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Digital Object Identifier 10.1109/OJIM.2022.1234567
IEEE 802.11bf DMG Sensing: Enabling
High-Resolution mmWave Wi-Fi Sensing
Steve Blandino1,2, Tanguy Ropitault1,2, Claudio R. C. M. da Silva 3, Anirudha Sahoo4,
and Nada Golmie4
1Associate, National Institute of Standards and Technology (NIST), Gaithersburg, MD
2Prometheus Computing LLC, Cullowhee, NC
3Reality Labs, Meta Platforms, Redmond, WA and Sunnyvale, CA.
4National Institute of Standards and Technology (NIST), Gaithersburg, MD
(Invited Paper)
CORRESPONDING AUTHOR: Steve Blandino. Author (e-mail: steve.blandino@nist.gov).
ABSTRACT
IEEE 802.11bf amendment is defining the wireless Local Area Network (WLAN) sensing procedure,
which supports sensing in license-exempt frequency bands below 7 GHz, and the Directional Multi-
Gigabit (DMG) sensing procedure for license-exempt frequency bands above 45 GHz. In this paper, we
examine the use of Millimeter-Wave (mmWave) Wi-Fi to enable high-resolution sensing. We first provide
an introduction to the principle of sensing and the modifications defined by the IEEE 802.11bf amendment
to IEEE 802.11 to enable mmWave Wi-Fi sensing. We then present a new open-source framework that
we develop to enable the evaluation of the DMG sensing procedure accuracy. We finally quantify the
performance of the DMG sensing in terms of the velocity/angle estimate accuracy, and its overhead on the
communication link. Results show that the DMG sensing procedure defined in IEEE 802.11bf is flexible
enough to accommodate a wide range of sensing applications. For the bistatic scenario considered, the
velocity accuracy is in the interval 0.1 m/s to 0.4 m/s, while the angular accuracy is between 1 degree
and 8 degrees depending on the sensing parameters used. Ultimately, the overhead introduced by sensing
is limited with a sensing overhead below 5.5% of the system symbol rate.
I. INTRODUCTION
Future wireless networks are envisioned to augment com-
munication operations with sensing capabilities [1, 2]. In-
tegrating sensing and communication is attractive thanks
to its potential benefits, such as more efficient spectrum
and hardware utilization, optimized resource allocation and
the support of a number of sensing services, including
human activity and gesture recognition in smart homes.
In addition, in-vehicle sensing has recently gained traction
promising safety enhancements with applications such as
driver sleepiness detection and reckless driving recognition
in smart vehicles [3].
Wi-Fi networks, both in the licence-exempt frequency
bands below 7 GHz and in the Millimeter-Wave (mmWave)
above 45 GHz, are the perfect candidates to implement sens-
ing features using existing communications infrastructures
and enabling coordination between multiple devices. Sensing
through Wi-Fi networks aims at utilizing Wi-Fi signals to
detect and sense targets, such as people, animals, objects,
and/or locations of interest. While the feasibility of using
Wi-Fi to enable sensing has been demonstrated over the
past several years [4–9], the range of applications that is
currently supported is limited due to the lack of sensing-
specific features in the IEEE 802.11 standard, hindering the
usage of multiple 802.11 devices from different vendors
for sensing applications. For this reason, Task Group IEEE
802.11bf (TGbf) started in September 2020 the development
of an amendment to the IEEE 802.11 standard supporting
sensing [10]. The main contribution of the IEEE 802.11bf
amendment is the definition of the Wireless Local Area
Network (WLAN) sensing procedure, which supports sens-
ing in license-exempt frequency bands below 7 GHz (2.4
GHz, 5 GHz, and 6 GHz), and its mmWave (above 45GHz)
counterpart, the Directional Multi-Gigabit (DMG) sensing
procedure, which is the focus of this paper.
Despite conceptual similarities in their operating mode,
the definition of two distinct sensing procedures is necessary
because of unique propagation, bandwidth, and hardware
characteristics. For instance, to satisfy the link budget and
provide sufficient coverage, DMG and Enhanced Directional
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Blandino et al.: IEEE 802.11bf DMG Sensing: Enabling High-Resolution mmWave Wi-Fi Sensing
Multi-Gigabit (EDMG) devices use Phased Antenna Arrays
(PAAs). PAAs may synthesize pencilbeams – beams with a
beamwidth of just a few degrees – dictating the direction of
the propagation. The smaller the beamwidth, the easier to
distinguish two targets apart. Hence, mmWave Wi-Fi has a
superior spatial resolution compared to Wi-Fi, because of its
integration capability to equip large PAAs on a single chip
and also thanks to its large signal bandwidth. Thus, mmWave
Wi-Fi enables use-cases requiring high spatial resolution,
for instance, discriminating fingers in a gesture recognition
application or obtaining reliable heart rate sensing for in-
vehicle vital sign monitoring.
IEEE 802.11bf aims to reuse existing features of the
IEEE 802.11ad and the IEEE 802.11ay standards [11, 12],
which define the DMG and EDMG Physical Layers (PHYs),
respectively. For instance, to guarantee uniform coverage
overcoming the intrinsic directional properties of PAAs,
IEEE 802 standard defines Training (TRN) sequences and
the beam refinement protocol (BRP), enabling the estimation
of the channel in different directions and thus potentially
enabling the detection of targets in the environment. While
these mechanisms can be repurposed to some extent, some
modifications need to be introduced to facilitate a sensing
framework, for example, specifying that the channel sound-
ing aims at performing sensing measurements, extending the
Beam Refinement Protocol (BRP) for multiple users and
enabling the report of sensing measurements. An overview of
IEEE 802.11bf is given in [13], presenting simulation results
to quantify the impact of quantization on the quality of the
channel state information report in the sub 7 GHz bands,
however, to the best of our knowledge, our contribution
is the first one to not only present the main definition
and features of IEEE 802.11bf to enable high-resolution
mmWave WiFi sensing, but also providing simulation results
to quantify the sensing accuracy achievable with the DMG
and EDMG PHYs of IEEE 802.11bf and the overhead on
the data transmission. Moreover, we study the main trade-
offs between sensing overhead and sensing accuracy and the
flexibility provided by the DMG sensing procedure defined
in IEEE 802.11bf to solve these trade-offs.
The remainder of this article is organized as follows.
Section II introduces the principles of radar and directional
sensing. Section III describes the DMG sensing proce-
dure defined by IEEE 802.11bf to perform sensing in the
mmWave band. Section IV describes the link level simulation
platform developed to evaluate the DMG sensing. Section V
provides the performance evaluation results of the sensing
accuracy and sensing overhead as well as shedding light on
the accuracy versus overhead trade-off. Finally, Section VI
summarizes the contributions and the main findings of the
paper.
II. PRINCIPLE OF RADAR REMOTE SENSING
In this section, principles of radar and directional sensing
are reviewed [14–17] to motivate IEEE 802.11bf’s support
Tx Rx
Tx Bea
m
Rx Beam
Tg
FIGURE 1: Generic radar remote sensing geometry.
of sensing applications. Specifically, the fundamental radar
design parameters and measurements are described.
A generic system geometry is shown in Figure 1 and con-
siders transmitter (Tx) and receiver (Rx) spatially separated
in a bistatic configuration. The path of length Rconnecting
Tx and Rx is known as the bistatic baseline, and the angle
βformed by the Tx to target range R1and target to Rx
range R2is known as the bistatic angle. The basic function
of a radar is to measure the time delay of the transmit
signal within the main propagation beam of the Tx and
the main propagation beam of the Rx, i.e., the time for a
signal to travel a distance R1to reach a target, plus the
time for the scattered signal, generated from the target, to
travel a distance R2to reach the Rx. From the measured
delay, the bistatic range R1+R2is computed. Another
fundamental measurement that can be derived from the range
measurement is the target velocity. To estimate the velocity,
a radar system obtains consecutive measurements over a time
window to detect variation of the range. Moreover, the use
of pencil-beam antenna patterns enabled by PAAs for the
transmission and reception of signals enables the estimation
of the angular position of the target using the steering angles
of the Tx/Rx beams combination.
Range, angle, and velocity measurements resolution are
constrained by transceiver capabilities; the resolution of a
radar can be defined as its ability to separate different
targets in range, angle, or velocity. In the following, radar
measurements and relative resolutions are discussed.
A. RANGE
A fundamental metric of radar sensing systems is range
resolution, which is a measure of the ability of a system
to resolve multiple targets in proximity. For determining the
target range, it is possible to measure the time of flight of
the reflected signal traveling the distance R1+R2. The total
range R1+R2, defines an isorange ellipse on which the
target is located. The foci of the ellipse coincide with the
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positions of the Tx and the Rx. The bistatic range resolution
is defined as the separation, ∆R, between two isorange ellip-
soid that can be resolved by the radar. The range resolution
depends on the capability to discriminate delays, hence on
the bandwidth of the transmit signal, and upon the target
position. As illustrated in Fig. 1, the minimun separation
between two isorange ellipsoids ∆Rmin is obtained at the
extended baseline and the maximum separation ∆Rmax at
the bisector of the baseline. The bistatic range resolution,
∆R, measured along the bisector of the bistatic angle of the
inner ellipse, can be expressed as:
∆R=c
2Bcos(β/2) ,(1)
where cis the speed of light in vacuum and Bis the signal
bandwidth. The best range resolution can be achieved for
a monostatic configuration when the Tx and the Rx are
co-located, i.e., when β= 0. For a generic case, when
β= 0, the range resolution depends also upon the relative
position of the target to the Tx and Rx. For large values
of β, i.e., when the target is close to the baseline, the range
resolution degrades, until a complete loss of range resolution
for β= 180◦. Hence, depending on the sensing topology,
the range resolution may vary. Monostatic architectures are
suitable to support high-range resolution applications, while
bi-static architectures might achieve a coarser resolution due
to dependency to their geometry. The resolution achieved
with bi-static configurations can be improved by intelligently
combining measurements obtained with multiple and diverse
links, i.e., using multistatic architectures.
DMG/EDMG signals are defined with a 1.76 GHz band-
width, which can be potentially extended with channel
bonding. Assuming a 1.76 GHz signal bandwidth, the best
range resolution that can be achieved is ∆R= 0.17 cm,
making mmWave Wi-Fi a fundamental technology to enable
applications requiring high resolution and high accuracy
recognition.
B. ANGLE
Spatial accuracy can be further improved using directional
antennas, as targets can be solved also in azimuth and
elevation angle. By combining the azimuth and elevation
information at both Tx and Rx with the bistatic range,
the target position can be estimated by intersecting the
bistatic ellipse with the Tx and Rx main lobes’ directions.
The azimuth and the elevation angles of the target are
determined by the pointing angle of the antenna main beam,
when maximizing the power of the scattering signal of the
target. The width of the main beam determines the angular
resolution ∆θiof the antenna, which is usually defined as the
3 dB antenna beamwidth. ∆θ1and ∆θ2refer to the angular
resolution of the transmitter and receiver, respectively.
In a bistatic configuration, the minimum separation be-
tween two targets lying on the same isorange ellipse that
can be discriminated by the radar in the angular domain, i.e.,
cross-range resolution, is usually defined by the beam with
the narrower cross-range resolution. For example, assuming
the transmit beam to be too large to contribute to the
angular resolution as in Fig. 1, i.e., if 2R2·sin(∆θ2/2) ≥
2R1·sin(∆θ1/2), the cross-range resolution ∆CR can be
defined as:
∆CR =2R1·sin(∆θ1/2)
cos (β/2) .(2)
In Fig. 1, the intersection between the range resolution area
and the cross-range resolution defines the boundary of a
rectangular spatial resolution cell. The assumption that the
spatial resolution cell is defined by the narrower beam does
not always holds and more complex and irregular cells may
be created; a general exact expression does not exist but
specific antenna patterns and geometry have been studied
[18].
As for the range measurement, fixing the position of the
target and the antennas configuration, the best cross-range
resolution can be achieved for a monostatic configuration,
i.e., when β= 0. However, the cross-range resolution also
depends on the distance of the target from Tx and Rx,
since the resolution improves when the range R1or R2is
decreased. Considering a monostatic DMG STA equipped
with a uniform linear array of 32 antennas, the ∆θ∼3◦
beamwidth yields a cross-range resolution ∆CR = 0.03 m
when the target is 0.50 m away from the STA, and it
increases to ∆CR = 0.10 m when the target is 2 m away.
Hence, such a device would be able to identify gestures
and finger movements for ranges below 0.5m, more coarse
gestures, for instance head motion, for a range between 0.5 m
and 2 m or recognize body movements and activities for a
range longer than 2 m.
C. VELOCITY
Fig. 1 highlights the spatial resolution cell defined by the
range limited resolution and the angle limited resolution.
Two targets in the same cell cannot be resolvable in space,
however, they could be still discriminated against if their
radial velocity falls in two different velocity resolution bins.
Assuming that Tx and Rx are stationary, and the target
is moving with velocity v, the range rate can be obtained
deriving the sum range over time:
ˆv=d
dt(R1+R2) = 2|v|cosϕcosβ/2=2|vr|cosβ/2,(3)
where ϕis the angle formed between the target velocity
vector vand the bistatic bisector, and |vr|=|v|cosϕis the
radial velocity equal to the projection of the target velocity
vector on the bistatic bisector. It can be shown that the
bistatic velocity resolution, ∆V, measured along the bisector
of the bistatic angle of the inner ellipse, can be expressed
as:
∆V=λ
2Tccos(β/2) ,(4)
where Tcis the observation time, also known as Coherent
Processing Interval (CPI) and λthe wavelength. As for the
range resolution, the maximum velocity resolution can be
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Blandino et al.: IEEE 802.11bf DMG Sensing: Enabling High-Resolution mmWave Wi-Fi Sensing
achieved in monostatic configuration. The velocity resolution
degrades as the target approaches the baseline, degenerating
to a loss of resolution when the target is on the baseline.
D. DATA CUBE
Achieving a fine velocity resolution, which is key in ap-
plications such as gesture recognition and heart/breathing
rate estimation, requires a long observation interval Tc, as
described in (4). To improve velocity resolution in a pulse
radar, usually, several pulses are transmitted with the antenna
beams pointed in a fixed direction, as shown in Fig. 2a,
because the observation time of a single pulse might not be
long enough to resolve the motion. In a pulsed radar the
observation time is thus Tc=I·∆T,Ibeing the number
of pulses and ∆Tthe pulse repetition interval. The pulse
number is usually referred to as slow time domain. For each
pulse, a series of echoes corresponding to different range
bins are collected, in the fast time domain, as shown in Figs.
2c and 2d. The Tx and Rx antenna arrays are electrically
swept across different steering angles since the direction of
the target is not known, and for each beam combination
a sequence of pulses is sent. The response for each beam
configuration is stored in the beam dimension. Hence, the
radar return of each pulse is collected in a 3-dimensional
matrix, shown in Fig. 2b, including information of the range,
evolution over time and antenna beams.
Doppler processing operates on the data cube to obtain
information about the Doppler frequency shift ν, which is
related to velocity ˆvas ν= ˆv/λ; coherent integration of
the returns from many consecutive pulses can be performed
using a discrete Fourier Transform; the discrete Fourier
Transform compensates the Doppler phase shift introduced
by the target motion and coherently sums the multiple
measurements. Using multiple pulses improves the velocity
resolution and increases the Signal-to-Noise Ratio (SNR),
which benefits from the boost of the coherent integration
gain. At the end of the Doppler processing, a range-Doppler
(or velocity) map is obtained, as shown in Fig. 2f.
III. DMG SENSING PROCEDURE
IEEE 802.11bf [10] amendment introduces Medium Access
Control (MAC) and DMG/EDMG PHY modifications to
support mmWave Wi-Fi sensing (i.e., sensing in the bands
above 45 GHz). The DMG sensing procedure is in charge
of mmWave Wi-Fi sensing and enables Stations (STAs)1to:
•inform other STAs of their sensing capabilities;
•request and setup transmissions that allow for sensing
measurements to be performed;
•perform sensing measurements enabling Doppler pro-
cessing, i.e., allow to transmit consecutive “pulses” at a
given repetition interval. In 802.11 context, the “pulses”
are PHY Protocol Data Units (PPDUs);
1“A STA is any MAC/PHY entity providing the IEEE 802.11 MAC
services” [19], and includes both Access Points (APs) and non-AP STAs.
•exchange either raw or Doppler-processed sensing mea-
surement results;
•release the resources allocated for sensing.
A. DMG SENSING PROCEDURE ROLES AND
SUPPORTED SENSING
The DMG sensing procedure operates with two sets of roles.
First, the STA initiating the sensing (i.e., the STA that
supports the sensing application), is referred to as the sensing
initiator, while the STAs participating to the sensing and
responding to the sensing initiator are denoted as the sensing
responder(s). Then, the STA sending the PPDU used for
sensing is designated as a sensing transmitter while a STA
performing the sensing measurements using this PPDU is
called as a sensing receiver.
The DMG sensing procedure supports the following sens-
ing architectures: monostatic, bistatic, multistatic, monostatic
sensing with coordination, bistatic sensing with coordination,
and passive sensing. As shown in Fig. 3, these architectures
can be differentiated based on the roles of each STA, and
the number of devices used to obtain the sensing mea-
surements. Monostatic architectures involve a single STA,
which is both the sensing transmitter and sensing receiver
(Fig. 3a). When the measurements are made by a single
sensing receiver using signals transmitted by a single sensing
transmitter, which is not co-located with the sensing receiver,
the architecture is said to be bistatic (Fig. 3b). When the
sensing transmitter and more than one sensing receivers are
distinct STAs (e.g., one sensing transmitter STA and two
sensing receiver STAs), the architecture is said to be mul-
tistatic (Fig. 3c). Coordinated monostatic (Fig. 3d)/bistatic
(Fig. 3e) is an extension of monostatic/bistatic architecture
which allows coordinating more than one monostatic/bistatic
sensing, the coordination is performed by the AP. Finally,
IEEE 802.11bf defines passive sensing where the STAs
receive PPDUs transmitted by one or more STAs that are not
necessarily intended for DMG sensing, e.g., DMG Beacon
frames (Fig. 3f). For sensing types requiring more than one
sensing responder, i.e., multistatic, coordinated monostatic
or coordinated multistatic, the sensing initiator of a DMG
sensing procedure must be an AP STA. Each of the different
sensing types is addressed in [10] except monostatic. As
monostatic sensing involves only one STA, it does not
require any communication or coordination between multiple
STAs and therefore is not specifically addressed in IEEE
802.11bf besides providing some constraints on the PPDU
to use for monostatic sensing.
B. DMG SENSING PROCEDURES PHASES
As displayed in Fig. 4, the DMG sensing procedure is made
of one or more of the following phases:
•DMG sensing session setup.
•DMG measurement setup.
•DMG sensing burst.
•DMG sensing instance.
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(a) (b)
(c) (d)
(e)
7 8 9 10
Range (m)
-2
-1
0
1
2
Velocity (m/s)
(f)
FIGURE 2: Example of data cube measurement and Doppler Processing. (a) Example topology (b) Corresponding Data cube
with nfast time bins, Islow time pulses and kbeam configuration tested (c) Power Delay Profile for beam configuration
1 and first four slow time pulses (d) Power Delay Profile for beam configuration k and first four slow time pulses (e) DFT
along the slow time axis to obtain the range Doppler map for a single beam configuration (f) Example of Doppler range
map for a single beam configuration.
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Blandino et al.: IEEE 802.11bf DMG Sensing: Enabling High-Resolution mmWave Wi-Fi Sensing
(a) (b)
(c) (d)
(e) (f)
FIGURE 3: Supported DMG sensing types examples: (a)
Monostatic. (b) Bistatic. (c) Multistatic. (d) Coordinated
monostatic. (e) Coordinated bistatic. (f) Passive.
DMG Sensing Burst (schedule the DMG sensing Instances)
DMG Sensing Session Setup
DMG Sensing Measurement Setup
Exchange DMG sensing capabilities
DMG Sensing Measurement Setup Termination
DMG Sensing Session Termination
DMG Sensing Instance
Initiation Sounding
Explicit Implicit
DMG Sensing Procedure
Exchange and set operational parameters to use in
DMG sensing bursts and DMG sensing instances
Sensing measurements are performed and reporting is done if
needed
Sensing Measurements setup is terminated
Session is terminated
Reporting
Raw Processed
DMG Sensing Instance
DMG Sensing Instance
Sensing Initiator
Sensing Responder
FIGURE 4: Overview of the DMG sensing procedure
DMG
Sensing
Burst 1 ...
DMG
Sensing
Instance 1
DMG
Sensing
Instance 2
DMG
Sensing
Instance i
...
PPDU
1PPDU
2...
DMG
Sensing
Burst 2
DMG
Sensing
Burst n
Inter-burst
Interval
Intra-Burst
Interval
Intra-Burst
Interval
Inter-burst
Interval
...
DMG
Sensing
Instance 3
SIFS PPDU
n
Refresh Time
ΔT (PRI)
I
IEEE 802.11bf
Radar Sensing
Terminology
DMG
Sensing
Burst 3
Observation
Time (Tc)
FIGURE 5: Organization of DMG Burst and DMG sensing
instances
•DMG measurement setup termination.
•DMG sensing session termination.
1) DMG sensing session setup
Not all 802.11 STA will support sensing or all the sensing
types and roles. Thus, the DMG sensing session setup allows
a sensing initiator and a sensing responder to exchange
DMG sensing capabilities such as the sensing type supported
(e.g., bistatic, multistatic), the roles supported for a given
sensing type (e.g., a STA supports to be sensing receiver
for bistatic), or sensing reports type available (either raw
Channel State Information (CSI) measurements or sensing
processed results). The capabilities are exchanged using
procedures commonly used in the IEEE 802.11 standard such
as beaconing for AP and association for non-AP STA. The
DMG session setup helps a sensing initiator to identify the
potential STAs that can be used as sensing responder for a
given sensing application with given requirements.
2) DMG measurement setup
To configure the sensing parameters and STA roles for a
given sensing application, the DMG measurement setup is
used. It enables a sensing initiator and a sensing responder
to exchange and agree on Operational Parameters (OPs)
associated with DMG sensing bursts and DMG sensing
instances (defined below). The OPs can include the sensing
roles of the sensing initiator and the sensing responder(s),
the DMG sensing type, the DMG burst configuration, the
DMG measurement report types, and other parameters. The
measurement setup is established with the initiator sending
aDMG Sensing Measurement Setup Request frame to a re-
sponder, which replies with a DMG Measurement Response
frame either accepting, rejecting or proposing new OPs to
the sensing initiator. The sensing initiator repeats this process
as many times as sensing responders involved in the DMG
sensing procedure.
3) DMG sensing burst
IEEE 802.11bf organizes the DMG sensing measurements
through DMG sensing bursts and DMG sensing instances as
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L-STF L-CEF L-
Header EDMG-A
Header EDMG-
STF EDMG
-CEF Data Sync1Sync2SyncnSync
PAD TRNs
P TRN-
SF1
P TRN-
SF2
P TRN-
SFnTRN field 1TRN field 2TRN field n
TRN-Unit1TRN-Unit2TRN-UnitL
TRN-SF1TRN-SF2TRN-SFM
...
...
...
...
FIGURE 6: Multistatic PPDU structure for nsensing respon-
ders.
depicted in Fig. 5. The DMG sensing burst represents the
channel access time dedicated to sensing measurements for
a single Doppler processing, i.e., the measurements used to
obtain a data cube as introduced in D. Each DMG sensing
burst is made of one or more DMG sensing instances, which
is the effective period of time where the measurements take
place. Each DMG sensing instance includes one or more
PPDUs where the PPDU used depends on the sensing type.
The DMG sensing burst defines two scheduling parameters:
the intra-burst interval and the inter-burst interval. The
former defines the time between the beginning of two
consecutive DMG sensing instances belonging to the same
burst, while the latter is the time between the beginning
of two consecutive bursts (i.e., the refresh time between
two Doppler cubes if the application requires more than
one Doppler processing). As described in Section D, the
computation of the Doppler frequency shift requires to
collect Imeasurements every ∆Tover a given observation
time Tc. IEEE 802.11bf allows Doppler processing with
the intra-burst interval being ∆T, the DMG sensing burst
duration being the observation time Tcand the number of
DMG sensing instances in a DMG sensing burst to be I.
4) DMG sensing instance
Each DMG sensing instance belong to a single DMG sensing
burst and might possibly include the following phases:
initiation phase, sounding phase, and reporting phase, the
sounding phase being the only mandatory phase. The initia-
tion phase allows a sensing initiator to check if the sensing
responders participating in the DMG sensing procedure
are available and also to configure information about the
sounding (e.g., which sensing responder should perform the
sounding first for a coordinated bistatic sensing). The initia-
tion operates with the sensing initiator sending DMG Sensing
Request frame to each responder which replies with a DMG
Sensing Response frame. During the sounding phase, the
sensing measurements are performed using PPDUs. Three
different PPDU are used depending on the sensing type:
•DMG/EDMG BRP PPDU as defined in IEEE
802.11ad/ay amendment [11] [12] for bistatic sensing
and bistatic sensing with coordination. BRP PPDU
are traditionally used to perform transmit (BRP-TX),
receive (BRP-RX), or transmit/receive (BRP-RX/TX)
beamforming training. To do so, TRN sequences (con-
sisting of complementary Golay sequences) are ap-
pended to packets. The TRNs may be used to perform
a fast angular scan covering different directions.
•The newly introduced EDMG multistatic sensing PPDU
for multistatic sensing, extends the design of the BRP
PPDU. This PPDU, as opposed to the legacy BRP
PPDU, allows for multiple sensing receivers to perform
sensing measurements using a single PPDU. The struc-
ture of multistatic sensing PPDU is shown in Fig. 6.
The first change compared to an EDMG BRP PPDU
structure is the insertion of a Sync fields between the
data field and the TRN field for each responder STA.
The Sync field needs to be added as a multistatic PPDU
is sent to a specific STA and thus beamformed for
this STA. As a consequence, the other STAs might
not be able to receive the preamble, header, and data
part. Thus, each sync field is beamformed in the di-
rections specific to each STA, and allows each STA to
obtain accurate synchronization and to know where the
intended TRN field starts. The second change is the
presence of PTRN subfields per each responder STA,
allowing each STA to track the phase and frequency.
Finally, a Sync PAD subfield is introduced for legacy
backward compatibility. The presence of a TRN field
per user allows for each responder to perform sensing
measurements using the same PPDU.
•Monostatic PPDU for monostatic and monostatic with
coordination sensing. Any DMG PPDU may be used for
monostatic sensing but IEEE 802.11bf provides some
constraints regarding to the waveform used in the TRN
field portion of a PPDU used for monostatic sensing.
These constraints are added to guarantee backward
compatibility with legacy mmWave Wi-Fi devices and
include constraints about the waveform length, trans-
mission power, spectral density, and transmit mask.
Since passive sensing aims at performing sensing without
allocating dedicated sensing resources, DMG passive sensing
uses conventional PPDU such as the DMG beacon frames.
The reporting phase is in charge of exchanging the sensing
measurements from the sensing receiver(s) to the sensing
initiator. The reporting phase is mandatory only if the sensing
responder is in the sensing receiver role or in the sensing
transmitter and sensing receiver role. There are two types of
reporting:
•“raw” results, i.e., the CSI results using the channel
measurement feedback element. In this case, the sens-
ing initiator receiving the report(s) will be in charge
of the sensing processing. While the CSI allows for
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Blandino et al.: IEEE 802.11bf DMG Sensing: Enabling High-Resolution mmWave Wi-Fi Sensing
a precise characterization of the channel, the overhead
resulting in the CSI report is large and can be detrimen-
tal to data communication. Thus, IEEE 802.11bf allows
exchanging already processed sensing results.
•sensing processed results. The processing is done by
the sensing receivers. There are two types of processed
reports: DMG sensing image report and DMG sensing
targets report. The DMG sensing image report allows to
return Doppler processed results (range, velocity or/and
sensing transmitter/receiver angles) either in 2D, 3D, or
4D, the larger being the dimension, the larger being
the overhead of the report. Thus, the DMG sensing
image report, when configured to 4D, represents the
doppler processing of the data cube depicted in Fig. 2b
(fast time being the range, slow time being used for
the Doppler, and beam configuration corresponding to
the sensing transmitter/receiver angles) and a subset of
the data cube when lower dimensions (2D or 3D) are
configured. An example of 2D DMG sensing image
using range and Doppler is shown in Fig. 2f. The DMG
sensing targets report processes the results one step
further as it enables to return information per detected
target (e.g., range, angle and velocity of each target),
lowering the reporting overhead.
5) DMG measurement setup termination and DMG sensing
session termination
The DMG Sensing Measurement Setup Termination and
DMG Sensing Session Termination allows to release the
sensing resources and are yet to be defined by TGbf.
IV. OPEN-SOURCE MODULAR SIMULATION PLATFORM
This section presents a novel open-source modular simula-
tion platform, suited to simulate the DMG sensing procedure
defined in IEEE 802.11bf. As shown in Fig. 7, the simulation
platform integrates two modules, available as part of a
collection of open-source tools to analyze WLAN mmWave
network performance [20, 21].
The first module is the NIST Q-D channel realization
software, i.e., a channel simulation software for Integrated
Sensing And Communication (ISAC) applications [21, 22].
The second module is the NIST ISAC-PLM [23], which
models the end-to-end IEEE 802.11ay PHY processing [24],
including TRN-R and TRN-T fields enabling the evaluation
of the DMG sensing procedure defined in IEEE 802.11bf.
The modular approach is adopted to allow flexibility to
simulate and test a wide range of user requirements, as
each of the modules can be easily replaced or extended.
For instance, respecting the ISAC-PLM input interface, the
channel model realization can be replaced with any other
channel model implementation output or measurement cam-
paigns output. Conversely, the NIST Q-D channel realization
software can be used with any custom signal processing
implementations.
Transceiver model +
Sensing processing
Channel
multipath
components
NIST QD-Channel Realization Software
Environment
+ Scenario
Range, Angle,
Velocity estimation
accuracy
Ray
Tracing
Link Layer
Simulation
NIST ISAC-PLM
FIGURE 7: Overview of simulation platform.
(a) T-rays ray tracing including direct backscattering and
ghosts’ reflections
(b) Boulic
Model
FIGURE 8: NIST Q-D channel realization software simu-
lates multipath components generating from a human body
model.
A. NIST Q-D CHANNEL REALIZATION SOFTWARE
The geometry of the environment and the topology of the
scenario, e.g., position of the STAs and targets over time
are defined in the NIST Q-D channel realization software,
as shown in Fig. 8a, depicting a single human target moving
in an empty indoor environment. Using the ray tracing
methodology, the channel multipath components (MPCs) are
returned. The MPCs are a description of the amplitudes,
delays and angles of arrival (AOA) and departure (AOD) of
the multipath propagation channel. Some of the MPC models
the radio signals reaching the Rx after interacting with
the environment (target unrelated MPCs), others describe
the interaction of the signal with the target (target related
MPCs). The ray tracing of a moving scattering center enables
space-time correlation of the scattered signal generating
from the moving target. A complex target, i.e., a target
composed of multiple scattering points, such as a human
target can be modeled as a group of individual scattering
centers distributed over the 3-dimensional space. In the NIST
Q-D channel realization software, the target model used
is a boulic model [25], shown in Fig. 8b, which is a set
of parameterized trajectories to represent the position of a
human body in space. The model describes the motion of
17 joints (16 body segments) and each joint is considered as
a scattering center of a human target.
B. NIST ISAC-PLM
The NIST ISAC-PLM module simulates an end-to-end
EDMG PHY transmission. At the end of a DMG/EDMG
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Doppler
processing
Range-
Velocity-
angle
estimation
EDMG
Transmitter Sensing processor
Clutter
removal
Channel
Estimation
TRN
EDMG
Receiver
Channel multipath components
FIGURE 9: ISAC-PLM simulates the transmission of TRN
fields to estimate target range, angle and velocity.
(a) Example of 3D DMG Sensing
Image obtained using NIST-PLM
0.2 0.4 0.6 0.8 1 1.2
Time (s)
-2
-1
0
1
2
Velocity (m/s)
(b) Microdoppler spectrum.
FIGURE 10: Data cube processing in NIST-PLM.
PPDU or in an DMG/EDMG BRP packet, a sequence of
Golay codes can be sent in a TRN field to train the receiver
(TRN-R), the transmitter (TNR-T) or both (TRN-T/R). IEEE
802.11bf enables the possibility to re-use BRP frames for
sensing purposes as well introducing a dedicated multi-static
PPDU, also consisting of a sequence of TRN fields, as
presented in Section 4.
1) DMG PHY transmission
ISAC-PLM simulates the transmission of a TRN field to
evaluate the sensing performance when using directional
communications, as shown in Fig. 9. The TRN field can be
precoded using different Antenna Weight Vectors (AWVs),
such that the sensing information can be extracted from the
channel components that fall into the analog beam created
by the selected AWVs. From a sensing perspective, the
channel estimate can be seen as echoes from the targets and
the environment. The delays of the echoes from the target
MPCs are proportional to the bi-static distance. The channel
estimate from consecutive the TRN sequences is collected
in a 3-dimensional matrix including information about fast
time, slow time, and angular domains.
2) DMG sensing image
NIST ISAC-PLM construct a 3D DMG sensing image
consisting of range, velocity and AOA or AOD azimuth
and elevation, from which target parameters are derived.
To obtain the 3D DMG sensing image, a discrete Fourier
transform (DFT) is applied on the slow time dimension of
the radar data matrix for each beam configuration, to obtain
information about the Doppler frequency shift, which is
proportional to the velocity of moving targets. Null Doppler
values are considered as originating from static clutters,
hence they are filtered out.
The obtained DMG sensing image is subject to a de-noise
processing.
A threshold eliminating the noise floor and a low-pass
filter with a 2D Gaussian window are applied to each beam
configuration of the 2D range-Doppler image. After that, a
peak detection algorithm is performed by comparing each
pixel of the 2D DMG sensing range-Doppler image to its
neighbors. If the tested pixel has a higher value than the
surrounding pixels, the tested pixel is declared as a local
maximum. The local maxima are thus retained in the de-
noised DMG sensing image. An example of 3D DMG
sensing image (range, the velocity, and the AOD azimuth)
obtained with a DMG sensing burst, using NIST-PLM is
shown in Fig. 10a.
3) DMG sensing target parameter
The estimation of the target properties, such as range, ve-
locity and angle, is extracted from the DMG sensing image.
To obtain the velocity estimation, DMG sensing images are
summed over the range dimension and beam configuration,
obtaining the microdoppler spectrum, shown in Fig. 10b. The
Doppler shift estimate is the point with the highest intensity
in the microdoppler spectrum. Similarly, the estimation of
AOA or AOD relies on the sum of the DMG sensing image
over the Doppler and range domain, and the estimated angle
is the highest value in the obtained angular spectrum.
V. EDMG IEEE 802.11bf EVALUATION
In this section, we present simulation results in terms of sens-
ing accuracy and overhead introduced by the sensing proce-
dure on the communication link to evaluate the achievable
performance of the DMG sensing procedure defined by IEEE
802.11bf and its flexibility to accommodate different sensing
applications. The trade-off sensing accuracy-overhead is also
discussed.
A. SENSING SCENARIO
We study a human motion detection application using a
bistatic sensing architecture. The simulated environment is a
room, shown in Fig. 11; the bottom left corner of the room
is set as the coordinate origin. The sensing Tx is located at
(4, 6.5, 1.6) m above a cabinet and the sensing Rx, on the
TV screen, is located at (6.8, 3.5, 1.5) m. A human target is
following a linear trajectory parallel to the yaxis, completing
a 1.3 m walk in a simulation time of 1.28 s. During the
motion, the target creates a bistatic angle βin the interval
between 39.8◦and 42.5◦. The living room is furnished, thus
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FIGURE 11: Simulation environment and bistatic geometry.
some of the rays impinging on the target or generating from
the target might be blocked by the furniture.
We consider that the sensing initiator is the sensing Rx;
thus the Doppler processing is executed at the Rx and no
reporting phase is required. The sensing Tx uses a PAA with
isotropic radiators, while the receiver is an isotropic radiator;
in this configuration, the Tx sends BRP-TX PPDUs precoded
with different AWVs. To study the impact of the antenna
pattern, several rectangular PAA sizes are simulated. We
consider Av= 4 vertical antenna elements, and we provide
simulations for Ah= 4,8,16 horizontal antenna elements.
As the beamwidth of the radiation pattern decreases with
the number of antenna elements in the PAA, to measure
the channel uniformly in the full search space, the entries
NCB of the PAA codebook scales with the antenna elements.
We consider a codebook with a uniform sampling in the
angular domain in both azimuth and elevation. To cover
the full space, the codebook contains NCB =81, 153,
297 AWVs to test for 4×4,4×8and 4×16 antenna
elements respectively. The maximum number of possible
AWVs trained in the EDMG BRP-TX is given by (M·L)/N ,
where Mand Nare the number of TRN subfields indicated
in the EDMG TRN-Unit M field plus one and in the EDMG
TRN-Unit N field plus one within the EDMG-Header-A of
the packet, respectively. The TRN field is composed of a
variable number Lof TRN-Units, defined by the parameter
EDMG TRN Length within the EDMG-Header-A of the
packet. To precode the TRN field with all the entries of the
codebook, the BRP packet is designed as M= 16,N= 1,
and L= 6,10,19 for 4×4,4×8,4×16 respectively. The
maximum number of AWVs that can be trained with these
configurations are 96, 160 and 304, thus covering the entries
of the designed codebooks.
To study the impact of the DMG sensing burst Intra-Burst
Interval on sensing accuracy, several Intra-Burst Interval
configurations are simulated. To keep the velocity resolution
constant, the DMG sensing burst duration is fixed to Tc=
32 ms, while we provide simulations for I=4, 8, 16, 32, and
64 DMG sensing instances per DMG sensing burst. Hence,
the Intra-Burst Interval varies as ∆T=Tc/I =0.5, 1, 2, 4,
and 8 ms. We set the Inter-Burst Interval equal to the Intra-
Burst Interval. The Doppler FFT length is set to be twice
the number of sensing instances per DMG sensing burst.
The main design parameters are summarized in Table 1.
B. PERFORMANCE METRICS
The performance of the DMG sensing procedure is evaluated
in terms of accuracy and overhead.
1) Accuracy
In the following, we use the definition of accuracy as the
absolute error ϵi=|ˆxi−xi|, where ˆxiis the estimated
parameter, xiis the ground truth and iis the index of the
observation. Moreover, to summarize the accuracy over the
different observations, we use the definition of root mean
square error (RMSE) defined as: RMSE =q1/K PK
iϵ2
i.
2) Overhead
To evaluate the overhead of the sensing procedure on the
symbol rate fc= 1.76·109symbol per second (sps), we first
compute the length of the EDMG BRP PPDU aiming at
performing sensing measurements. The EDMG BRP PPDU
length is:
LBRP =LDMG +LEDMG+
T RNBL ·(P·L·(P+M)),(5)
where LDMG = 4352 symbols is the length of the legacy
part of the DMG BRP PPDU (L-STF, L-CEF, L-Header)
and LEDMG = 4608 symbols is the length of the EDMG
part of the EDMG BRP PPDU (EDMG-STF, EDMG-CEF).
T RNBL represents the length of the Golay sequence used
in the TRN subfields. In this work, we use T RNBL = 128.
Pis the value indicated by the EDMG TRN-Unit P field
in the EDMG-Header A of the packet and in this work, we
assume P= 2.
The sensing rate in sps is thus computed as:
fsens =LBRP
∆T.(6)
The antenna pattern and the codebook design influence fsens
with the length of the EDMG BRP-TX PPDU, since the
number of TRN-T subfields in the EDMG BRP PPDU
depends on the codebook size; the larger the codebook, the
larger is the number of TRN-T subfields required. The Intra-
Burst Interval determines how often the resources need to be
allocated to sensing tasks.
Finally, to quantify the impact on the symbol rate, the
overhead is computed by normalizing the sensing rate to the
symbol rate, and expressed as a percentage as:
OH =fsens/fc·100.(7)
C. EVALUATION OF SENSING ACCURACY
1) Impact of Intra-Burst Interval on sensing accuracy
Fig. 12 shows the impact of different Intra-Burst Interval
configurations on the velocity estimation. Figs. 12a-12c show
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TABLE 1: Parameters of simulated DMG IEEE 802.11bf
bistatic sensing architucture.
Parameter Value Unit
Signal bandwidth - B1.76e9 Hz
Bistatic angle β[39.77 - 42.47] deg
Range resolution ∆R0.09 m
Velocity resolution ∆V0.10 m/s
Target velocity - |v|0.88 m/s
DMG sensing burst duration - Tc32 ms
Packet type BRP-TX -
Golay Length - T RNBL 128 -
EDMG TRN-Unit P - P2 -
EDMG TRN-Unit M - M−115 -
EDMG TRN-Unit-N -N−10 -
Antenna dependent parameters
Vertical PAA size Av4 -
Horizontal PAA size Ah4 8 16 -
EDMG TRN Length - L6 10 19 -
Codebook Size - NCB 81 153 297 -
Max number of AWVs 96 160 304 -
Cross Range Resolution - ∆CR 2.9 1.5 0.75 m
Intra-Burst Interval dependent parameters
Intra-Burst Interval - ∆T0.5 1 2 4 8 ms
DMG Sensing Instances - I64 32 16 8 4 -
Doppler FFT size 128 64 32 16 8 -
0.2 0.6 1
Time (s)
-2
0
2
Velocity (m/s)
Ground Truth
Estimated
(a) ∆T= 0.5ms
0.2 0.6 1
Time (s)
-2
0
2
Velocity (m/s)
Ground Truth
Estimated
(b) ∆T= 1 ms
0.2 0.6 1
Time (s)
-2
0
2
Velocity (m/s)
Ground Truth
Estimated
(c) ∆T= 2 ms
0 0.1 0.2 0.3 0.4 0.5 0.6
Absolute velocity error (m/s)
0
0.2
0.4
0.6
0.8
1
P(x< )
8ms
4ms
2ms
1ms
0.5ms
(d) CDF of accuracy varying Intra-Burst Interval
FIGURE 12: Impact of Intra-Burst Interval on velocity
accuracy.
0.2 0.6 1
Time (s)
0
20
40
60
80
Azimuth (deg)
Ground Truth
Estimation
(a) Ah= 4
0.2 0.6 1
Time (s)
0
20
40
60
80
Azimuth (deg)
Ground Truth
Estimation
(b) Ah= 8
0.2 0.6 1
Time (s)
0
20
40
60
80
Azimuth (deg)
Ground Truth
Estimation
(c) Ah= 16
0 2 4 6 8 10 12
absolute AOA error (deg)
0
0.2
0.4
0.6
0.8
1
P(x< )
4x4
4x8
4x16
(d) CDF of absolute azimuth error varying PAA size.
FIGURE 13: Impact of antenna pattern on angular estima-
tion.
the microdoppler spectrum obtained with an Intra-Burst
Interval set to 0.5 ms, 1 ms, 2 ms, respectively. Increasing
the Intra-Burst Interval, the maximum velocity that can be
detected decreases, as shown by the vertical span of the
microdoppler spectrum. The configurations with a value of
intra-burst interval of 0.5 ms and 1 ms, can detect a maxi-
mum velocity of 2.2 m/s and 1.1 m/s respectively, providing
robustness to aliasing, as shown in Fig. 12a and Fig. 12b.
Instead, the configuration with a value of intra-burst interval
of 2 ms, shows some aliasing, since the maximum velocity
resolution is 0.6 m/s, while the speed of the target is around
0.88 m/s, as shown in Fig. 12c. Despite the aliasing, the
sensing processing is able to detect the target and estimate
its velocity. Increasing the Intra-Burst Interval even further is
not recommended for the considered scenario. Quantifying
the error of the radial velocity estimation, Fig. 12d shows
the cumulative density function of the velocity accuracy; a
median accuracy of 0.05 m/s for Intra-Burst Interval smaller
than 2 ms, while increasing the Intra-Burst Interval the
accuracy error escalates, and a median error of 0.3 m/s and
0.4 m/s is observed for 4 ms and 8 ms respectively.
2) Impact of antenna pattern on sensing accuracy
Fig. 13 shows the impact of different PAA configurations
on the AOA azimuth estimate. We omit the analysis of the
elevation estimate as the target motion considered does not
present any noticeable variation in the elevation plane. Figs.
13a-13c show the azimuth spectrum varying the horizontal
number of antennas as Ah= 4, 8 and 16. A larger beamwidth,
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4x4 4x8 4x16
Phased Antenna Array
0
20
40
60
80
100
Sensing Rate (Msps)
0
1.1
2.2
3.3
4.4
5.5
Sensing Overhead (%)
8ms
4ms
2ms
1ms
0.5ms
FIGURE 14: Sensing rate fsens (Msps) and sening overhead
OH (%) for different Intra-Burst Intervals and PAA size.
such as the 4 horizontal antenna case, yields a lower angular
resolution, a coarser azimuth spectrum, and a worse azimuth
estimation. Increasing the number of antennas, i.e., increas-
ing the angular resolution, the azimuth spectrum becomes
sharper, and the azimuth estimation improves. The absolute
azimuth error is quantified in Fig. 13d. A median error of
1◦, 3◦and 7◦is obtained for 4, 8 and 16 horizontal antennas
respectively.
D. EVALUATION OF SENSING OVERHEAD
In this section, we quantify the overhead introduced by the
sensing procedure and how it scales with the intra-burst
interval and with the length of the EDMG BRP PPDU.
Using the definition in (6) and in (7), Fig. 14 quantifies the
overhead in both absolute value, i.e., sensing rate in Msps,
and as a sensing overhead expressed as a fraction of the
symbol rate. As expected, the sensing rate increases linearly
with the Intra-Burst Interval, since halving the Intra-Burst
Interval doubles the overhead. The sensing rate also increases
linearly with the size of the PAA, however with a smaller rate
compared to the Intra-Burst Interval; for instance, increasing
the antenna size from 8 to 16, the sensing overhead increases
from 60 Msps to 90 Msps. This is explained by observing
that while the TRN-T subfields portion of the EDMG BRP
PPDU increases, the rest of the EDMG BRP PPDU remains
constant in length. Fig. 14 also shows the impact of dedicated
sensing packets on the communication rate. In the most
demanding case considered, i.e., using an antenna array of
4×16 antenna elements and an Intra-Burst Interval ∆T=
0.5 ms, the resources occupied by the sensing packets are
below 5.5 % of the symbol rate. The lowest overhead of
0.14 % is achieved with ∆T=8 ms and a 4x4 antenna array.
E. SENSING ACCURACY VS OVERHEAD TRADEOFFS
To analyze the trade-offs between sensing accuracy and
sensing resource requirements, we characterize the rela-
tionship between RMSE (velocity and azimuth AOA) and
sensing rate, displaying the possible outcomes combining
simulations with different Intra-Burst Intervals and different
PAA antenna sizes.
0 20 40 60 80 100
Sensing Rate (Msps)
0
0.1
0.2
0.3
0.4
RMSE Velocity (m/s)
8ms, 4x4
4ms, 4x4
2ms, 4x4
1ms, 4x4
0.5ms, 4x4
8ms, 4x8
4ms, 4x8
2ms, 4x8
1ms, 4x8
0.5ms, 4x8
8ms, 4x16
4ms, 4x16
2ms, 4x16
1ms, 4x16
0.5ms, 4x16
(a) Trade-off velocity accuracy-sensing rate
0 20 40 60 80 100
Sensing Rate (Msps)
0
2
4
6
8
RMSE AOA (deg)
8ms, 4x4
4ms, 4x4
2ms, 4x4
1ms, 4x4
0.5ms, 4x4
8ms, 4x8
4ms, 4x8
2ms, 4x8
1ms, 4x8
0.5ms, 4x8
8ms, 4x16
4ms, 4x16
2ms, 4x16
1ms, 4x16
0.5ms, 4x16
(b) Trade-off angular accuracy-sensing rate
FIGURE 15: Sensing overhead - sensing accuracy tradeoffs.
1) Trade-off velocity accuracy - sensing rate
Fig. 15a shows the velocity RMSE over the sensing rate.
The RMSE of the velocity estimation improves decreasing
the Intra-Burst Interval, while the antenna pattern does not
affect the trade-off. Hence, the trade-off velocity accuracy -
sensing rate optimality is achieved with the smaller antenna
pattern. Using a 4×4PAA, the sensing rate is minimized
using an Intra-Burst Interval ∆T= 8 ms, while the RMSE
is minimized using an Intra-Burst Interval ∆T= 2 ms. The
RMSE of the velocity estimation saturates when ∆T < 2ms.
Thus, configurations with large antenna arrays (4×8and 4×
16) and small Intra-Burst Interval (∆T < 2) are inefficient
to optimize the velocity estimation accuracy - sensing rate
tradeoff.
2) Trade-off angular accuracy - sensing rate
Fig. 15b shows the azimuth RMSE over the sensing rate. The
RMSE AOA estimation improves, increasing the number of
antennas, thanks to the smaller angular resolution. The Intra-
Burst Interval does not affect the trade-off since the angular
variation for a human walk is negligible when considering
Intra-Burst Intervals in the order of milliseconds. Hence,
optimality is achieved with the largest Intra-Burst Interval.
Using ∆T= 8 ms, the sensing rate is minimized using a 4x4
antenna pattern, while RMSE is minimized using the 4x16
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antenna array. The configurations with faster Intra-Burst
Interval are thus inefficient to optimize the angle estimation
accuracy vs sensing rate tradeoff.
VI. CONCLUSION
IEEE 802.11bf amendment is defining the WLAN sensing
procedure, which supports sensing in license-exempt fre-
quency bands below 7 GHz, and the DMG sensing procedure
for license- exempt frequency bands above 45GHz. The dis-
cussion presented in this paper provides a concise introduc-
tion to the principles of sensing and how these principles are
being integrated by the IEEE 802.11 MAC and DMG/EDMG
PHY. We also provide an open-source framework, following
the standard, to simulate its performance. In this paper we
quantify the performance of the EDMG sensing procedure
defined by IEEE 802.11bf in terms of sensing accuracy of
velocity and angle estimation, and in terms of the overhead
introduced by the sensing procedure. Based on an end-to-end
simulation, we find that the DMG sensing procedure defined
by IEEE 802.11bf provides a wide range of flexibility to
accommodate different sensing applications. The RMSE of
velocity can be adapted depending on the use case and we
report a velocity accuracy in the interval 0.1 m/s to 0.4 m/s,
in the scenario considered. Similarly, the RMSE of the angle
estimate can be adapted with the antenna array configuration
and we measure an error between 1 degree and 8 degrees, in
the scenario considered. The overhead introduced by sensing
is kept under control, as in the simulated scenarios the
overhead is always below 5.5% of the symbol rate of the
system. Hence, a flexible accuracy-overhead trade-off can
be solved based on the application requirements.
ACKNOWLEDGMENT
The authors would like to thank Assaf Kasher, Solomon
Trainin, and Alecsander Eitan of Qualcomm Inc for con-
structive discussions and guidance.
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VOLUME , 13
This article has been accepted for publication in IEEE Open Journal of Vehicular Technology. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/OJVT.2023.3237158
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
