Category: 5G

How mmWave Technology Will Transform Private Networks

Nearly all private 5G cellular network deployments to date have been in low-band frequencies, less than 1 Ghz, or mid-band frequencies, between 1 and 6 Ghz. There have been very few deployments of high-band frequencies above 20 Ghz, which are also known as millimeter wave technology, or mmWave.

That’s despite the fact that, in many ways, mmWave technology is a higher capacity extension of 5G.

MmWave technology offers more advantages for specific use cases where extremely high bandwidth and low latency are critical.

For example, mmWave could be used by autonomous vehicle fleets to transmit real-time updates on their location and status to each other. This could help improve safety and reduce the risk of collisions. Additionally, high-band mmWave frequencies could also be used in industrial settings to enable faster, more reliable communication between robots and sensors. This could help to improve the efficiency of production lines and allows for the creation of smarter factories.

More Private 5G mmWave Technology Deployments

However, there is a shift happening in the market.

More private 5G mmWave deployments are taking place, owing to several factors:

Improvements in technology and expertise are making deployment easier

The extremely high bandwidth and low latency of mmWave is becoming more relevant as more data-intensive and latency-sensitive applications emerge

mmWave Technology Deployment Challenges

However, mmWave poses numerous technical challenges for network deployment, with significant upfront costs, even over standard 5G. As such, design considerations are even more crucial for enterprises looking at private mmWave network deployments.

To help address these challenges, the FCC allocated spectrum in the 24.25-30 GHz range to enable new 5G and mmWave services in the US. This allocation of mid-band spectrum allows for wider coverage areas and lower latency than mmWave spectrum, which is typically limited to short-range, line-of-sight deployments. This spectrum greatly improved the availability and affordability of mmWave services, allowing for a broader range of use cases, including for private networks.

Enterprises must fully address the complexity of a network environment and the technical challenges it may present to a mmWave network to ensure ROI on these investments.

Self-Contained and Highly Intensive Environments Are Best Candidates for mmWave

Data-intensive use cases, particularly in open industrial settings, are some of the best candidates for private 5G mmWave networks.

This is because these use cases have the high-bandwidth and low-latency requirements necessary to justify a mmWave network. Large industrial settings provide the ideal functional environment in which to deploy it.

The industrial setting also provides the necessary physical infrastructure for a private mmWave network. The large area, along with the metal and concrete structures, provide the self-contained environment that is necessary for a successful deployment. The physical environment provides a reliable, robust mmWave signal that can be maintained even in the face of interference from other wireless signals. The large area allows for a highly intensive network that can provide faster speeds and lower latency than other types of wireless networks.

The environment also needs to be self-contained and highly intensive to maximize the benefits of mmWave networking. mmWave requires the environment to be free of external interference sources, such as other radio signals, which can interfere with the mmWave signal.

The environment also needs to be dense enough for the 5G signal to be able to propagate between multiple nodes. This density makes sure that the 5G signal can reach its full potential and provide the best possible experience for users.

As technologies like IoT and AI become more commonplace in these settings, mmWave will become increasingly more relevant because of the key advantages it delivers over 4G/LTE.

Use Cases for mmWave Technology

In general, mmWave technology is a viable option in self-contained environments where:

Line-of-sight and obstructions can be controlled

The principal concern is under design

There is no functional limit on how much bandwidth is needed or how low latency needs to be

In many cases, mmWave deployment is not necessary to enable a given use case. However, mmWave can often better address the specific pain points of particular use cases, making it a better option.

Empowering AI and IoT

For example, mmWave’s greatest strengths are its ability to support an arbitrary number of sensors or devices, and throughput essentially an arbitrary amount of data with minimal latency. This makes it extremely well-suited for AI and IoT use cases that need high throughput and low latency to make maximum use of these technologies.

MMWave’s superior performance also enables more complex applications, such as autonomous vehicles, which rely on a high degree of connectivity and responsiveness. It can also be leveraged to enable dynamic, intelligent networks that can learn and respond to changing situations and environments. MMWave can provide more secure connections, reducing the risk of data breaches and other malicious activities. MMWave is a superior option for many applications, especially those that require high throughput, low latency, and secure connections.

Enabling Real-Time Security Monitoring

However, mmWave presents difficult design considerations. Its signal is low-range and is easily blocked by any kind of obstruction. This is why controllable environments are of such importance in mmWave deployments. In highly dynamic settings, the high throughput of mmWave can become extremely difficult, if not impossible, to maintain consistently.

Security cameras are another excellent example of where mmWave can shine. As the cameras are static, obstructions are rarely, if ever, a concern. And mmWave can easily stream high-quality video with low enough latency for real-time monitoring and analysis — a crucial factor in security.

MMWave is also ideal for short-range bandwidth applications, such as backhaul for access points, where operators need to send large amounts of data quickly and reliably. MMWave is able to transmit data faster than sub-6 GHz frequencies and can facilitate a more consistent and reliable connection to access points. Furthermore, the small wavelength size of mmWave allows for the use of smaller antennas, which can then be made even more discreet, particularly useful when deploying access points in public places.

iBwave Simplifies Design and Deployment

While it’s important to identify the right use case when considering deployment of a private mmWave network, enterprises also need to ensure their network design lets them take advantage of mmWave’s strengths.

iBwave’s survey and design software is optimized to address the complexity of mmWave network design and presents easy-to-use and easy-to-understand interfaces that simplify the design process. Network designers can easily and efficiently design accurate networks, avoiding the risk of both overdesign and under design.

With iBwave Private Networks, designers can:

Model venues in advanced 3D with AutoCAD import

Design from a database of vendor-modeled network components, including Small Cells, Aps, cables, controllers, routers, and more

Calibrate prediction with survey results

Run key project reports

iBwave Private Networks delivers the simplest and most reliable solution for planning, designing, and delivering private, high-performance, mmWave networks.

Advanced and powerful features such as the Fast Ray Tracing Prediction Engine, Prediction Calibration, Inclined Surface Modeling, and Attenuation by Frequency ensure the network you design functions exactly as intended.

The Fast Ray Tracing Prediction Engine allows users to quickly design, simulate and visualize the complete coverage of a mmWave network and provide users with the insights they need to make informed decisions.

With Prediction Calibration, users have the ability to compare the simulation results to survey results, allowing them to ensure their design is accurate and reliable.

The Inclined Surface Modeling feature ensures that the propagation of the signal is accurate for surfaces that are not perfectly flat.

Attenuation by Frequency allows users to simulate the frequency-dependent signal attenuation, ensuring the accuracy of the predictions.

Plus, cloud-connectivity and seamless integration with iBwave Mobile Survey ensures that iBwave can meet all your mmWave network needs, present and future.

Overall, the features of iBwave make it the perfect tool for designing and deploying mmWave networks. The powerful simulation capabilities, seamless integration with iB and cloud-connectivity make it an invaluable asset for all mmWave network designers.

For more information, take a look at the full product breakdown of iBwave Private Networks.

And for more insights into the growing demand for private 5G networks, download our latest e-book: Top Trends in Private Networks for 2023.

What to Expect With the 5G Shift in Private Networks

While enterprises have predominantly relied on public networks in the past, that situation has changed with the 5G shift in private networks.

Private networks, principally 4G/LTE, have become much more common, offering numerous advantages over public networks, including:

Enhanced cybersecurity

Greater control

More flexibility and customization

However, there is a shift happening in the market and more 5G deployments are taking place, driven by:

Increasing private network allocations in 5G

Greater availability of needed technology and expertise

The device ecosystem embracing 5G

More industry groups certifying 5G solutions

Network design considerations are very important for any enterprise interested in a private 5G network. Justifying the ROI on a private network deployment will depend on accurate network design to deliver that ROI.

Networks must be designed to take advantage of the full potential of 5G’s superior capabilities.

Present and Future Use Cases Should Drive Adoption

For the moment, 4G/LTE remains dominant for a few reasons:

Many deployments required 4G as an anchor

Few standalone 5G products on market

Fragmentation of 5G bands limited market viability

Many products and applications were only available in 4G/LTE

Perception of 5G as a moving target, immature market, has limited demand

Despite featuring in fewer deployments, private 5G networks offer substantial benefits over private 4G/LTE networks. 5G offers greater bandwidth and lower latency, allowing enterprises to introduce bandwidth-intensive and latency-sensitive technologies more easily. It also enables support for more users, devices, and access points simultaneously.

The use cases an enterprise needs to address should drive the potential adoption of a private 5G network. In general, use cases that need significant bandwidth or have extremely sensitive latency requirements will see benefits from 5G.

It’s important to note that enterprises should consider both present and future use cases in their planning.

Preparing in advance for future technologies and bandwidth that may need 5G capabilities can help enterprises avoid significant added costs down the road.

Ideal Use Cases that Take Advantage of the Benefits of 5G

What are the ideal use cases? While the number and variety of use cases will vary by industry sector, let’s look at three examples.

5G Shift in Private Networks: Enhancing Live Events

Inside of a football stadium, showing the 5G Shift in Private Networks: Enhancing Live EventsA stadium that hosts thousands of fans at a time wants to allow those fans to use their handheld devices to buy merchandise, livestream an event as it happens, or live-tweet or use social media during the event.

Enhancing the event with online services delivers direct value by allowing customers to purchase concessions and merchandise. And it can also generate free organic marketing via social media. But allowing thousands of devices to operate simultaneously requires enormous bandwidth capabilities that only private 5G networks can offer.

5G Shift in Private Networks: Delivering Location-Based Services

Similarly, a shopping mall may want to offer direct marketing to customers inside the mall by advertising stores and products available on premises. Many retail establishments also want to offer services to users, such as directions to shops or amenities like washrooms and parking lots.

These types of location-based services improve the user experience inside the mall and produce direct revenues for retailers. 5G’s substantial bandwidth allows them to support a large number of users at the same time. And its improved latency can be used to provide directions and product offerings in real time as users move through the mall.

5G Shift in Private Networks: Enabling AI and IoT

Factories are also ideal environments for private 5G deployments. With 5G, factory operations can be enhanced with autonomous, connected vehicles that leverage IoT and AI technologies. These vehicles can deliver substantial value by increasing operational efficiency and reducing personnel costs, among other benefits.

Private 5G networks provide the higher bandwidth and low latency needed to support the movement and delivery of the enormous quantities of data that is constantly generated by autonomous vehicles. High-latency networks reduce the value of having autonomous vehicles on the factory floor and can make those vehicles inefficient.

iBwave Delivers Needed Design Capabilities

None of these use cases will exist in sterile, fully controlled environments. Stadiums, malls, and factories are all highly dynamic environments with many ways for a signal to get lost, interrupted, or blocked. And 5G, like 4G/LTE, will have to co-exist with other technologies, such as Wi-Fi. So, integration and interaction with these technologies must be considered when enterprises are designing a 5G network.

5G networks are also more difficult to design than 4G/LTE networks, and more expensive. This means that mistakes are easier to make and more costly. Therefore, accurate network design and coverage prediction are crucial to ensuring that enterprises get the value they need from an investment in a 5G network.

iBwave Private Networks fully addresses all the design complexities and provides clear, easy-to-use software for survey and design. It enables network designers to easily and quickly design accurate networks, avoiding both under design and overdesign. Available as a 5G/LTE and Wi-Fi solution, iBwave Private Networks enables designers to easily:

Model venues in advanced 3D with AutoCAD import

Design from a database of vendor-modeled network components, including Small Cells, Aps, cables, controllers, routers, and more

Calibrate prediction with survey results

Run key project reports

iBwave Private Networks delivers the simplest and most reliable solution for planning, designing, and delivering high-performance private 5G networks. Advanced features such as the Fast Ray Tracing Prediction Engine, Prediction Calibration, Inclined Surface Modeling, and Attenuation by Frequency ensure the network you design and install functions exactly as intended. Plus, cloud connectivity and seamless integration with iBwave Mobile Survey ensures that iBwave can meet all your network needs, present and future.

For more information, take a look at the full product breakdown of iBwave Private Networks.

And for more insights into the growing demand for 5G networks, download our latest e-book: Top Trends in Private Networks for 2023.

How to Design Accurate 5G Networks at 3.5 GHz

Improved coverage and better use of available spectrum are two of the key advantages 5G offers. A properly planned and designed 5G network will support delivery of enhanced mobile broadband applications and experiences, provide more reliable connections for mission-critical communications, and enable the mass deployment of IoT devices.

To capitalize on the benefits 5G offers, the networks that provide coverage indoors must be designed accurately. But planning and designing effective 5G coverage for complex indoor spaces can be much more challenging than existing technologies, like 4G. With so many variables to consider, predicting network performance accurately is the key to deploying a high-quality 5G network end users can rely on.

So, before you tackle any 5G deployment at 3.5 GHz, take a hard look at the prediction software available to support your design process.

An Accurate Network Design Software Eliminates Risk

The accuracy of 5G coverage at 3.5 GHz in any venue is only going to be as good as the software that is used to predict that coverage. Without an accurate software, RF engineers run the risks of delivering a network design that will not perform as needed once it’s installed and activated, and either over-designing or under-designing the network to compensate for variations in the predicted coverage based on wide margins of error.

When a network simulation does not accurately predict how it will perform, once the network is installed, it can lead to very costly troubleshooting, downtime or re-design work. Under-designing the network can create blind spots and negate all the benefits 5G offers. Over-designing the network adds unnecessary equipment costs, complicates deployment and creates more coverage than is needed. Both scenarios can complicate network rollout and add additional costs to a deployment budget.

How can you maximize the accuracy with which you design 5G networks?

You will want to evaluate the features of the software and ensure they are focused on delivering accuracy. Consider:

  • 3D Modeling – If the building is not modeled accurately in the software, then the network simulation will not be accurate either. Ensure that the materials you are modeling with are accurate to the real building, that materials have attenuations set for the frequency you’re designing for, scales are properly set, and all walls and surfaces (including inclined) are included in your model.
  • Accurate Database of Components – The database you are using to model your building and network should contain accurately modeled materials and parts. Ensure the right materials are available and that the parts in the database are accurately modeled vendor parts – antennas, cables, small cells, and other hardware needed for a deployment.
  • Demonstrated accuracy based on stress tests – The software should have a proven history of delivering accurate network simulations and be backed by analytical data and case studies that demonstrate the ability of the software to deliver accurate predictions in a variety of venues, such as stadiums, malls, hotels, and more.
  • Coverage Compliance – The survey tool you use to validate the network should have built in, pre-configured metrics that engineers can use to determine whether design goals have been met, and which network operators can use to verify that the design provided meets all specified goals.

You should also consider different prediction methods and their suitability for particular venues, as not all prediction models have the same accuracy. We’re talking about COST 231, VPLE (Variable Path Loss Equation) and Ray Tracing. You can read more about each and their pros and cons in our dedicated blog.

But of course, the most important consideration is the algorithm the software’s prediction engine uses to generate an accurate prediction.

Accuracy in the prediction process should be based on both mean error and standard deviation.

Mean error is a measure of how much measured data deviates from prediction. If one has collected, say, 400 measured points in a venue, then calculates the difference between predicted and measured value, then sums up the difference in each point and then divides with the number of points, one gets the value for average mean error.

Because the difference in each point can be positive or negative, mean error is sometimes close to zero. But that does not mean that the prediction is very accurate, because the difference in one point could be +20 dB and -20 dB in another, and yet average mean error for those two points is zero.

That’s why it’s very important to also know what the standard deviation should be. This is a number that is always positive and points to how much variation of accuracy can be expected in the prediction.

For inbuilding deployments, the algorithm should be delivering prediction results with a standard deviation and absolute margin of error of 3-5 dB. Anything more than that can lead to an over-designed or under-designed network.

If you want to learn more, read about the 6 Ways iBwave Focuses on Prediction Accuracy.

Deployment Data Confirms Accuracy of Predicted Coverage

Ultimately, you won’t know how accurate the tool you use is until after you have deployed your network. Data provided by QMC Telecom earlier this year from a 5G trial network at the Bossa Nova Mall in Brazil demonstrates just how important accuracy is when planning in-building coverage.

As outlined in a recent iBwave white paper, the Bossa Nova Mall trial was designed to demonstrate the efficiency of 5G service at 3.5 GHz in indoor environments, so getting optimal coverage in place in high traffic areas was a priority.

After deployment, QMC Telecom compared the predicted and measured 5G coverage. The results show that the accuracy of the prediction tool QMC Telecom used enabled design and deployment of the optimal network configuration to support all trial coverage requirements.

iBwave Design Prediction Accuracy Confirmed at 3.5 GHz

The prediction tool QMC Telecom used was iBwave Design. As the Bossa Nova Mall example shows, iBwave Design provides the right combination of elements needed to streamline the design of all 5G indoor wireless networks, including deployments at 3.5 GHz.

To enable accurate planning and design, the software offers three prediction models — VPLE, COST 231, and Fast Ray Tracing — that provide different levels of accuracy for different environments. Most of our customers rely on the Fast Ray Tracing prediction algorithm, which is based on Ray Tracing and was developed by iBwave RF engineers in partnership with scholars and experts from the in-building industry.

Download the white paper to learn more about how iBwave Design prediction modeling enabled QMC Telecom to accurately design coverage for its 3.5 GHz trial deployment at the Bossa Nova Mall.

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