(LABEL:)Eq.

# Performance Evaluation of IEEE 802.11bf Protocol in the sub-7 GHz Band

Anirudha Sahoo1, Tanguy Ropitault2,3, Steve Blandino2,3 and Nada Golmie1
1National Institute of Standards and Technology, Gaithersburg, Maryland, USA
2Associate, National Institute of Standards and Technology, Gaithersburg,
Maryland, USA
3Prometheus Computing LLC, Bethesda, Maryland, USA
Email: {anirud, tanguy.ropitault, steve.blandino<EMAIL_ADDRESS>
###### Abstract

Changes in Wi-Fi signal, using Wi-Fi sensing, have been used to detect
movements in the environment and have led to development of many related
applications. However, there has not been a standard way to do this until the
IEEE 802.11bf standard development activity was taken up recently by the IEEE.
Wi-Fi sensing is an overhead to data communication. While the IEEE 802.11bf
standard has been designed with careful attention to the overhead and its
impact on data communication, there has been no study done to quantify those.
Therefore, in this paper, we evaluate performance of IEEE 802.11bf protocol
with different system configurations corresponding to different sensing loads
and the impact of sensing on data communication in those configurations. We
outline some of the key findings from our simulation experiments which may be
useful in practical operating configurations of an IEEE 802.11bf network.

##  I Introduction

Using Wi-Fi sensing, changes in Wi-Fi radio channel can be used to detect
movements in the environment enabling a wide range of applications such as
presence of humans, localization, fall detection etc. [1]. Since Wi-Fi
networks are widely deployed, the above paradigm would make it possible to
make aforementioned diverse set of applications available to the users and
eliminate the need for different kind of sensors for different applications.
Although a lot of work has been reported in the literature about Wi-Fi based
sensing [1, 2, 3, 4, 5, 6, 7, 8], lack of standardization has limited the
proliferation of Wi-Fi sensing based applications. Therefore, the Task Group
IEEE 802.11bf (TGbf) started the development of an amendment to the IEEE
802.11 standard in September 2020 to standardize Wi-Fi based sensing [9] which
will be known as IEEE 802.11bf. The IEEE 802.11bf standard defines Wireless
Local Area Network (WLAN) sensing procedures, both in the sub-7 GHz [9, 10]
and above 45 GHz band [9, 11]††U.S. Government work, not subject to U.S.
Copyright..

Integrating sensing with data communication in Wi-Fi network is quite
attractive since it allows for more efficient use of spectrum and hardware.
However, for sensing, the system may need to allocate a part of its radio
resources to send dedicated sounding frames and other sensing related
information, reducing resources available for regular data communication.
Thus, sensing may become an overhead for data communication. Hence, TGbf has
given careful attention to the design of WLAN sensing procedure to limit the
sensing overhead and its impact on communication performance. But, to the best
of our knowledge, there is no performance study available to quantify the
sensing performance and the impact of sensing on communication. Therefore, in
this paper, we evaluate WLAN sensing performance and the impact of WLAN
sensing on communication using performance metrics defined by us.

In the IEEE 802.11bf sensing protocol for sub-7 GHz, the actual sensing
measurements are done during, what are called, sensing measurement exchanges
(SMEs) and those SMEs account for most of the sensing overhead. So, in this
performance study, we focus on the SME part of the protocol. To perform an
SME, the initiator of sensing, which could be the Access Point (AP) or a Wi-Fi
Station (STA), has to get access to the channel and obtain a Transmission
Opportunity (TxOP). It may use Enhanced Distributed Channel Access (EDCA) or
Point coordination function (PCF) Interframe Space (PIFS) to get a TxOP. The
IEEE 802.11bf protocol also defines a periodically occurring sensing window
within which the SMEs have to be performed. The duration and periods of
sensing windows are configurable. Our simulation based performance study
examines both the EDCA and PIFS based access methods at different sensing load
in the system when the AP is the initiator of sensing. The sensing load in the
system is varied by varying the number of sensing STAs, the number of sensing
applications, the number of transmit and receive antennae and the sensing
window duration. We present an extensive set of simulation results at
different system configurations corresponding to different sensing loads. We
then highlight some important and interesting findings from our simulation
results which, we believe would be useful in configuring IEEE 802.11bf systems
in the sub-7 GHz band.

The main contributions of this work are as follows.

  * •

To the best of our knowledge, this is the first work to present extensive
simulation of the IEEE 802.11bf protocol.

  * •

This work provides quantitative insights into performance of the IEEE 802.11bf
protocol in terms of defined performance metrics.

  * •

Our simulation exposes limitations of WLAN sensing using EDCA based access
when sensing reports need to be sent from sensing STAs to the AP.

  * •

The results presented in this work provide guidance and insights into
practical operating configurations of an IEEE 802.11bf network.

##  II Related Work

There has been a lot of research work on Wi-Fi reported in the literature. In
[3], the authors present a passive Wi-Fi radar system for human sensing by
exploiting high data rate OFDM signals and periodic Wi-Fi beacon signals.
Change in Received Signal Strength (RSS) in a commercial off-the-shelf (COTS)
Wi-Fi device held on a person’s chest is used to design a respiratory
monitoring system in [2]. Changes in Wi-Fi signal strength have been studied
to detect hand gestures around a user’s mobile device in [4]. In [6], the
authors have implemented an end-to-end system to monitor human respiratory
motion using Wi-FI Channel State Information (CSI). They propose a deep
learning based processing algorithm called BreatheSmart that anaylzes the
changes in amplitude and phase of CSI data to detect respiratory motion. In
[7], a four antenna passive bistatic indoor radar configuration is set up
using IEEE 802.11ax Wi-Fi system to track multi-target human based on range,
doppler and angle-of-arrival measurements. A prototype of Wi-Fi based passive
radar system for localization and tracking of moving targets using range,
doppler and direction of arrival is presented in [8]. The above mentioned
research works are focused on methodologies or algorithms for the concerned
applications, but do not deal with estimating the Wi-Fi sensing related
overhead of the system. A fairly comprehensive survey of Wi-Fi sensing with
CSI is presented in [1].

To standardize Wi-Fi sensing process, the TGbf is developing a standard which
will be known as IEEE 802.11bf [9]. This standard defines the mechanisms and
protocols to provide channel state information in the sub-7 GHz band and radar
based information (e.g., range, doppler, beam azimuth) above 45 GHz band.
Since Wi-Fi sensing protocol is an overhead to the Wi-Fi data communication,
it is important to study the performance of Wi-Fi sensing and its impact on
data communication in different configurations. To the best of our knowledge,
there is no such study available in the literature. This paper is the first to
report performance analysis of IEEE 802.11bf protocol in various
configurations.

##  III Overview of IEEE 802.11bf Sensing Procedure

An IEEE 802.11bf capable STA and AP exchange their sensing capabilities during
the association process. WLAN sensing in the sub-7 GHz band, referred to as
Sensing Procedure, starts out with the establishment of a sensing measurement
session between a sensing initiator and a sensing responder at which time the
operating parameters of the session are determined. Examples of operating
parameters include bandwidth, role of the STAs (sensing transmitter or sensing
receiver), timer values etc. The actual sensing measurements are performed in
SMEs. SMEs can be trigger based (TB) or non-trigger based (NTB). Since TB SME
is envisioned to be the most common deployment scenario, in this study, we
focus on TB SME. In a TB SME an AP is the sensing initiator and one or more
non-AP STAs are the sensing responders. A TB SME can have up to four phases as
shown in Fig. 1. In the polling phase, an AP (the sensing initiator) sends a
Sensing Polling Trigger frame to the sensing responder STAs to participate in
the SME during a sensing availability window (SAW). A SAW has two parameters:
SAW duration and SAW period. SAW durations occur periodically and the period
is determined by SAW period. An AP and the STAs may participate in SMEs only
in a SAW duration. In the Null Data Packet Announcement (NDPA) Sounding phase
the AP is the sensing transmitter and one or more STAs are the sensing
receivers. The AP sends a NDPA frame followed by a Null Data Packet (NDP)
frame to the receiver STAs. The STAs measure the channel state using the
received NDP frame. In the Trigger Frame (TF) Sounding phase, the AP acts as
the sensing receiver and the STAs as sensing transmitters. The AP sends a TF
to the sensing receiver STAs, which then send NDP frames (which are
multiplexed in the spatial domain) to the AP. The AP measures the channel
state using the received NDP frames. Reporting phase is present, if sensing
report (mainly consisting of CSI) is required to be sent from the STAs to the
AP. Orthogonal Frequency Division Multiple Access (OFDMA) mechanism is used
for reporting, for which the AP allocates Resource Units (RUs) to the STAs.
Only NDPA sounding phase may necessitate a Reporting phase, if reporting was
enabled as part of operational parameters during sensing measurement session
set up. For more details on TB SME, please refer to [9].

Figure 1: Different phases in a TB Sensing Measurement Exchange

An AP starts an SME by obtaining a TxOP after a SAW period starts. It may
obtain the TxOP by EDCA or PIFS mechanism. We refer to them as EDCA access and
PIFS access respectively. If it uses EDCA access, then due to contention, the
actual SAW duration available for SME sometimes may be shorter than the
configured SAW duration. But when PIFS access is used, AP gets priority access
to the channel and hence, gets almost the entire SAW duration for sensing.

###  III-A Overhead Calculation

The Sensing Procedure is an overhead for data communication. Majority of the
overhead is incurred in the SME part of the protocol. So, in this study, we
concentrate on the SME of the protocol. In an SME, the NDPA sounding phase and
Reporting phase account for most of the overhead. Hence, our overhead
calculation involves only those two phases. Note that in an NDPA sounding
phase, the AP acts as a sensing transmitter and one or more STAs act as
sensing receivers. For the NDPA sounding phase, we take the number of bytes in
NDPA and NDP frame structure as overhead [9]. The NDPA frame structure is
presented in Fig. 9.58 in [12] and the STA Info field format used in NDPA
frame is shown in Fig. 9-61da in [13]. The NDP format shown in Fig. 27.46a in
[13] is used for computation of NDP overhead. For the reporting overhead, we
use the CSI size computation used in Equation (9-5f) in [9]:

$\begin{split}CSI\,size=&\lceil 1.5\times N_{tx}\times
N_{rx}\rceil+\frac{N_{tx}\times N_{rx}\times N_{b}\times N_{sc}}{4}\\\
&+2\times N_{rx},\end{split}$ (1)

where $N_{tx}$ is the number of transmit antennas, $N_{rx}$ is the number of
receive antennas, $N_{b}$ is the number of bits used for quantization of each
CSI value, $N_{sc}$ is the number of subcarriers reported in CSI. The bytes
transmitted as part of NDPA, NDP, and reporting frame will be referred to by a
general term called sensing information bytes throughout this paper.

##  IV Simulation Experiments

###  IV-A Simulation Setup

In our simulation setup, in terms of network topology, we assume that there is
one AP and a variable number of STAs associated with the AP. Our simulation
assumes that all messages are received correctly by the receiver, i.e., there
is no message error due to interference.

We assume that each sensing application runs on every STA in the network and
that the AP requires CSI report from every STA in the network for a given
sensing application. Due to resource limitation, if a complete report cannot
be sent from a STA, the STA still sends a partial report. Although in
practice, this will not happen, we resort to this method to highlight the
sensing overhead and the missed sensing that such cases lead to. When there is
no sensing activity in the network, the STAs send data traffic using EDCA with
full bandwidth. We assume that each STA always has at least a TxOP worth of
data to be sent. The TxOP duration was set to its maximum value of 5.484 ms
[14]. The AP only participates in sensing and does not send any data traffic.

Performance evaluation of systems at high load is usually more interesting.
Since the SME part of the Sensing Procedure incurs most of the overhead, it
leads to high sensing load. Hence, it is the focus of our simulation. As
mentioned in Section III-A, we compute overhead based on the NDPA sounding and
Reporting phase of an SME. During Reporting phase, the AP allocates one RU (RU
sizes given in Table II) to each STA which is used by each STA to send sensing
reports using OFDMA. We have implemented our simulation code by incorporating
IEEE 802.11bf features into the software available at [15], which was used in
the simulation study reported in [14].

###  IV-B Performance Metrics

We define the following performance metrics in our evaluation.

  * •

Percentage Sensing Overhead (PSO): It is the percentage of total simulation
duration spent on exchanging sensing related messages.

  * •

Percentage Sensing Missed (PSM): In every SAW period, sensing is considered to
be complete, if all the sensing messages, for all the applications, were able
to be sent in the SAW duration. If no sensing messages were sent (completely
missed) or only a part of sensing messages were sent (partially missed), then
we consider those cases as sensing missed. So, the percentage of the number of
SAW periods in which sensing is missed is defined as Percentage Sensing Missed
(PSM).

  * •

Data Throughput: This is the total number of data bits sent by all the STAs
divided by the simulation time.

  * •

Percent Available Window Duration (PAWD): This is defined as the percentage of
SAW duration actually available for sensing related tasks. Note that when EDCA
acess is used, some part of SAW duration may be lost due to contention. In
such situations, the PAWD would fall below 100 %.

TABLE I: Simulation Parameters Common to all Configurations Parameter |  Value
---|---
Sensing availability window period |  1 (=100 TU = 102.4 ms)
TxOP duration |  5.484 ms
Number of antennas in the AP |  8
Number of antennas in each STA |  2
AP Bandwidth |  80 MHz
STA Bandwidth |  80 MHz
maximum number of subcarriers |  996
subcarrier grouping ($N_{g}$) |  4
Number of subcarriers reported in CSI ($N_{sc}$) |  250
Number of bits used for quantization of each CSI value ($N_{b}$) |  8
EDCA transmission in a TxOP |  Payload = 10 ethernet packets of size 1500 bytes each in an A-MPDU
MCS |  6
Simulation duration |  10000 seconds
TABLE II: Subcarrier Allocation vs. Number of STAs Number of STA |  Subcarriers per STA (size of RU allocation per STA)
---|---
1 |  996
2 |  484
[3 - 4] |  242
[5 - 9] |  106
[10 - 16] |  52

###  IV-C Simulation Experiment Design

Three parameters decide the sensing load in an IEEE 802.11bf network: (i)
number of sensing STAs, (ii) number of sensing applications, and (iii) number
of transmit and receive antennae involved in sensing. Hence, for this
performance study, we increased the sensing load in the system by increasing
the value of one of those parameters while keeping the values of other two
constant. This led us to run our experiments in three configurations as
described below.

  * •

Configuration 1: In this configuration, sensing load is increased by
increasing the number of STAs (nSTAs) involved in sensing at different SAW
durations. The number of applications is fixed at 4 and the sensing
transmitter and receiver antenna configuration is set to 2x2. Note that
sensing transmitter and receiver antenna configuration 2x2 implies that for
each STA, the AP (sensing transmitter) uses two of its eight antenna and each
STA (sensing receiver) uses all of its two antenna. Thus, the AP can engage
with up to four STAs in an SME for sensing.

  * •

Configuration 2: Sensing load, in this configuration, is increased by
increasing the number of sensing applications (numapp) in the system at
different nSTA values. The SAW duration is fixed at 127 (Note: SAW duration 1
= 100 $\mu$s), which corresponds to its maximum possible value of 12.7 ms. The
sensing transmitter and receiver antenna configuration is set to 2x2.

  * •

Configuration 3: Sensing load is increased by involving more transmit and
receive antennae, referred to as sensing transmitter and receiver antenna
(STRA) configuration, at different nSTA values. The number of application is
fixed at 4 and SAW duration is fixed at 127 (12.7 ms).

Simulation parameters common to all configurations are shown in Table I.

###  IV-D Experiment Results

####  IV-D1 Configuration 1

Fig. LABEL:fig:edca_nsta_pso shows how PSO changes as nSTA increases with EDCA
access. Generally, PSO decreases as nSTA increases, because there is more
contention for getting TxOP for sensing and hence, less duration is available
for sensing. However, PSO increases from nSTA = 4 to 5 and from nSTA = 9 to 10
for SAW duration $>$ 10\. At these nSTA transition points the size of an RU
assigned to each STA goes down (see Table II), hence, more time is needed to
send a given number sensing information bytes thereby increasing the overhead.
SAW duration 10 (1 ms) is very short relative to the SAW period of 102.4 ms.
Hence, the overhead is very low in this case and at high nSTA, due to high
contention, PSO goes down to almost zero.

As seen in Fig. LABEL:fig:pifs_nsta_pso, with PIFS access, PSO remains
unchanged when RU size per STA does not change. Unlike EDCA access, there is
no variability in actual SAW duration available for sensing since no
contention is involved. Report size per STA does not change since the number
of application is constant in this configuration. Hence, PSO remains constant
in the intervals where RU size per STA does not change. But when RU size per
STA decreases (e.g., from nSTA = 9 to 10), the duration to send sensing report
goes up and hence, PSO goes up. SAW duration = 10 is too short which limits
the number of sensing information bytes sent, to a constant value across the
nSTA values and hence, PSO does not change. Note that PSO for SAW duration 90
and 127 are identical all throughout. For these two SAW durations PSM is 0 %
all throughout (see Fig. LABEL:fig:pifs_nsta_psm). Hence, the amount of
sensing information bytes sent is same for the two SAW durations. For SAW
duration 50, PSO is identical to those of SAW duration 90 and 127 until
nSTA=9, since PSM is 0 % until that point. But after that PSM goes up to 100
%. But these missed sensing are due to partial missed sensing and PSO beyond
nSTA=9 is just 0.04 % lower than those of SAW durations 90 and 127. Hence, its
PSO looks almost identical to them after nSTA=9. This indicates that SAW
duration 50 fell slightly short of the duration needed to send all the sensing
information bytes.

With EDCA access, as nSTA increases, PSM increases (see Fig.
LABEL:fig:edca_nsta_psm). Due to more contention, actual SAW duration
available for sensing decreases, hence, more sensing is missed. SAW duration
10 is too short for EDCA such that $100\,\%$ sensing is always missed. Except
for nSTA = 1 none of the configurations can give $0\,\%$ PSM, which is
important for sensing application performance. For SAW duration = 10, even
though PSM is $100\,\%$, there is overhead which is due to partial missed
sensing.

SAW duration 10 is too short even for PIFS access, hence $100\,\%$ sensing is
missed (see Fig. LABEL:fig:pifs_nsta_psm). But SAW duration 90 and 127 give
$0\,\%$ throughout. SAW duration 50 is not long enough, beyond nSTA = 9, to
send all the sensing information bytes, due to decrease in RU size per STA.

With EDCA access, from Fig. LABEL:fig:edca_nsta_thrpt, it can be seen that
throughput goes down when sensing is on (compared to no sensing). As nSTA
increases, throughput decreases due to more contention and collisions. Also,
as SAW duration increases, throughput decreases because more time is used for
sensing. For SAW duration 10 and nSTA $\geq$ 3, throughput almost equals to
that of no sensing case because actual available sensing duration becomes very
short due to higher TxOP contention.

As shown in Fig. LABEL:fig:pifs_nsta_thrpt, with PIFS access, throughput is
lower than the respective EDCA cases due to higher sensing overhead (and lower
missed sensing). The throughput of SAW duration 50, 90 and 127 are almost
equal since sensing overheads for these cases are almost equal.

With EDCA access, PAWD generally decreases as nSTA increases due to increase
in contention (see Fig. LABEL:fig:config1_pawd). As expected, higher the SAW
duration, higher is the PAWD. PAWD is found to be always $100\,\%$ or very
close to $100\,\%$ for PIFS access (not shown in a graph).

####  IV-D2 Configuration 2

With EDCA access, PSO generally increases as numapp increases (Fig.
LABEL:fig:edca_numapp_pso). At high numapp (e.g., 6 and 8), for nSTA = 12 and
nSTA = 16, PSO remains flat, because the number sensing report bytes that can
be sent in the SAW duration is limited by the RU size allocated to the STA.
This can also be explained through PSM graph in Fig. LABEL:fig:edca_numapp_psm
where between numapp = 6 and 8, PSM becomes $100\,\%$ for nSTA = 12 and 16. We
notice that the overhead of nSTA = 12 is more than nSTA=16 which is counter
intuitive. With nSTA = 16, there is less duration available for sensing due to
more contention. Hence, nSTA = 12 gets more sensing opportunities and incurs
higher overhead. We also notice that for nSTA = 8, overhead goes beyond nSTA =
12 and 16 at numapp = 8. nSTA = 8 has larger RU size than nSTA = 12 and 16 and
hence, could send more sensing information bytes than nSTA = 12 and 16.

In case of PIFS access, overhead consistently increases as numapp increases
and nSTA increases (see Fig. LABEL:fig:pifs_numapp_pso). This can be explained
by observing PSM (see Fig. LABEL:fig:pifs_numapp_psm), where there is no
sensing missed. Hence overhead increases with increase in numapp and also with
increase in nSTA.

Fig. LABEL:fig:edca_numapp_psm shows that with EDCA access, as numapp
increases, PSM increases. At some nSTA values, the jump is more drastic at
certain numapp. For example, For nSTA = 12 and 16, as numapp increase from 4
to 6, the report size increases such that with the allocated RUs full report
cannot be sent even for one application. Hence, PSM increases drastically to
$100\,\%$. For nSTA = 1 and 4, the numapp increase does not affect PSM due to
low report size.

In case of PIFS access (see Fig. LABEL:fig:pifs_numapp_psm), there is no
missed sensing in any configuration since PIFS gives priority access to the
channel and the SAW duration 127 is long enough to send all sensing
information bytes.

With EDCA access (see Fig. LABEL:fig:edca_numapp_thrpt), for a given nSTA, the
throughput goes down slowly as numapp increases since it is only affected by
the report size increase. But for a given numapp, as nSTA increases throughput
drops much more due to higher contention and collisions as well as due to
report size increase. For nSTA = 12 and 16, as numapp increases from 6 to 8,
throughput remains flat because the PSO in this case does not change (see Fig.
LABEL:fig:edca_numapp_pso).

From Fig. LABEL:fig:pifs_numapp_thrpt, we observe that with PIFS access,
throughput decrease is steeper than EDCA access as numapp increases, since
PIFS access incurs $0\,\%$ PSM and higher sensing overhead than EDCA access.

Fig. LABEL:fig:config2_pawd shows the PAWD performance for EDCA access. Since
SAW duration is 12.7 ms and TxOP is 5.484 ms, sensing can have up to three
TxOPs. When nSTA is small (1 and 4), then increasing numapp does not change
the duration and the number of TxOPs required to complete sensing. Hence, PAWD
remains almost constant. But at large nSTA and large numapp, (e.g., nSTA = 12
and numapp = 6), it requires more TxOPs to finish sensing operation. Since
each TxOP is subject to contention, PAWD comes down. PAWD is always $100\,\%$
or close to $100\,\%$ for PIFS access (not shown).

####  IV-D3 Configuration 3

Figure 3: Available Window Duration vs. number of STAs with EDCA Access
(Configuration 3)

With EDCA access, as STRA increases, PSO generally increases (see Fig.
LABEL:fig:edca_antenna_pso). For nSTA = 12 and 16 PSO remains flat from STRA =
4x2 to 8x2, because the number of sensing information bytes that can be sent
in the SAW duration is limited by the RUs assigned to the STAs. This is
similar to what was seen in Configuration 2. Between nSTA = 12 and 16, the
overhead of nSTA = 12 is more than nSTA = 16, which is again similar to
Configuraiton 2 and the same explanation is applicable. We also notice that
for nSTA = 8, overhead goes beyond nSTA = 12 and 16 at STRA 8x2. nSTA = 8 has
larger RU size than nSTA = 12 and 16. Hence, nSTA = 8 could send more sensing
information bytes than nSTA = 12 and 16 which results in higher PSO.

With PIFS access, as shown in Fig. LABEL:fig:pifs_antenna_pso, PSO goes up as
STRA increases. As STRA increases report size also increases which leads to
more overhead. Overhead for nSTA = 12 and 16 are the same throughout, since RU
size per STA and PSM are same for them.

With EDCA access, as STRA increases, generally PSM also increases (see Fig.
LABEL:fig:edca_antenna_psm). Since nSTA = 8 has larger RU size, it does not
hit $100\,\%$ until STRA = 8x2. nSTA = 12 and 16 have smaller RU size, hence
they hit $100\,\%$ at a lower STRA = 4x2. For a given STRA configuration, as
nSTA increases, more sensing is missed due to the larger report size and more
contention to claim TxOP.

For PIFS access, as shown in Fig. LABEL:fig:pifs_antenna_psm, PSM is mostly
$0\,\%$, except for STRA = 8x2 and nSTA = 12 and 16 when the report size
becomes too large for the RUs assigned to the STAs and PSM hits $100\,\%$.

From Fig. LABEL:fig:edca_antenna_thrpt, for EDCA access we notice that
generally throughput decreases as STRA configuration increases due to increase
in sensing overhead. Also as nSTA increases for a given STRA configuration,
throughput decreases due to more contention as well as due to higher sensing
overhead. For nSTA = 12 and nSTA = 16 between STRA = 4x2 and 8x2, throughput
is flat because the corresponding PSO is also flat.

In Fig. LABEL:fig:pifs_antenna_thrpt it is observed that, with PIFS access,
throughput drops more than EDCA access case as STRA configuration increases
because of more sensing overhead. Between STRA = 4x2 and 8x2 throughput drop
is more drastic which matches with corresponding drastic increase in sensing
overhead.

PAWD performance with EDCA access, shown in Fig. 3, is very similar to the
that in Configuration 2. Hence, the same explanation holds. For PIFS access
PAWD is always 100 % or close to 100 %.

####  IV-D4 Discussion

From the above discussions on our simulation results, we highlight the
following key points. Since keeping PSM to $0\,\%$ is important for the
performance of a sensing application, EDCA access is not a suitable option as
it can lead to missed sensing in almost all cases. So, PIFS based access
should be used for sensing. Very short PAW duration (e.g., 10) is not a good
choice even at very low sensing load since it leads to missed sensing. In
fact, with PIFS access, it is better to set the SAW duration to its maximum
value 127 to avoid missed sensing, since the performance impact of sensing on
data communication in terms of PSO and throughput is almost identical to
smaller SAW durations at which there is no missed sensing. With PIFS access,
when numapp = 4 and nSTA = 16, the overhead is about $5\,\%$ and the
throughput drops by about 8 Mbps (or about $5\,\%$) compared to “no sensing”
case. Considering that this is a very high sensing load situation, the
overhead and throughput drop may be acceptable. Another important thing to
note is that the RU size changes at discrete points (with respect to nSTA) and
there can be sudden change of performance or performance may seem counter-
intuitive at those change points. These results also show that a system can be
designed with an upper limit on sensing overhead. The AP in such a system
would allow the sensing load to increase (by having more applications,
stations or more antennae), until the sensing overhead limit is reached.

##  V Conclusion

IEEE 802.11bf is a relatively new standard for Wi-Fi sensing. While
integrating sensing with communication in Wi-Fi network leads to more
efficient use of spectrum and hardware, it also contributes to communication
overhead. Although the TGbf has carefully designed the IEEE 802.11bf protocol
to limit the overhead, there is no performance analysis of the protocol and
its impact on data communication available in the literature. In this paper,
we evaluate performance of the protocol and its impact on data communication
using performance metrics defined by us. Our simulation results show that,
when NDPA sounding phase with reporting is enabled, EDCA access is not
suitable for sensing since it can lead to missed sensing. Also, very short SAW
duration (e.g., 10), even at low sensing load, is not a good choice since it
leads to missed sensing. A good rule of thumb is to have PIFS access with SAW
duration set to a large value (e.g., its maximum value of 127), which ensures
$0\,\%$ PSO in almost all the cases.

## References

  * [1] Y. Ma, G. Zhou, and S. Wang, “WiFi sensing with channel state information: A survey,” _ACM Computing Surveys (CSUR)_ , vol. 52, no. 3, pp. 1–36, 2019.
  * [2] H. Abdelnasser, K. A. Harras, and M. Youssef, “Ubibreathe: A ubiquitous non-invasive wifi-based breathing estimator,” in _Proceedings of the 16th ACM international symposium on mobile ad hoc networking and computing_ , 2015, pp. 277–286.
  * [3] W. Li, R. J. Piechocki, K. Woodbridge, C. Tang, and K. Chetty, “Passive wifi radar for human sensing using a stand-alone access point,” _IEEE Transactions on Geoscience and Remote Sensing_ , vol. 59, no. 3, pp. 1986–1998, 2020.
  * [4] H. Abdelnasser, M. Youssef, and K. A. Harras, “Wigest: A ubiquitous wifi-based gesture recognition system,” in _2015 IEEE conference on computer communications (INFOCOM)_. IEEE, 2015, pp. 1472–1480.
  * [5] S. Arshad, C. Feng, Y. Liu, Y. Hu, R. Yu, S. Zhou, and H. Li, “Wi-chase: A wifi based human activity recognition system for sensorless environments,” in _2017 IEEE 18th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM)_. IEEE, 2017, pp. 1–6.
  * [6] S. Mosleh, J. B. Coder, C. G. Scully, K. Forsyth, and M. O. A. Kalaa, “Monitoring respiratory motion with Wi-Fi CSI: Characterizing performance and the BreatheSmart algorithm,” _IEEE Access_ , pp. 1–1, 2022.
  * [7] L. Storrer, H. C. Yildirim, M. Crauwels, E. I. P. Copa, S. Pollin, J. Louveaux, P. De Doncker, and F. Horlin, “Indoor tracking of multiple individuals with an 802.11ax Wi-Fi-based multi-antenna passive radar,” _IEEE Sensors Journal_ , vol. 21, no. 18, pp. 20 462–20 474, 2021.
  * [8] P. Falcone, F. Colone, A. Macera, and P. Lombardo, “Localization and tracking of moving targets with WiFi-based passive radar,” in _2012 IEEE Radar Conference_ , 2012, pp. 0705–0709.
  * [9] “IEEE p802.11bf™/d3.0 draft standard for information technology— telecommunications and information exchange between systems local and metropolitan area networks— specific requirements part 11: Wireless LAN medium access control (MAC) and physical layer (PHY) specifications amendment 2: Enhancements for wireless LAN sensing,” 2023.
  * [10] T. Ropitault, S. Blandino, A. Sahoo, and N. Golmie, “IEEE 802.11bf: Enabling the widespread adoption of wi-fi sensing,” accepted in IEEE Communications Standards Magazine: https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=935175, [Online; accessed September 28, 2023].
  * [11] S. Blandino, T. Ropitault, C. R. da Silva, A. Sahoo, and N. Golmie, “IEEE 802.11 bf DMG sensing: Enabling high-resolution mmwave wi-fi sensing,” _IEEE Open Journal of Vehicular Technology_ , vol. 4, pp. 342–355, 2023.
  * [12] “Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications,” 802.11 Working Group of the LAN/MAN Standards Committee of the IEEE Computer Society, Dec. 2020.
  * [13] “IEEE p802.11az™/d7.0 draft standard for information technology— telecommunications and information exchange between systems local and metropolitan area networks— specific requirements part 11: Wireless LAN medium access control (MAC) and physical layer (PHY) specifications amendment 4: Enhancements for positioning sensing,” 2022.
  * [14] Y. Daldoul, D.-E. Meddour, and A. Ksentini, “Performance evaluation of ofdma and mu-mimo in 802.11 ax networks,” _Computer Networks_ , vol. 182, p. 107477, 2020.
  * [15] “802.11ax lightsim,” https://github.com/yousri-daldoul/802.11ax-lightsim, accessed April 2023.

  *[TGbf]: Task Group IEEE 802.11bf