The Wi-Fi access point (AP) has had a distinguished career performing a singular task: shuttling data between our devices and networks – quickly, accurately and reliably. That deceptively simple function transformed the Wi-Fi AP into a data network connecting our smartphones, televisions, appliances, sensors and other smart electronics to the cloud and each other.

Like much of today’s computing infrastructure, the advent of artificial intelligence is pushing Wi-Fi AP technology to expand its scope as residential gateways and enterprise appliances are called upon to do more than simply relay data.

Designers are pondering a new class of Wi-Fi APs equipped with neural processing units (NPUs) that execute AI inference locally. In this model, APs operate as small processing centers that allow connected devices to move compute-intensive workloads to the network edge instead of performing them on the client or sending them to a remote cloud server. Here, the AP begins to blur the line between networking and computing by functioning both as the pathway to AI and the AI coprocessor itself.

In this blog, we examine why AI compute is moving into the Wi-Fi network and what an AI-capable AP could mean for connected devices and edge computing.

The Benefits of Shared Access

Conceptually, the expanding role of the Wi-Fi AP reflects a broad trend toward greater local autonomy and less cloud dependency by shifting AI closer to the edge of the network.

Edge AI Architecture

By owning both the wireless connection and some of the AI execution, APs would evolve from “access points” to data “distribution hubs” imbued with contextual reasoning for deciding which workloads receive airtime, bandwidth and compute priority through traffic profiling and dynamic resource scheduling. The upside is less internet congestion and fewer routing interruptions by making signal latency more predictable and manageable.

The implications don’t end there. Manufacturers could design lighter weight endpoints by tapping AI-enhanced APs as a shared resource for executing inference, rather than packing dedicated AI processors into every watch, sensor and phone.

Bringing AI Closer to the User

The impetus for expanding the role and purpose of Wi-Fi access points is driven, in part, by the limitations of today’s AI architecture.

When a smartphone sends a request to an AI application, it can perform some processing locally or send the request through an AP to a cloud server. Cloud AI provides enormous computing resources, but the farther data travels, the greater the potential for latency and variability that are outside the user’s control.

The latency issue can be addressed by running more AI directly on client devices, but that creates other challenges. Smartphones, smart glasses and other small battery-powered devices have limited power, thermal and compute budgets. AI inference consumes battery power, generates heat and must be designed into already tight form factors.

AI-capable Wi-Fi APs present an attractive middle ground. APs are generally plugged into an external power source and have fewer space restrictions. This would allow inference to remain at the network edge without consuming precious client compute resources while also easing dependence on the backhaul connection between the AP and cloud.

Emerging applications such as augmented and virtual reality, smart glasses and home robotics could benefit from this model. The timing is important, too. Wi-Fi 7, emerging Wi-Fi 8 capabilities and the broad availability of NPUs are creating the technological foundation needed to make distributed AI architectures practical.

But putting AI compute into the access point is more than a design integration decision. It also changes what engineers need to know about Wi-Fi performance.

As connectivity and compute begin to converge at the network edge, familiar RF measurements take on a new context. Engineers must increasingly understand not only whether the wireless link performed as intended, but whether its performance enabled the AI application to deliver the expected outcome. Stay tuned for my next blog, where I will examine how that shift could change Wi-Fi testing – from evaluating graceful degradation and increasingly demanding uplink traffic to recreating multi-client AI workloads and connecting application behavior back to the physical layer.

What AI Edge Means for Wireless Devices

A few days ago, I was sitting in a far corner of my home, upstairs, trying to build a small tool of my own using Claude. Nothing fancy — just me, a laptop, and an AI model doing the heavy lifting. Because I live in a multi-level house, I’ve got a mesh Wi-Fi system to avoid dead zones. I should’ve been fine, right?

Except I wasn’t. The model kept telling me it was about to give me a fast response. And then… nothing. Minutes went by before my query even landed on the system, and minutes more before the response came back.

Same story a few days later, uploading a video to my YouTube channel. I was excited to get it up, and instead I sat there watching a progress bar crawl. Twice, in two completely different situations, the thing standing between me and a good experience wasn’t the AI. It was my Wi-Fi.

That’s the thing nobody really addresses when they talk about “AI at the Edge.” Everyone’s excited about the models — how big they are, how smart they are, how fast they can reason. But almost nobody’s talking about the fact that all that intelligence has to travel through a wireless link before it ever reaches you. And that link is, frankly, the weakest part of the chain.

So, What Does “AI Edge” Actually Mean?

Let’s back up, because “AI at the edge” sounds like one of those terms that gets thrown around a lot without anyone really explaining it. Here’s the simple version: it means AI processing that happens on devices close to you — your smartphone, a smart hub in your home, or your Wi-Fi access point — instead of way off in some data center.

AI in the network is a different beast: models and processing happening deep inside network infrastructure, in places you never see. That side has plenty of processing power, with massive models and huge datasets, but all that horsepower comes with a tradeoff: latency. The further your request travels, the longer you wait.

Edge AI flips that. It’s built for speed, low latency, and quick answers. But here’s the catch, and it’s the whole point of this article: edge AI is only as fast as the wireless device carrying it. You can run the most advanced large language model in the world on your network. Doesn’t matter. If the last few feet — or the last few walls — between the AI and your device are a choke point, you’re going to feel it. And you’re going to experience it as “AI is slow,” even though the AI did its job in milliseconds.

That’s exactly what happened to me upstairs. Mesh system and all, the wireless portion of my network was the bottleneck dictating how “fast” my AI felt. You can validate a model’s accuracy all day in a lab with a clean connection, but if you don’t test how the RF performance holds up as conditions vary — signal attenuation, degraded power levels, changing interference — you’re not testing the full picture of what the device can actually deliver.

My personal experience reinforces a larger point: wireless testing can’t be an afterthought in AI-edge deployment; it must be central to the conversation.

Enter Wi-Fi 8

This is exactly why the coming generation of wireless technologies — whether it’s Wi-Fi 8 in your home network, 5G on your phone, or 6G on the horizon — matters more now than ever. Past generations mostly chased peak throughput in the form of bigger numbers on a spec sheet. Wi-Fi 8 (the industry’s working name for IEEE 802.11bn) breaks that pattern. With an official designation of “ultra-high reliability,” or UHR, Wi-Fi 8 is built to support higher throughput in poor-signal conditions, lower worst-case latency, and fewer dropped connections. This is especially important as devices roam between access points (APs) via features like Multi-AP Coordination (MAPC), where nearby access points work together instead of competing for airtime.

The emphasis on UHR represents a larger and significant shift away from “how fast can Wi-Fi run under ideal conditions” and toward “how consistently fast is my Wi-Fi as attenuation, power, and interference levels shift.” That distinction matters enormously for AI at the edge.

AI-powered devices aren’t just streaming a video where a little buffering is annoying but survivable. These systems are often making real-time decisions while interacting with voice assistants, AR/VR devices, industrial sensors, and even smart cameras performing on-device inference. Any hiccup in the wireless link doesn’t just slow things down; it can break the entire interaction. Nobody wants to repeat themselves to a smart speaker or discover too late that their security camera is lagging behind reality.

Where Testing Moves the Needle

Performance isn’t just about the client device in your hand — it affects the access point, too. Both ends of the link have to hold up. And here’s the part that’s easy to miss: you don’t have much control over the link itself. You can’t fix the walls in someone’s house, the neighbor’s Wi-Fi network, or a crowded RF band. What you can control is how well your device’s Tx/Rx chain performs when it’s handed a bad link. If you’re not testing that, you’re relying on chance. Designing hardware for a target performance range is one thing. Proving the software actually drives that hardware to hit the target, consistently, is another.

That leads to the next consequential question, which is what does testing actually look like under these new conditions? Broadly, it splits into three categories:

Non-signaling testing measures RF performance in isolation, away from live communication, so problems get pinned to hardware. Considerations here include:

• Power level and power spectral density (PSD) validation for 6-GHz devices in a dRU configuration. This confirms that a device hits its specified power gain without exceeding regulatory limits, which impacts how much uplink range it can deliver.
• Spectrum mask testing, which catches energy leaking into adjacent channels, especially where tones are spread non-contiguously across a wider bandwidth as in case of dRU.
• Receiver sensitivity testing, which measures the weakest signal a device can still decode, for ELR-PPDU specifically. This is the number that proves the “long range” claim.

Mission-mode testing, by contrast, deliberately changes channel conditions while the device is running, to see how it adapts in real time. This includes:

• Per-stream rate-adaptation validation, which introduces path loss on a single MIMO link and confirms only that link’s MCS steps down.
• Checking that the rate-vs-range curve (MCS versus power) degrades smoothly as conditions worsen, rather than falling off a cliff at some threshold validating newer code rates introduced in Wi-Fi 8.
• Packet Error Rate vs Signal to Noise Ratio (PER-vs-SNR) sweeps confirm a feature like Long low density parity check (LLDPC) codewords genuinely lowers the error rate at poor SNR.

Co-existence testing puts the device in a shared, contested RF environment — alongside Bluetooth and neighboring Wi-Fi networks — to see how it holds up on signal power and adjacent-channel levels when it’s not the only radio in the room.

None of this is about recreating every real-world scenario. It’s about knowing, with precision and a great degree of certainty, where a device’s hardware and software start to fall short of spec — and before a user finds out first.

The Bottom Line

Here’s what I keep coming back to: we can build the smartest edge AI in the world, but if we don’t rigorously test the wireless network carrying it, we’re leaving performance — and user trust — on the table. My AI tool wasn’t slow because Claude was slow. My video wasn’t slow to upload because YouTube was slow. In both cases, it was the wireless hop that quietly decided how good my experience was going to be.

That makes the case for treating wireless test as a first-class citizen in AI edge deployment. And it supports the need for taking Wi-Fi, 5G and 6G seriously — not as a marketing spec bump. Wireless communications is the connective tissue that determines whether all that edge AI investment will actually pay off where it matters most: in someone’s hands, in their home, in the moment they need it to just work.

After decades chasing speed as the primary performance benchmark, wireless technology providers have entered a new era defined by broader connectivity, network efficiency and support for new devices classes. At LitePoint, we see the transition manifesting through two technologies introduced in 3GPP Release 17: non-terrestrial networks (NTN) and 5G reduced capability, also known as RedCap or NR-Light. Together, these advances represent a noteworthy shift in how wireless infrastructure and devices are designed and validated.

As a result, the conversation around wireless innovation has evolved significantly. I shared these observations during the Test and Measurement Forum hosted by RCR Wireless News in a session titled: “Device Performance for the Next Era of Networks: NTN, RedCap and Advanced RF Validation Considerations.”

Earlier generations of cellular technology focused largely on throughput and user data rates. The transition to 5G expanded the mission by introducing three differentiated service categories: enhanced mobile broadband (eMBB), ultra-reliable and low-latency communications (URLLC) and massive machine-type communications (mMTC). With the advent of 5G Advanced, the next wave of cellular technology is moving us even closer to universal and highly efficient device communications as we approach the commercial launch of 6G in the 2030 timeframe.

What Is Bumping Us Off the Speed Wagon?

We believe at least three macro trends are catalyzing momentum. Chief among them is the immense impact of IoT and the support required to deploy and manage tens of billions of connected devices. At the same time, the industry is eager to close a stubborn global coverage gap where mobile broadband access is either unavailable or economically impractical using conventional terrestrial infrastructure. These forces are triggering a third event by inspiring a new generation of connected devices for industrial automation, autonomous systems, AR and VR applications, maritime connectivity and remote asset monitoring. This next wave of IoT-enabled technology is already placing new demands on wireless networks that require more flexible and resilient connectivity models.

This is where NTN and RedCap begin to play a transformative role.

NTN transcends terrestrial cellular by tapping satellite networks. Depending on the deployment architecture, NTN can leverage low Earth orbit (LEO), medium Earth orbit (MEO) or geostationary (GEO) satellites to provide coverage to areas that are otherwise inaccessible. This creates new opportunities for global asset tracking, maritime logistics, remote industrial operations and emergency communications.

RedCap serves a very different segment of the market. Rather than pursuing maximum throughput, RedCap lowers complexity and power for IoT hardware that requires reliable, continuous communication without the cost and energy budgets of 5G broadband. Devices such as wearables, industrial sensors, smart meters and connected consumer electronics fall into this category.

Market projections underscore the significance of both technologies. Forecasts indicate RedCap device shipments could exceed one billion units by the early 2030s. NTN adoption is accelerating as mobile operators and satellite service providers expand partnerships for global connectivity services. These trends suggest that NTN and RedCap will become foundational elements of the wireless ecosystem.

With Convenience Comes Complexity

As exciting as these opportunities may be, they are also complex. NTN changes the RF design equation at the device level. Satellite communication operates across new frequency bands including L-band, S-band and even higher frequency Ku and Kaband spectrum. That demands RF front-end architectures with specialized filters, new power amplifier characteristics and more sophisticated antennas.

Doppler compensation becomes especially important for NTN implementations in low Earth orbit. Since LEO satellites move rapidly relative to the devices they serve, modems must continuously compensate for frequency shifts in the transmit path. Even small inaccuracies can cause uplink transmissions to miss the intended satellite frequency allocation. Timing synchronization also becomes more challenging because of commensurately larger propagation delays.

Signal path loss is another major consideration. Unlike terrestrial cellular, NTN devices must maintain reliable communication over extremely long distances between the Earth and orbiting satellites. This places greater emphasis on antenna efficiency, link budget management and front-end sensitivity optimization.

RedCap brings a separate set of challenges. Compared to traditional enhanced mobile broadband, RedCap implementations simplify bandwidth requirements, reduce antenna counts and optimize power consumption. These slimmer architectures are particularly attractive for IoT applications where battery life, device size and cost efficiency are often more important than peak throughput performance. However, RedCap devices still require rigorous RF validation to ensure stable operation under real-world conditions.

Validation Takes a Front Seat in Evolving Test Regimens 

As these technologies mature, validation strategies become increasingly important. At LitePoint, we see testing requirements evolving well beyond traditional conformance validation. Conductive RF testing is already essential for validating parameters such as output power, error vector magnitude, adjacent channel leakage ratio, harmonics and spurious emissions. NTN adds new layers of complexity that require more advanced validation methodologies.

Frequency error validation in NTN devices, for example, now involves evaluating dynamic Doppler compensation rather than simply verifying static carrier accuracy. Timing advance synchronization and link budget margin verification become critical components of overall system validation. On the receiver side, sensitivity measurements must account for reduced antenna diversity and changing propagation characteristics.

Validation Helps Reconcile the Lab to Fab Variance

Engineers today face a significant challenge when maintaining device performance between initial silicon validation and commercial deployment. Early-stage silicon often arrives with incomplete firmware, partially implemented AT command sets and immature calibration routines. NTN modem features such as Doppler pre-compensation are frequently among the last subsystems to stabilize because they depend on the successful integration of multiple hardware and software components.

This reality requires a far more aggressive and automated testing approach. Validation teams must carefully track firmware revisions, build retry logic into automated test sequences and derive early test limits directly from specifications rather than relying on mature golden reference devices that may not yet exist.

Commercial device validation introduces additional pressures tied to regulatory certification, carrier acceptance requirements and manufacturing throughput constraints. Moving from controlled conductive measurements into over-the-air testing environments adds correlation complexities that become especially pronounced for satellite-based communication systems.

New Performance Vectors Are Expanding the Test Envelope

As the industry adopts more densely integrated multi-mode devices that combine NTN, RedCap and future 6G capabilities, holistic validation strategies will become essential. Engineers can no longer treat individual wireless technologies as isolated subsystems. Instead, testing must evaluate how these technologies coexist within increasingly compact and highly integrated device architectures.

At LitePoint, we believe successful validation in this next era of wireless connectivity will depend on intelligent automation, scalable RF test architectures and system-level visibility throughout the design lifecycle. NTN and RedCap represent a broader transformation for an industry that has historically measured itself against speed and data throughput. The next decade will be defined by reach, reliability and the ability to connect people in ways that defy the practical limits of conventional wireless communication.

Wireless connectivity is what allows wearables to communicate with your phone, sync with the cloud, and even talk to other devices around you. It’s the invisible thread that ties the entire ecosystem together. Our video series, 3 for 3, provides 3 answers for 3 pressing questions about trends in wireless test. In our latest video, LitePoint’s Adam Smith discusses why wireless technology is essential for wearables, the challenges wearables pose for connectivity and AI, and efforts to improve battery life.

VIDEO LINK

For patients who depend on insulin pumps, remote continuous glucose monitors, wearable heart-rate patches, pacemakers and other wireless medical technologies, the battery is a precious energy resource. When it comes to healthcare outcomes, how long – and how well – a battery does its job before it must be charged or changed isn’t a matter of convenience, it can define clinical safety and quality of life.

Unlike consumer electronics, these devices operate in tightly constrained biological environments where every milliwatt counts. Batteries power sensors, compute, storage and radio communications while also pushing RF signals through dense human tissue – one of the more challenging mediums for wireless signal propagation.

As medical devices become more connected, manufacturers increasingly face a complex question: How to guarantee robust wireless performance without accelerating battery drain? LitePoint’s testing expertise helps medical device designers solve that problem to efficiently validate RF performance, extend device uptime and improve product reliability all while ensuring patients receive timely, accurate, actionable data.

Why Wireless Testing Is Critical for Battery-Powered Medical Devices

Whether a device uses a replaceable cell or a sealed rechargeable battery, wireless testing plays an essential role in ensuring the radio transmitter and receiver behave reliably. For battery-powered medical devices, inefficient testing can shorten device life.

For example, a continuous glucose monitor patch rated for 14 days of use might lose an entire day of uptime simply because its RF tests consumed too much battery during production. Faced with this trade-off, some manufacturers may opt to reduce their RF test coverage. While this may seem like a fair exchange, shipping devices with unverified connectivity performance can cause problems down the line.

The primary battery-saving contribution that test delivers is optimizing energy during the test process itself, ensuring devices leave the factory at maximum capacity and with verified performance. Rigorous testing also reduces:

• Device recalls by catching RF failures before shipment
• Warranty and service costs by limiting early-life field failures
• Healthcare risk by improving the reliability of continuous monitoring
• Yield fallout by accurately identifying borderline units

Without sufficient test data, companies may think they are saving battery capacity during manufacturing, when in fact they are compromising long-term device reliability and the patient experience.

What Makes Wireless Operation Uniquely Challenging in Biological Environments

Human tissue is a notoriously lossy environment for RF signals. The body absorbs and attenuates energy, increasing the amount of transmit power required to deliver data to an external receiver. This drives up energy consumption, especially for implantable devices like pacemakers or emerging ingestible sensors, where signals must travel through multiple tissue layers and fluid types.

Even for devices worn on the skin, performance varies based on placement, body orientation or whether the device is covered by clothing. These factors create propagation conditions far more complex than a smartphone or wearable used in open air.

Medical manufacturers often rely on specialized labs equipped with CTIA-style phantoms – human-body simulators filled with tissue-mimicking material – to quantify how much signal is lost through the body. While LitePoint does not conduct biological absorption testing directly, RF data gathered from LitePoint equipment complements phantom-based testing to give engineers a full picture of how signal strength, receiver sensitivity and antenna orientation perform in real-world conditions.

Test Solutions Help Optimize Connectivity and Energy Efficiency

Optimizing wireless efficiency in medical applications means tuning the radio for both performance and power. Device accuracy and battery life are tightly linked as missed data packets mean retransmissions, and retransmissions cost energy. But the longer a device stays in test mode, the more battery life it burns before ever reaching a patient.

LitePoint’s specializes in test time optimization using advanced techniques like proprietary Bluetooth over-the-air (OTA) test. This method measures radio transceiver performance using beacons without establishing a full Bluetooth connection, which allows manufacturers to capture critical RF data in far less time than a manual test using a standard Bluetooth adapter and software.

This preserves battery capacity and allows designers to test multiple devices in parallel, reducing manufacturing bottlenecks without compromising quality.

How Coexistence Testing Contributes to Better Battery Management

Many medical devices now incorporate multiple radios, for instance, Bluetooth LE for data transfers, Wi-Fi to sync data with the cloud and near-field communication (NFC) for device pairing and user authentication. While each radio may be optimized individually, coexistence issues can cause interference that forces retransmissions, increases latency and drains battery life.

The key is to determine where and when to run these tests as full coexistence testing is too power-intensive for many high-volume production lines. LitePoint helps manufacturers by moving testing earlier in the product development cycle or design verification environment, where engineers can identify interference patterns and tune channel plans or antenna layouts without draining battery life during manufacturing.

The Risks of Insufficient Wireless Testing in Medical Devices

The consequences of inadequate testing are more serious than a dropped Netflix video frame. At minimum, poor RF performance leads to inconsistent data transmission or premature battery drain. In the worst cases, devices risk missing early warning signs of a serious condition, such as a glucose spike or a heart arrhythmia.

There is also a significant brand-trust implication. Medical device companies spend years building credibility, and a single high-profile failure tied to missed wireless data can easily erode customer confidence.

Still, some manufacturers rely on shortcuts like “golden devices.” These prototype units are presumed to represent ideal RF performance, but unless they are repeatably calibrated, they’re subject to signal drift over time which can degrade accuracy. A dedicated tester eliminates that variability and ensures consistent measurement across every unit.

Likewise, radio duty-cycling, which determines how often the radio wakes to transmit or receive data, is primarily a design consideration. Test data can help refine these decisions by revealing signal margins, sensitivity thresholds or susceptibility to interference, while RF test helps designers understand how aggressively they can duty-cycle without data loss.

Connected Healthcare Is Reshaping Wireless Test

Medical device volumes can reach hundreds of thousands or even millions of units. Repeatability and speed are essential. LitePoint’s automation tools, such as the IQfact+™ platform and IQfactATM™ software for module-based designs, allow manufacturers to validate critical RF parameters quickly and consistently, even when they lack direct access to the underlying chipset. These tools help ensure every device shipped meets its RF performance targets, enabling reliable operation for months or years in the field.

Healthcare is becoming a high-connectivity device environment. Everything from blood pressure cuffs to ultrasound imaging have wireless – and cloud-connected – options. As adoption scales, the need for efficient, precise, low-impact RF testing becomes even more critical. LitePoint is continuing to innovate in areas such as OTA efficiency, beacon-based RF measurement and automation, all aimed at helping med-tech engineers extend battery life while ensuring the data their devices generate is trustworthy.

As one of this century’s seminal communications technologies, Wi-Fi has steadily improved its performance over the past 25 years primarily by focusing on one key metric: speed. Expanded frequency bands, wider channels, higher-order modulation, more spatial streams – these advances were all designed to push peak throughput ever higher. Wi-Fi 8 is a decisive break from that pattern.

This was the conclusion shared by wireless technology experts during a recent RCR Wireless News Wi-Fi Forum I joined to explore the future of the evolving communications protocol. The panel – Wi-Fi 8 on the horizon: Will it be the connectivity fabric for the AI era?, was moderated by Rosalind Craven, Principal Analyst with Telecoms Consultancy, STL Partners, and featured Andy Davidson, Senior Director of Technology and Planning at Qualcomm; Spirent’s Principal Product Manager, Janne Linkola; Marcus Brunner, ISG F5G Vice-Chair for the European Telecoms Standards Body, ETSI; and myself, Product Marketing Manager at LitePoint.

As the panelists emphasized, Wi-Fi 8 is not about chasing data rates, its goal is to ensure wireless connections are predictable, resilient and deliver a consistently positive user experience even in the most challenging environments.

From “How Fast?” to “How Reliable?”

The motivation driving Wi-Fi 8 reflects how wireless networks are actually employed. For home, enterprise and industrial users, peak speeds are bounded by how they hold up in the real world, where dozens – or even thousands – of devices compete for airtime and require equal weight be paid to throughput and latency.

This includes more robust handling of interference, improved coordination across access points and better consistency at the edge of the cellular network. These performance imperatives address the frustrations users encounter every day: dropped calls, frozen video and unpredictable roaming behavior.

The panel reached a consensus that Wi-Fi 8’s value is most impactful when applied to high-stress use cases. These include enterprise and campus networks in addition to densely populated public venues like airports, stadiums and convention centers. In these cases, reliable roaming, congestion management and improved coexistence are essential for consistent performance. Smart home devices, immersive XR, spatial computing and cloud gaming platforms are another target category where low latency defines the user experience, while industrial automation applications demand deterministic performance and reliability to manage communications links for robotics, monitoring and safety-critical systems.

Standardization Timing and Top Challenges

As for timing, the panel aligned on a familiar pattern. Wi-Fi 8 is still in development within IEEE 802.11bn, with formal ratification expected later in the decade. Early implementations based on draft versions of the standard are likely to appear before then, followed by broader commercial adoption as certification programs mature.

This staged rollout mirrors previous Wi-Fi generations, but with one important difference: expectations are being managed more carefully. Rather than promising dramatic speed jumps, the industry is positioning Wi-Fi 8 as a foundation for long-term performance stability, especially in dense, high-reliability deployments.

Ultimately, timing will be influence by a series of recurring challenges that surfaced throughout the discussion:

Reliability and Interference: As wireless environments grow more crowded, interference is no longer an edge case, it’s the norm. Wi-Fi 8 aims to improve robustness in the presence of both Wi-Fi and non-Wi-Fi interferers, reducing performance cliffs and improving fairness across devices.

Congestion and Density: From apartment buildings to stadiums to campus-wide deployments, usage density stresses today’s networks. Wi-Fi 8 introduces mechanisms to better coordinate transmissions across access points and clients, improving aggregate performance and reducing contention.

Latency and Jitter: For applications like real-time collaboration, immersive media and industrial control, latency consistency often matters more than peak throughput. The panel highlighted key enablers for time-sensitive applications, including minimizing latency spikes and improving determinism.

Interoperability: With more complex features comes a greater risk of inconsistent implementations. Ensuring that devices from different vendors behave predictably and work together is a major focus of the standard.

Why Test Still Matters – More Than Ever

The panel reiterated that Wi-Fi 8’s improved reliability places a burden on validation in the form of wireless signal and radio testing. This remains essential to ensure that devices behave correctly across a wide range of real-world conditions, including interference, mobility, congestion and coexistence scenarios. Interoperability testing becomes especially critical as features grow more complex and user expectations rise.

At LitePoint, we believe Wi-Fi 8 is reinforcing the importance of comprehensive test strategies that extend beyond basic throughput measurements. Validating reliability, latency behavior and performance consistency across diverse scenarios is what ultimately ensures that the promise of Wi-Fi 8 pays off with a consistently excellent user experience.

More Than “Just Another Wi-Fi”

The panel’s conclusion was clear: Wi-Fi 8 is not defined by a single breakthrough feature. Its significance lies in a philosophical shift from maximizing peak performance to delivering dependable, high-quality connectivity where it matters most.

In that sense, Wi-Fi 8 represents a maturation of the technology. It acknowledges that wireless is no longer a convenience layer, but critical infrastructure. By prioritizing reliability, interoperability and experience, Wi-Fi 8 moves the industry closer to a future where wireless performance isn’t just something users can measure – it delivers an experience they can trust.

Bluetooth® and ultra-wideband (UWB) are often discussed in isolation rather than as parts of a coordinated system. That framing no longer reflects how real systems are being designed or deployed. In practice, Bluetooth Low Energy (LE) and UWB are increasingly architected to work together, each addressing different technical objectives. Automotive digital keys are the clearest example today, but the same complementary pattern is emerging across other proximity-based applications.

At the same time, both technologies are evolving rapidly. Bluetooth is adding more sophisticated ranging capabilities, while UWB continues to advance accuracy, security and performance features. For manufacturers, suppliers and designers in the automotive and other markets, this convergence leads to a conclusion: wireless test methodologies must evolve from assessing individual radios to validating complete, multi-radio systems.

Why Bluetooth LE and UWB Coexist in Digital Keys

Modern digital key architectures grew out of early Bluetooth LE-only implementations that relied heavily on a Received Signal Strength Indicator (RSSI). RSSI is a relative measure of received signal strength that is used to infer proximity to the signal source. While effective for discovery and convenience, RSSI-based distance estimation is inherently vulnerable to relay and man-in-the-middle attacks, making it unsuitable as the sole mechanism for security-critical access decisions.

This limitation drove the industry toward UWB-based secure ranging, formalized through initiatives such as the Car Connectivity Consortium (CCC). Today’s digital key designs intentionally divide responsibilities:

• Bluetooth LE = Discovery and Intent. Vehicles identify authorized phones or key fobs using Bluetooth LE, then monitor approach behavior and user intent over a relatively long range with very low power consumption.
• UWB = Secure Localization. Once the user enters a defined proximity zone, UWB performs precise, secure distance measurements before authorizing unlock.

This handoff is foundational. Bluetooth LE acts as the always-on, low-power sentinel. UWB acts as the high-confidence decision engine.

When Accuracy and Security Matter, UWB Is Essential

UWB’s advantage lies in how it measures distance. Instead of inferring proximity from signal strength, UWB uses time-of-flight techniques that deliver centimeter-level accuracy under typical operating conditions. Security is reinforced at the physical layer through mechanisms such as Scrambled Timestamp Sequence (STS), which significantly raises the bar against relay and replay attacks.

Equally important, Bluetooth LE and UWB do not compete for spectrum. Bluetooth LE operates in the 2.4 GHz band, while UWB occupies much higher frequencies, typically in the 6- to 10-GHz range depending on region. This allows both radios to operate concurrently without RF interference.

Bluetooth Is Evolving and Strengthening Coexistence

Bluetooth LE itself is not standing still. Technologies such as Bluetooth Channel Sounding are improving ranging accuracy by combining phase-based ranging and round-trip time measurements.

These advances do not replace UWB; they make coexistence more efficient. Improved Bluetooth LE ranging enables better intent detection and helps systems decide when to wake UWB, reducing unnecessary power consumption while maintaining responsiveness. In effect, improvements in Bluetooth enhance the effectiveness of UWB-based systems.

Automotive As a Proof Point

Automotive remains the most visible example of this complementary design approach because the consequences of failure are so high. Unlocking must be seamless, secure and repeatable across devices, environments and regions.

Orange: UWB, Blue: Bluetooth

At the same time, UWB adoption is expanding beyond access control. Applications such as indoor navigation, asset tracking and tap-free interactions are gaining traction. In vehicles, UWB-based sensing use cases, including child presence detection, intrusion alerts, and gesture or kick sensing, are already appearing, often through proprietary implementations. Over time, many of these sensing applications are expected to converge toward standardized approaches, increasing the importance of adaptable test strategies.

The Real World Is Not a Clean RF Lab

Even well-designed systems can fail if they’re only validated under ideal conditions. In practice, RF performance is affected by multipath, orientation and human factors. A phone in a back pocket, for example, can experience significant signal absorption by the human body, altering RSSI readings and delaying discovery or intent detection.

This variability reinforces a critical lesson: passing standards-based RF tests is not the same as delivering a reliable user experience. Over-the-air (OTA) test and system-level validation are essential, particularly for proximity-based applications where timing and behavior matter as much as raw RF performance.

What Changes for Test When Bluetooth LE and UWB Form a System

When Bluetooth LE and UWB are combined, test requirements expand beyond single-radio validation. During design and validation, teams must verify Bluetooth LE discovery and intent detection under realistic attenuation and orientation, as well as testing UWB ranging accuracy, timing determinism and secure ranging behavior. They also need to confirm the correct Bluetooth LE-to-UWB handoff across defined proximity zones.

In manufacturing, production test must adapt accordingly. Bluetooth LE still requires RF parametric checks such as transmit power verification and RSSI calibration. UWB introduces additional requirements, including power calibration to meet regulatory limits and antenna delay compensation to enable accurate time-of-flight measurements. Timing synchronization and intent verification increasingly become part of end-of-line testing.

Beyond the factory, field testing and regression validation – often linked to OTA software updates – play a growing role. CCC certification processes already emphasize real-world scenario testing, where OEMs and device makers jointly validate lock and unlock behavior. These results feed regression testing to ensure future updates do not introduce new failure modes.

Implications for Wireless Test Platforms

The message across all stages is clear: test must evolve with system complexity. As Bluetooth LE and UWB continue to advance through improved ranging, higher data rates and extended range, test platforms must adapt without forcing redesigns at every technology transition.

For LitePoint, this means enabling customers to validate Bluetooth LE and UWB together, over the air, with the timing, phase, power and security measurements these systems demand. The goal is not simply to test radios, but to verify complete proximity systems – from discovery to secure action. UWB and Bluetooth are not rivals. They are complementary technologies whose combined strengths enable secure, intuitive user experiences. Automotive shows how this coexistence works today – and why modern wireless test must evolve alongside it.

As we plunge into the new year, LitePoint scouted three wireless trends in various stages of maturity: the rise of AI wearables, the awaited arrival of Wi-Fi 8 silicon, and early spadework required to seed 6G cellular networks. In their own way, each is enabling the future of AI communications by moving actionable intelligence closer to the user, shoring up wireless data security and privacy, and expanding global access to a new swath of frequency bands.

Here’s how we see these forces shaping up over the next 12 months, and why, for wireless design and test engineers, the coming year is more about preparation than promotion.

AI Wearables Trend from Geek to Chic

AI wearables are a relatively small but high-growth segment. Grand View Research found that the on-device AI category – where tasks are run locally without heavy cloud support – accounted for about 10 percent of the $84 billion wearables market in 2024, the last period for which full-year data is available. Look for acceleration this year as on-device AI rides a 27.8 percent CAGR wave through 2030 – twice that of the total wearables segment.

The growing interest in AI wearables isn’t due to any breakthrough in bleeding-edge wireless technology. Rather than demanding more gigabits per second, consumers want lower latency, hands-free content capture, intelligent personal assistance, detailed health insights and enhanced security.

These features define an expanding universe of AI wearables that includes smart watches, rings, fitness trackers and eyewear. In a sign that form is now driving function, AI devices not only need to operate for days on small batteries they have to fit comfortably on the face or body and be stylish enough to wear in public.

Where AI performance meets sophisticated design, expect wireless engineers to open new frontiers for the efficient integration of radios, sensors, processors and memory in millimeter-sized packages. For test, AI wearables create challenges both familiar and new:

• Highly integrated multi-radio design (Bluetooth, Wi-Fi, UWB, cellular) in tight spaces
• Aggressive power budgets that demand careful RF optimization
• On-body propagation effects that don’t exist in other electronic designs
• New durability concerns: AI-enabled garments must weather everyday use, including washing cycles, without RF signal degradation.

Ensuring reliability requires test methodologies that replicate true on-body conditions, validate coexistence under load and measure the performance impact of AI workloads on battery life and RF behavior. The rise of AI wearables isn’t just a new device trend; it’s a new paradigm for wireless validation.

Wi-Fi 8: The Quiet Enabler of Next-Gen AI

While AI wearables is a trend consumers will touch in 2026, the transition to Wi-Fi 8 has a more ephemeral quality. Throughout 2025, Wi-Fi 8 existed largely in chipset labs. In 2026, those early silicon samples will move into the hands of access point vendors, IoT designers and smartphone makers. Commercial products won’t reach shelves this year in any meaningful volume, but the design and development phase will be in full swing.

Wi-Fi 8 isn’t designed to be a dramatic technological leap. Instead, it focuses on the foundational requirements of rich AI experiences:

Reliable Access to Data: Whether you’re wearing smart glasses in an airport or managing industrial robots, AI experiences break down quickly when data transfers become unpredictable. Wi-Fi 8 targets greater reliability under congestion and interference – conditions increasingly common in RF-dense environments.

Lower Latency: Many emerging AI applications are latency-sensitive, from real-time translation to predictive navigation and contextual assistance. Even modest reductions in round-trip delays can make AI feel more immediate, personal and trustworthy.Security and Privacy: AI depends on deeply personal data: biometrics, voice patterns, behavior trails. Some processing can stay on-device, but cloud inference remains essential, meaning data must move securely between the device and network.

Robustness equates to signal integrity and resilience while reliability indicates the probability of successful transmission

Together, these pillars make Wi-Fi 8 an AI-enabling technology, even if the connection isn’t always palpable. For test teams, this translates into new performance criteria: measuring latency under load, validating advanced features, assessing coexistence in mixed-radio environments and stress-testing devices in conditions that mimic real-world Wi-Fi.

Companies like LitePoint that have worked closely with early Wi-Fi 8 chipset developers will be best positioned to help device makers navigate the transition. Much of 2026 will be spent translating new silicon capabilities into robust, production-ready products capable of supporting next-generation AI workloads.

6G in 2026: Pre-Spec, But Busy

If Wi-Fi 8 is closing in on its commercial debut, 6G is still playing the pre-spec long game, with most roadmaps pointing to 2030 for initial market deployments. But that doesn’t mean the industry is idle. Behind closed doors it’s grappling with two big challenges.

The first is reconciling the unrealized promise of 5G cellular. 5G was marketed as a transformative platform enabled by heterogeneous networks, network slicing and ultra-reliable low-latency communications. What materialized, mostly, was faster 4G. The majority of global networks remain non-standalone, anchored in 4G cores, while private networks exist primarily as niche deployments. And carriers have not yet realized significant new revenue streams from 5G. These lessons loom large for 6G.

The second reality stems from an emerging hypothesis that 6G will be driven less by speed and more by distributed intelligence. This includes AI embedded throughout the network, massive sensor fabrics and new architectural models such as AI-RAN. The industry is still in exploration mode, evaluating which of these use cases genuinely require new spectrum, new waveforms or new topologies.

6G Spectrum: The FR3 Frontier

Much of the early 6G buzz in 2026 centers on FR3 spectrum (~7-24 GHz), sometimes called the “middle child” between today’s sub-7 GHz cellular bands and millimeter-wave FR2. Below ~16 GHz, FR3 behaves like traditional sub-6 GHz and can use existing packaging and antenna techniques. Above that, it becomes more like FR2, requiring complex antenna arrays and more specialized integration.

But unlike FR2 during 5G development, FR3 is not a clean slate. It’s a patchwork of incumbents that includes satellite systems, weather radar and point-to-point radio links. The 2023 World Radiocommunication Conference deferred major decisions that the WRC-27 confab will have to make to determine how spectrum can be harmonized globally.

Source: https://arxiv.org/abs/2310.06425

In the absence of ratified standards, engineers need flexible test platforms capable of exploring candidate bands, characterizing new modulation techniques, pushing higher-order MIMO concepts and validating performance in contested, fragmented spectrum. The ability to measure what’s possible – before regulators and standards bodies decide what’s permissible – may be what keeps 6G momentum chugging along.

Portending a Year of Preparation

Across AI wearables, Wi-Fi 8 and early 6G work, a common narrative emerges: 2026 is not the year everything changes, but the year we prepare for everything that will.

AI wearables are redefining human-device interaction for the first time since the smartphone. Wi-Fi 8 development will give AI devices the reliability, latency and security they require. And 6G experimentation will lay the engineering and spectrum groundwork for the next era of network intelligence.

For wireless engineers and test teams, 2026 is a pivotal year. It’s when we refine the methods needed to validate devices worn on the body, characterize Wi-Fi 8 in chaotic real-world conditions and explore 6G candidate bands long before they become standardized. The work done now will determine how fast the industry can translate today’s AI ambitions into tomorrow’s reality.