Wi-Fi 7 adoption is forecast to expand rapidly, with raw speed taking a back seat to network reliability and low latency. At the same time, the latest generation of Wi-Fi requires a more sophisticated radio architecture to enable applications such as online gaming, video conferencing and over-the-top (OTT) media that streams internet content without cable or satellite connections.

Consumers may take the enhanced performance of Wi-Fi 7 for granted, but behind the scenes we know that these advances come at the expense of additional radio complexity. Put simply, the more features we add to the physical layer, the more intricate the RF design becomes to consistently deliver the expected performance across diverse deployment scenarios.

A good example is preamble puncturing, a technique that improves spectral efficiency by allowing the system to transmit data on clean portions of a wide channel while avoiding interfered subcarriers or Resource Units (RUs). It’s important to note that preamble puncturing involves specific puncturing patterns, and these different patterns can lead to different spectral mask requirements that the transmitter must meet. This adds a layer of complexity to testing. 

Separately, multi-link operation (MLO) allows devices to transmit and receive across multiple frequency bands simultaneously, for instance, using both 2.4 GHz and 6 GHz to achieve higher throughput or redundancy. The interplay of these features, particularly the various band combinations of MLO and the pattern-dependent spectral characteristics of preamble puncturing, necessitates rigorous analysis and validation of how RF performance impacts overall system performance.

The following use cases illustrate the need for an optimized test and measurement regimen for quick and cost-effective Wi-Fi 7 deployment.

Use Case I: Wi-Fi Device Performance Relies on RF Parametric Performance

For the end customer, there are three things that matter most: range, speed and reliability. These features hinge on the RF parametric, which is the backbone of superior Wi-Fi performance and includes Error Vector Magnitude (EVM) and the Modulation and Coding Scheme (MCS) index. 

Even a minor change in the RF parametric can significantly affect performance. Imagine that a Wi-Fi device is operating at the maximum MCS index of 13, which translates to a modulation scheme of 4096 QAM at 320MHz. Let’s say we have two spatial streams that theoretically will yield throughput of approximately 5Gb/s – 6Gb/s. Poor EVM performance could force the device to move to the next-lower MCS scheme, which is MCS 11 (1024 QAM). The penalty of carrying fewer bits per symbol could result in a 20 percent drop in throughput, which would compromise real-world device performance.

Another RF parametric, uplink OFDMA, enables multiple devices to simultaneously transmit traffic to the access point, which reduces latency by enhancing spectrum usage and network performance. That requires tight pre-coordination and pre-correction of three essential physical layer features – power, timing and frequency – between the access point and end devices that include smartphones, tablets and laptops. 

When several devices, or stations, connect to an access point, each at a separate distance, the station closest to the access point must balance its power or risk weakening the signals coming from the other stations. From a timing perspective, once a trigger frame is received by the station or client devices, they initiate an uplink OFDMA transmission. These transmissions must be sent within 400 nanoseconds of each other to ensure seamless operation. The third feature, frequency, requires each station to pre-compensate or correct for the carrier frequency-offset of the signal that they receive from the access point. If any residual CFO error after compensation is greater than 350 Hz, it will likely result in inter-carrier interference degrading the quality and success of UL-OFDMA transmission.

Poor performance among any of these physical layer parametrics could lead to a network collision and reduced efficiency. And aside from uplink OFDMA, Wi-Fi does not have a specific scheduling algorithm, which makes it crucial for high-density and multi-user environments to work in harmony.

Use Case II: Device Rate Vs. Range

Rate adaptation, or adaptive modulation, is a device behavior that changes according to signal strength and the range between the station and the access point. A device close to an access point transmits at the highest MCS rate of 13. If conditions change due to noise, or if the device moves away from the access point, then it will try to retransmit or step down to the lower MCS modulation scheme and attempt multiple packet retransmissions. Without rate adaptation, the resulting signal congestion can degrade system throughput, performance and reliability and needlessly drain the device battery.

There are many physical layer parametrics that can cause such behavior, including poor EVM and signal-to-noise ratio (SNR) performance. This requires evaluation and measurement at the physical layer to determine EVM performance for each transmission, transmission power, the correct MCS scheme and the distance and transmission timing intervals between the access point and the stations.

Analyzing Bluetooth Coexistence with LitePoint IQsniffer

It’s important to measure and analyze coexistence performance given that Wi-Fi and Bluetooth® share the 2.4GHz spectrum. Any interference caused by transmit-power leakage, co-channel or adjacent channel noise, or unpredictable frequency hopping patterns can result in packet collisions and data loss. 

This speaks to the need for a visual tool that identifies power transmission levels, EVM and what type of Bluetooth packet is in use, whether it’s classic Bluetooth, Bluetooth Low Energy or a proprietary Bluetooth protocol that’s impacting Wi-Fi device performance.

The solution is a radio traffic sniffer that can capture and analyze the physical layer and the MAC layer. The sniffer captures the real-world signal exchanges between devices and physical layer parametrics like power, EVM, frequency error and the MCS index to distinguish with nanosecond-level accuracy which device is transmitting the right information. This is especially useful for analyzing and validating uplink OFDMA to determine if the frame gap and timing are correct. 

Until they reach economies of scale, every new generation of technology starts out with higher costs. This is especially true for consumer wireless device manufacturers and chipset suppliers that have already made heavy capital expenditures in Wi-Fi deployments. Tools like the LitePoint IQsniffer provide an accessible, scalable and cost-effective means for analyzing both Wi-Fi and Bluetooth packets and capturing the communication between devices to better managing Wi-Fi performance, cost and complexity.

My presentation, “Translating RF Performance to Real-World Results in Wi-Fi 7” is available on-demand, as part of a Wi-Fi Forum hosted by RCR Wireless News.