Hz RBW. Remember that when setting up a monitoring network, having a noise floor or DANL that is 3 dB better may mean that you need only have half as many monitoring stations.

Dynamic Range - A large dynamic range allows you to see a small signal when a large signal is also present. This is essential if the signal you are interested in is small and close in frequency to a large signal. Unfortunately, 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).”

Overload Indicator - It is important that your instrument indicates when its front-end is experiencing overload conditions. Overloads may generate spurious signals that appear to be legitimate, and these signals are made worse by poor TOI. If you are alerted when an overload is occurring, you can add attenuation, external band-pass filters, or even move to a different location. Otherwise, you may waste a lot of time investigating signals that are generated inside the instrument itself!

Multi-step Attenuator - A wide range attenuator with many different possible attenuation values is a fundamental tool when monitoring spectra. Some instruments have few or no attenuation settings, making it difficult to see low-level signals after enabling the attenuator. Coarse attenuation selections also make it more difficult to deal with input overloads.

Preamplifier - Weak signals may need preamplification to become visible. The difference can be dramatic, lowering the apparent noise floor by about 10 dB. It is important that the preamplifier be wide range, preferably equal to that of the instrument. Certain handheld spectrum analyzers (figure 1) limit the maximum attenuation when using the preamp to help you avoid setups that degrade the dynamic range of the instrument.

Burst Detect - Burst Detect is a feature found on Anritsu handheld spectrum analyzers that makes spotting or even locating intermittent signals much easier and faster. It is quite helpful to have a way to see bursty signals quickly because many of the current generation of digital signals are bursty in one form or another.

Video Bandwidth Control - A Video Bandwidth (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.

Field Strength Measurements - If coverage or exposure limits are important, you will want to measure in field strength units. This capability normally includes an automatically calculated correction for the antenna gain factors, as well as automatically converting measurements into field strength units such as Watts per Meter Squared. This is a real time saver if accurate, repeatable field strength measurements are required.

Spectrum Recording – Not all spectrum measurement devices allow traces, spectrograms, and events to be saved. If you are in a position where it is necessary to compare or report on measurements, the ability to store traces, either normally, through save-on-event, or within a spectrogram, will be important.

A free spectrum monitoring application note is available for download. It contains more information on features to look for in a monitoring tool, as well as techniques you can employ to locate interference.