An introduction to Wi-Fi Sensing and IEEE 802.11bf

Published on: Jan 27, 2023

Today, environment sensing is carried out using sensors such as radars and lidars. For example, mmWave radars are extensively used for various sensing activities, like people tracking, activity recognition, vital sign monitoring, health sensing, etc. However, this requires setting up additional hardware in the environment of interest, which might not be feasible.

What if I told you that these sensing activities can also be performed using the existing wireless infrastructure that is already available in the majority of homes today? In this blog, we are going to look into one of the next-generation applications of Wi-Fi technology – Wi-Fi Sensing. Although this blog will explain the basics, it would be really helpful if you have also read our previous blog “Radio Detection and Ranging (RADAR)”

Wi-Fi Sensing

Wireless sensing is realized by analyzing radio signals as they propagate through an environment and detecting the variations resulting from an event or activity of interest. As Wi-Fi becomes more and more available in public and private spaces (not just in the form of smartphones and routers, but also computers, smartwatches, sensors, etc.), we can leverage its ubiquitousness not just for communication purposes but also for sensing. In the case of Wi-Fi sensing (also known as WLAN sensing), the Wi-Fi signal characteristics such as the received signal strength indicator (RSSI) and channel state information (CSI) measurements are exploited to detect and track the obstacles affecting the channel. Note that CSI essentially describes the channel properties of the communication link between the transmitter and the receiver, taking into account the combined effect of scattering, fading, and power decay with the distance on the radio signal. Once this information is extracted, signal processing techniques are employed to determine the features like range, velocity, angle, etc. These features can then be used to train various models (machine learning or deep learning models) to identify and classify different applications. Fig. 1 presents an overview of the Wi-Fi Sensing system [1].

              Figure 1: Overview of Wi-Fi Sensing system [1]

The standard Wi-Fi sensing system comprises the following functional blocks:

  • Measurement Acquisition: This functional block corresponds to obtaining sensing measurements from 802.11-based communication packets. This is challenging as the current 802.11 standard doesn’t explicitly support sensing. Wi-Fi sensing applications make use of CSI between the access point (AP) and the station (STA), tracking the CSI over time/space to capture certain regularity that can be used to identify patterns. The sub-7 GHz (especially the 5 and 6 GHz) and over-45 GHz mmWave frequency bands used for next-generation WiFi offer wide bandwidth for ranging purposes. In the case of 60 GHz (or directional multi-gigabit (DMG) as it is popularly known), the wider channel bandwidth results in higher range and angular resolution. DMG sensing performs Doppler estimation while transmitting the DMG burst frames. This is done after the DMG beamforming training phase is completed between the AP and the STA. To dig deep into the technical aspects of the sensing procedures at both frequency bands, please refer to [2].
  • Measurement Processing: During this phase, the timing and phase offsets from the obtained measurement data are filtered out. Then, different signal processing algorithms are applied to further process the measurement data in order to obtain application-specific features for sensing algorithms.
  • Sensing Algorithms: Once we have the processed information from the measurement data, we can design algorithms for different applications. In the literature, most of the algorithms for complex applications are data-driven, though for simple detection-based applications, thresholding and signal processing schemes also suffice. 

Efforts toward standardization

In order to define the modifications to the existing Wi-Fi standards to enhance the sensing capabilities, the IEEE 802.11 Working Group (WG) defined a new task group (TG) called IEEE 802.11bf (TGbf). Formally, the TGbf defines Wi-Fi Sensing as the usage of received Wi-Fi signals from WLAN sensing-capable stations (STAs) to detect features like range, velocity, angle, etc. of targets (such as humans and objects) in an environment [1,2]. The main role of TGbf is to develop amendments that define modifications to the IEEE 802.11 standards (IEEE 802.11 ad/ay/n/ac/ax/be) at both the physical layer (PHY) and the medium access control (MAC) for enhanced sensing operation in the license-exempt bands of 1 GHz – 7 GHz as well as over 45 GHz (mmWave frequency bands) [1]. The standardization process will not just impose strict quality control on the technical features of sensing procedures, but also facilitate interoperability and compatibility with the legacy Wi-Fi standards. Note that the TGbf amendments make only MAC layer modifications for the sub-7 GHz, but both PHY and MAC layer modifications for 60 GHz bands. This only corresponds to the “Measurement Acquisition” phase. The “Measurement Processing” and “Sensing Algorithms” blocks used for Wi-Fi sensing applications are left at the discretion of the users who design the algorithms.

Figure 2: Timeline of the standardization activities of IEEE 802.11bf [2].

Figure 2 summarizes the timeline and progress of the IEEE 802.11bf standardization activities, as presented in [2]. The first draft of the amendment, i.e, Draft 0.1 was released in April 2022. There should be three other amendment drafts (Draft 1.0, 2.0, 3.0) over multiple ballots, scheduled to be available by September 2022, January 2023, and May 2023. During the ballots, IEEE 802.11 voting members can vote for or against the draft and add comments. The Standards Association (SA) ballot process will take place in September 2023, after ensuring that Draft 4.0 has reached enough maturity [1]. The IEEE 802.11 WG and Executive Committee should approve the IEEE 802.11bf standard by July 2024, and the actual deployment of the standard is expected by the end of 2024.

Use Cases

IEEE 802.11bf has defined and categorized a variety of use cases [3] for Wi-Fi sensing, the most significant ones being:

  • Room Sensing: Applications in this category include human presence and motion detection, people counting and tracking, object and obstacle detection, and intruder detection.
  • Health Care: This includes fall and abnormal position detection, heart rate monitoring, breathing rate monitoring, and sneeze sensing.
  • Gesture Recognition: Hand and finger gesture recognition, human activity recognition, gesture-based home appliance control.
  • In-car Sensing: Detection of humans in the car, driver sleepiness detection.

Figure 3 illustrates some of these applications.

      Figure 3: Some applications of Wi-Fi Sensing [2].


Although Wi-Fi sensing has been existing for quite some time, and IEEE 802.11bf standardization activities are in their prime, there is still a long way to go before it will actually be available on commercial off-the-shelf devices. As most next-generation devices will be equipped with both sub-7 and 60 GHz radios, multi-band information fusion could enhance the sensing capabilities. For example, Sub-7 GHz CSI could provide richer multipath information, while the RSSI from mmWave bands could provide highly directional information. There are a lot of open research opportunities and challenges that, if addressed, could contribute towards a successful and ubiquitous WiFi Sensing standard, thus revolutionizing the next-generation wireless systems.

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[1] F. Restuccia, “IEEE 802.11bf: Toward Ubiquitous Wi-Fi Sensing”, [Online] Available:

[2] R. Dui, H. Xie, M. Hu, et al  “An Overview on IEEE 802.11bf: WLAN Sensing”, [Online] Available:

[3] A. Kasher, A. Eitan, S. Trainin, Y. Sun, and R. Du, “WiFi sensing use cases,” IEEE 802.11-21/1712r2, Jan. 2021. [Online] Available:

About The Author

Anish Shastri is a Marie-Curie Early Stage Researcher for the EU Horizon 2020 MSCA-ETN MINTS at the University of Trento, Italy, where his Ph.D. research work focuses on the development of algorithms for device localization and location-aided network optimization for Indoor Millimeter Wave Networks.

He completed his Bachelor’s Degree in Engineering (B.Tech) in Electronics and Communication (ECE) from Maulana Azad National Institute of Technology – Bhopal (MANIT), India in 2016. During his undergraduate days, he had worked as an Intern in the Programme – “Air Defense (AD)” lab of Defense Research and Development Organization (DRDO), Government of India, in Hyderabad. After his graduation, he joined the MS by Research in Electronics and Communication Engineering Program (MS by Research in ECE) at the International Institute of Information Technology – Hyderabad (IIIT-H), India. His Master’s Thesis was on Algorithms, Implementation, and Proof-of-Concept demonstrations of various Data-Reduction techniques for IoT Networks. During his Masters, he was selected as a Visiting Research Student at the Department of Electronics and Nanoengineering at Aalto University, Finland under the CIMO Asia Programme-Education Cooperation along with CIMO Grant Scholarship. After completing his Master’s degree in 2019, he joined Nokia Bell Labs-Ireland as a Research Intern, where he worked on Implementation, Over-the-Air testing, and Validation of Coordinated Null Steering techniques for Next Generation Wireless Systems.

Anish’s research interests include signal processing for next-generation wireless communication systems and their applications. Other areas of interest include Machine Learning and PHY/MAC for Wireless LAN.