Author: Vladan Jevremovic

PhD, Senior Director of Research, iBwave Solutions Vladan Jevremovic has joined iBwave in 2009 as Director of Engineering Solutions and has been in the Telecommunication industry for more than 17 years. He is responsible for developing custom solutions as a part of professional services portfolio. He’s also responsible for ideation and requirements specification in the new product development life cycle, and works closely with the development team on new product implementation. Vladan received his Diploma Ing. from University of Belgrade in Serbia, and his Masters and PhD from University of Colorado at Boulder.

At a Glance: What Is New in Wi-Fi 7?

 Wi-Fi 6/6EWi-Fi 7
Operating bandsTri band: 2.4 GHz, 5 GHz, 6 GHzNo change
TechnologyUplink/Downlink OFDMANo change
MU-MIMOUplink/Downlink MU-MIMONo change
Modulation1024 QAM4096 QAM
Spatial Streams816
Bandwidth (MHz)20, 40, 80, 160 MHz20, 40, 80, 160, 320 MHz
Multi-Link OperationNoYes

Let’s break down each new feature.

Multi-link Operation

All previous Wi-Fi technologies relied on establishing a wireless connection over a single channel, be that 2.4, 5, or 6 GHz spectrum. Wi-Fi 7 allows a client to connect to all three bands simultaneously. This feature allows for duplicate transmission/reception over multiple bands/frequencies, increasing transmission/reception reliability.

Thanks to this feature, it is now easier to balance the load in the network across multiple bands. Suppose the radio conditions change, and a channel or most of the band experiences an increased ambient noise level. In that case, the client may automatically switch to a channel or band with more favorable radio conditions. Depending on the RF channel size the switch was made to, this may or may not affect the data transmission rate, as available channel sizes differ among the three spectrum bands.

Advanced Modulation

Advanced modulation choice determines spectral efficiency, which determines data rate. The higher the QAM number, the higher the spectral efficiency and the higher the data rate. Thus, it is beneficial to have as high QAM modulation as possible. The table below shows an overview of QAM modulations and their relationship to spectral efficiency, expressed in b/s/Hz:

Modulation Level (QAM)Bits/Symbol/Hz
4 (QPSK)2
83
164
325
646
1287
2568
5129
102410
204811
409612

Wi-Fi 7’s highest modulation (4096 QAM) has 12 b/s/Hz, while Wi-Fi 6E has only 10 b/s/Hz. This 20% increase in spectral efficiency translates to a 20% increase in maximum achievable data rate.

However, to successfully demodulate any signal at the receiver, a minimum signal-to-noise ratio (SNR) must be achieved. The higher the modulation level, the higher the minimum SNR. For 1024 QAM, it is widely accepted that the minimum SNR = 35 dB. To illustrate coverage calculation examples, we assume 4096 QAM to be SNR = 38 dB.

Let’s assume that both the 6E and 7 protocols are active in a 160 MHz channel. The thermal noise level in such a channel is -174 + 10*log10(160,000,000) = – 92 dBm. A typical noise figure at a Wi-Fi receiver is NF = 6 dB and has to be added to thermal noise. Thus, the noise level at the receiver is -86 dBm. If the minimum SNR for 1024 QAM is SNR = 35, the minimum signal level at the receiver is -86 + 35 = – 51 dBm. For 4096 QAM, we assume SNR = 38 dB, so the minimum signal level at the receiver is -86 + 38 = -48 dBm. From this example, we see that the receiver needs a higher input signal to receive the highest modulation. This means that the receiver must be closer to the transmitter to be able to decode 4096 QAM than 1024 QAM.

How far can a client be from an AP before the signal at the client is less than -48 dBm? It all depends on AP transmit power and antenna gain. There are online tools that can calculate path loss; one example is Free Space Path Loss Calculator.

If we assume transmit power = 0 dBi, transmit antenna gain = 3 dBi, frequency of operation = 5.7 GHz, and receive antenna = 0 dBi, then path loss is 48 dBm, and the distance between AP and client is 1.5 meters. This is the maximum distance at which a 4096 QAM signal can be demodulated. By comparison, a 1024 QAM can be demodulated at a distance of 2.1 meters.

Maximum Channel Bandwidth

Wi-Fi 7 doubles the Wi-Fi 6E channel width from 160 to 320 MHz. This doubles the maximum achievable data rate. However, this does not come without a penalty. The thermal noise level increases by 3 dB every time we double the channel bandwidth. Thus, the noise level for a 320 MHz channel is -83 dBm, and, using the example above, the minimum signal level at the receiver for a 4096 QAM demodulation is -45 dBm. Using a path loss calculator above, we see that the maximum distance between the AP and client at which 4096 QAM demodulation is possible in a 320 MHz channel is 1.05 meters.

Maximum Spatial Streams

The maximum number of spatial streams doubles in Wi-Fi 7, from 8 to 16. In theory, all 16 streams can be active at the same time, each serving a unique client. If the streams do not overlap, SNR at each client will be high, and the compounded data rate delivered to the clients will be double that of 8 streams. However, this may happen only in specific deployment scenarios.

One such scenario is mounting an omnidirectional AP in the middle of the cafeteria. When the cafeteria is full, and clients are equally spaced throughout, AP has a 360-degree view of the clients in range. In that case, it is possible to have all 16 spatial streams active, non-overlapping, and serving clients. Another deployment scenario that can fully take advantage of this feature is an AP in a stadium, a convention center, or an office boardroom. In a general case, the clients would not be equally spaced, and 16 streams would rarely transmit all at once. In general, the higher the number of advertised spatial streams, the lesser the chance that the highest number of streams can be seen in most deployment scenarios.

Tying It All Together

If we only look at the specifications on paper, we expect that doubling the channel size will double the maximum data rate per stream. We also expect another data rate doubling when all 16 streams are active. Then, we expect a modest 20% gain from using higher-order modulation to get a total of a 420% increase in the maximum achievable data rate. In reality, this astounding improvement can only be experienced if we happen to have clients standing in a circle directly below an AP, about a meter away.

In reality, most of the APs will be deployed in such a manner that clients will be positioned asymmetrically around it, and will be more than 1 meter away. Having said that, the effects of higher modulation and wider channels should be noticeable in home office/small office environments, but also in large public venues where transmit power and transmit antenna gain are larger than what was assumed in the example we have shown. We expect to see better reliability and latency at all venues and in all deployment scenarios, thanks to the multi-link features.

Check out our blog for more tips and topics to learn more about wireless networks and their planning!

Accurate Prediction Simplifies Private, In-Building 5G Network Deployments

Not too long ago, IT managers, RF engineers, system integrators, project managers, and OEMs had limited options when it came to deploying wireless networks in large warehouses and multi-floor buildings. The cellular bands were the exclusive domain of major carriers who held the licenses. To get cellular performance indoors, anyone wanting to deploy a private network had to work with individual carriers to extend macro networks to indoor spaces. In many facilities, this could be a complex and costly undertaking. As a result, the comparatively low cost and simplicity of Wi-Fi made it the go-to option for most in-building deployments where mobility rather than performance was the main requirement.

CBRS changed the game. Today, private 5G NR networks that deliver all the benefits of the most advanced cellular technologies can be deployed almost anywhere. Smaller facilities can leverage the reach, coverage, reliability, and performance of 5G in the 3.5 GHz spectrum to create seamless user experiences that go beyond the capabilities of Wi-Fi.

But, while choice is a wonderful thing, designing a private 3.5 GHz network that capitalizes on all the benefits 5G has to offer can be challenging. There’s always a risk of over-designing or under-designing the network, which can complicate deployments and add additional costs to limited budgets. The only way to effectively simplify network deployment and keep costs low is with accurate prediction of the coverage needed before the design process begins.

Inaccurate Designs Increase Cost and Reduce Performance

From a network service perspective, private 3.5 GHz networks provide several advantages over Wi-Fi indoors, including:

Increased reliability and security, which is needed to support performance-sensitive applications, such as on-site voice communications and a variety of IoT connections

Neutral host configuration capabilities, which can be leveraged to provide seamless user experiences for anyone entering the building

Greater reach and coverage compared to Wi-Fi at 5 GHz and 6 GHz, which means the same area in a building can be served with fewer base stations and access points

Potential for future network slicing, which is an exclusive 5G NR feature. A portion of the network can be dedicated to one functionality (IoT), while another portion can be dedicated to data streaming, and yet another to other functionality

To capitalize on these and other advantages a private 5G NR network offers, the network must be designed to achieve the ideal balance between cost and performance. This requires careful consideration of the same variables that impact Wi-Fi performance, such as the size of the space, the number of floors, the configuration of the coverage area, obstructions that could affect signal propagation, potential interference, dead zones, and more.

Inaccurate designs can negatively impact cost and performance. Under-designing the network can create blind spots and negate all the benefits 5G offers. Over-designing the network can complicate deployment and create more coverage and/or more interference at a higher cost compared to Wi-Fi. And the potential for error increases as the size and/or complexity of the venue increases.

Network design tools created specifically to enable the design of Wi-Fi networks simply aren’t equipped to provide the prediction accuracy needed. And design tools that offer optional 5G network modules may not provide the high degree of accuracy delivered by those specifically engineered for 5G network design.

Prediction Accuracy Enables Efficient Design and Deployment of 5G Networks

For anyone looking to achieve the right balance between cost and performance, the ideal prediction tool must be optimized for 5G networks and simple to use. Unlike carriers, IT managers, RF engineers, system integrators, project managers, and OEMs don’t need to delve deeply into RF engineering principles and processes to get their network up and running. They need features and functions that will enable them to quickly predict coverage and visualize the placement of small cells on one floor or multiple floors of a facility.

Of course, the tool must be proven to provide the prediction accuracy needed to enable the design of reliable, private 5G networks. A trial network deployed by QMC Telecom earlier this year at the Bossa Nova Mall in Brazil is a good example of how accurate prediction can be leveraged for 3.5 GHz deployments.

Designed to demonstrate the efficiency of 5G service at 3.5 GHz, the Bossa Nova Mall trial confirmed that all the benefits of 5G can be delivered in large, indoor, public spaces where multiple users and devices are vying for bandwidth and service. With a private 3.5 GHz network, Wi-Fi upgrades can be avoided, and the 5G performance needed to provide seamless user experiences indoors can be delivered easily.

The Bossa Nova Mall trial network also offered an opportunity to compare predicted and actual 5G coverage. As explained in a recent iBwave webinar, the results of the data analysis show that the accuracy of the prediction provided by iBwave Design enabled QMC Telecom to deploy a 3.5 GHZ network that met all coverage requirements.

More importantly, the analysis shows that accurate prediction modeling before design can be used to leverage the reach, coverage, reliability, and performance of 5G to go beyond the capabilities of any Wi-Fi network. And it confirms that accurate prediction can simplify and streamline the in-building network design process so that deployment is done right the first time.

iBwave Design Enables Accurate 3.5 GHz Deployments

The data from the Bossa Nova Mall trial shows that iBwave Design offers the prediction accuracy needed to simplify deployment of indoor 5G networks at 3.5 GHz. With its powerful prediction engine and advanced 3D modeling capabilities, iBwave Design goes beyond tools that have been adapted for 5G. It enables users to predict and visualize the placement of network components and cabling from floor-to-floor in any indoor venue, streamline deployment, and strike the right balance between cost and performance for any 5G indoor wireless network.

Watch the webinar to learn more about the data analysis that shows 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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Accurate Network Design Needed to Harness the Full Potential of Metamaterials

More coverage at less cost is the holy grail of in-building wireless network design. Whether you’re deploying an LTE network or a 5G network at 28 GHz or 3.5 GHz, the whole point of going through the site survey, prediction modeling, and design effort is to determine the ideal placement of antennas and access points to deliver the optimal coverage for an enclosed space. This overarching objective holds true for any in-building network design, whether it is for a large, multi-floor deployment in a downtown office building, a warehouse distribution center at the edge of town, or a multi-floor shopping mall.

The ideal design eliminates the risk of over-designing or under-designing the network, thereby enabling a more efficient deployment that provides the coverage needed. To date, the key variables that network designers have had to take into consideration to ensure antennas and access points provide that coverage have been the size of the space, the number of floors, the configuration of the coverage area, obstructions that could affect signal propagation, potential interference, and dead zones.

But ongoing research and development efforts with metamaterials and metasurfaces may soon throw more variables into the mix. As they move from research labs and proof of concept into widespread application, the importance of accurate prediction and design will become even more critical to providing the coverage needed to deliver high-quality, seamless user experiences cost-effectively in indoor spaces.

Engineered Materials Enable Signal Manipulation

Metamaterials have been a hot topic in microwave and RF circles for at least 20 years, primarily because of potential applications in wireless networks. These artificially engineered materials are built with microstructures known as “meta-atoms,” which are much smaller than the wavelength of an electromagnetic wave and have electromagnetic properties that are not found in naturally occurring materials.

Most metamaterial research is focused on creating Negative Index Materials (NIMs) that have a negative refraction index (NRI). These materials refract electromagnetic waves in a different direction compared to conventional materials with positive refractive properties. As a result, metamaterials with an NRI can be used to control and manipulate electromagnetic waves.

To date, metamaterials have been used in the design and construction of RF and microwave antennas to make them smaller and more powerful. But it’s their potential use as metasurfaces in construction materials for buildings that may soon affect how indoor wireless networks are designed.

More Potential Options for Signal Propagation

Research into the future applications of metamaterials points to significant potential in the creation of reconfigurable intelligent surfaces (RISs). An RIS is a thin surface composed of multiple small antennas that have been created with NIMs to receive and passively re-radiate an RF or microwave signal.

In its simplest form, an RIS can be a dynamic reflectarray built with multiple omnidirectional antennas. A more elaborate implementation would be to use an RIS as a dynamically tunable metasurface, which can not only scatter and phase-shift a signal but can also have a controllable reflection angle and even polarization manipulation abilities.

A typical use case of an RIS, where it receives a signal from the transmitter and re-radiates it focused on the receiver

With an RIS, wireless signals could be altered in ways not possible with traditional MIMO arrays. The RIS could be used to keep the wireless channel well-conditioned, which increases the achievable data rate. It could also be used to mitigate the effects of Doppler spread and multipath fading. And in the future, RISs could be applied to make the use of TeraHerz signals a reality.

In short, by leveraging the full potential of metamaterials, it may soon be possible to integrate RISs in the construction of walls and ceilings to improve network coverage in office buildings, warehouses, and even shopping malls in areas where today’s antennas aren’t effective. Strategic positioning of RISs can ensure optimal coverage in dead zones, around obstructions, and even around corners.

User A is far away from the AO and has low received signal strength. User B has high received power but low rank channel. The RISs can be optimized to help in both scenarios

New Solutions Also Create New Challenges

Of course, while more propagation options can help solve many of today’s challenges with in-building network design, they can also create new challenges.

As noted in our metamaterials white paper, strict modeling of reflective metasurfaces requires full wave analysis, which is costly in terms of CPU usage. But an alternative hybrid method can be used. This method is a combination of ray tracing applied everywhere but on RIS, and a full wave analysis applied on RIS. It requires full wave simulation to get the complex radar cross section (RCS) of the RIS. The complex RCS is used to represent the RIS as a secondary radiation source in the ray traces. The final signal (electric field) distribution in the network is a summation of two field components: one due to the presence of the RCS/RIS, and the another approximated by a ray tracing algorithm, which would exist if the RIS had not been implemented.

Accurate Prediction and Design Required

As the potential uses of RISs emerge, the ability of network designers to accurately predict and design a network before deployment will be more critical than ever. Full wave electromagnetic simulation will be needed to fully capture the interaction of an incoming wave with an RIS and compute the reflected fields both in the near and the far field of the surfaces.

However, ray tracing, an asymptotic method that is widely used for propagation modeling, seems to be the most efficient way for designers to simulate and model coverage for any space, in terms of CPU usage versus prediction accuracy.

iBwave provides the right combination of elements needed to streamline the design of all indoor wireless networks and is working to support modeling of networks that leverage the capabilities of RISs. To enable accurate planning and design, iBwave Design 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.

Read the metamaterials white paper to learn more about metamaterials, RISs, and how prediction modeling based on ray tracing can be used to streamline the design of today’s and tomorrow’s in-building networks.

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