Preparing Wi-Fi 7 for Healthcare Internet-of-Things

Abstract

The healthcare Internet of Things (H-IoT) is an interconnection of devices capable of sensing and transmitting information that conveys the status of an individual’s health. The continuous monitoring of an individual’s health for disease diagnosis and early detection is an important application of H-IoT. Ambient assisted living (AAL) entails monitoring a patient’s health to ensure their well-being. However, ensuring a limit on transmission delays is an essential requirement of such monitoring systems. The uplink (UL) transmission during the orthogonal frequency division multiple access (OFDMA) in the wireless local area networks (WLANs) can incur a delay which may not be acceptable for delay-sensitive applications such as H-IoT due to their random nature. Therefore, we propose a UL OFDMA scheduler for the next Wireless Fidelity (Wi-Fi) standard, the IEEE 802.11be, that is compliant with the latency requirements for healthcare applications. The scheduler allocates the channel resources for UL transmission taking into consideration the traffic class or access category. The results demonstrate that the proposed scheduler can achieve the required latency for H-IoT applications. Additionally, the performance in terms of fairness and throughput is also superior to state-of-the-art schedulers.

Full text

Citation: Qadri, Y.A.; Zulqarnain;
Nauman, A.; Musaddiq, A.;
Garcia-Villegas, E.; Kim, S.W.
Preparing Wi-Fi 7 for Healthcare
Internet-of-Things. Sensors 2022,22,
6209. https://doi.org/10.3390/
s22166209
Academic Editor: David Plets
Received: 14 July 2022
Accepted: 16 August 2022
Published: 18 August 2022
Publisher’s Note: MDPI stays neutral
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Copyright: © 2022 by the authors.
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sensors
Article
Preparing Wi-Fi 7 for Healthcare Internet-of-Things
Yazdan Ahmad Qadri 1, Zulqarnain 1, Ali Nauman 1, Arslan Musaddiq 2, Eduard Garcia-Villegas 3
and Sung Won Kim 1,*
1Department of Information and Communication Engineering, Yeungnam University,
Gyeongsan-si 38541, Korea
2Department of Computer Science and Media Technology, Linnaeus University, 391 82 Kalmar, Sweden
3Department of Network Engineering , Universitat Polit‘ecnica de Catalunya (UPC), 08034 Barcelona, Spain
*Correspondence: swon@yu.ac.kr
Abstract:
The healthcare Internet of Things (H-IoT) is an interconnection of devices capable of sensing
and transmitting information that conveys the status of an individual’s health. The continuous
monitoring of an individual’s health for disease diagnosis and early detection is an important
application of H-IoT. Ambient assisted living (AAL) entails monitoring a patient’s health to ensure
their well-being. However, ensuring a limit on transmission delays is an essential requirement of such
monitoring systems. The uplink (UL) transmission during the orthogonal frequency division multiple
access (OFDMA) in the wireless local area networks (WLANs) can incur a delay which may not be
acceptable for delay-sensitive applications such as H-IoT due to their random nature. Therefore, we
propose a UL OFDMA scheduler for the next Wireless Fidelity (Wi-Fi) standard, the IEEE 802.11be,
that is compliant with the latency requirements for healthcare applications. The scheduler allocates
the channel resources for UL transmission taking into consideration the traffic class or access category.
The results demonstrate that the proposed scheduler can achieve the required latency for H-IoT
applications. Additionally, the performance in terms of fairness and throughput is also superior to
state-of-the-art schedulers.
Keywords: IEEE 802.11be; Internet of Things; orthogonal frequency division multiple access; healthcare
1. Introduction
The current demographic trends indicate that the world is moving towards an aging
population. The World Health Organization (WHO) has predicted that one in six people will
be over the age of 60 by 2030 [
1
]. The number of people aged over 60 will double by 2050.
This aging trend has led to a growing research interest in ambient assisted living (AAL)
systems. AAL is essentially assisted care for humans, especially at an advanced age or with
severe medical conditions. The caregivers are assisted by technology, especially monitoring
systems and socially assisted robots [
2
]. Internet of Things (IoT) is a key enabler in an
AAL system. IoT can provide insights about the patient’s health and living environment
and can ubiquitously monitor their well being [
3
,
4
]. An IoT-based AAL system consists
of an array of sensors, which may be health sensors such as an electrocardiogram (ECG),
glucometer, pulse oximeter or an activity sensor such as an accelerometer, inertial sensor
or even a camera [
5
]. These sensors continuously sense and transmit their respective
data to a processing unit which may be local or cloud to generate insights about the
patient’s health. The end users, i.e., patients, caretakers and healthcare professionals can
access these insights to advise the observed individuals and take a suitable course of
action [
6
]. Integrating artificial intelligence (AI) and fog computing with these sensor
networks can enable greater autonomy and accuracy in detection, especially in terms of
activity recognition [
7
]. The smart homes equipped with sensors are also capable of high
accuracy health monitoring, especially in an AAL scenario. Figure 1depicts the scenario of
an AAL environment. However, the effectiveness of this monitoring system is significantly
Sensors 2022,22, 6209. https://doi.org/10.3390/s22166209 https://www.mdpi.com/journal/sensors

Sensors 2022,22, 6209 2 of 16
affected by the communication performance between the sensors and the processing unit.
The IEEE 802.15.6 is a standard defined for wireless body area networks (WBANs) [
8
], but
Bluetooth, Zigbee, Wi-Fi and cellular networks are commonly used in the commercially
available wearable devices such as smart watches and health trackers [
9
]. The IEEE 802.11
Wireless Local Area Networks (WLANs) or Wi-Fi networks are capable of delivering high
throughput especially for health monitoring and smart home applications [
10
]. However,
one of the key requirements of the healthcare applications is low latency, where the critical
health data should be transmitted within a bounded time limit. Due to the random nature
of the wireless links, the latency performance of Wi-Fi has not been suitable for healthcare
applications. The Wi-Fi standard has evolved over time to deliver increased data rates but
has been limited in its support for dense deployments and low latency applications. The
introduction of multi-user techniques (MU), allowing multiple simultaneous transmissions
from different stations, opened new ways to support a large number of connected devices
by keeping a low collision probability and access delay. Orthogonal frequency multiple
access (OFDMA) is one of the new MU mechanisms brought by the IEEE 802.11ax (Wi-Fi 6)
amendment [
11
]. However, OFDMA, as defined in IEEE 802.11ax’s specifications, does
not ensure the optimal performance in terms of delay, network throughput and scalability.
Therefore, an efficient management of spectral resources i.e. transmission opportunities
(TXOP) and assigned spectrum is required.
QoS Aware Link
Cloud Server
Remote processing and global data sharing
Local Server
Critical alert generation
Local Caregiver Health Service Providers
Continuous Monitoring
Ambient Assisted Living
Ambient Assisted Living Space
Video Monitoring
Activity recognition
Wearables and Sensors
Health status measurement
High Throughput
Low Latency
Figure 1. Continuous monitoring of patients in H-IoT: ambient assisted living.
The upcoming IEEE 802.11be amendment is also known as the extremely high through-
put (EHT) amendment. It will also be branded as Wi-Fi 7. There are a number of proposed
incremental upgrades over Wi-Fi 6 and a number of new features under consideration.
The IEEE P802.11-Task Group BE (TGbe) is tasked with defining EHT physical (PHY) and
medium access control (MAC) layers for a major amendment in the next generation of
WLANs. The Project Authorization Report (PAR) approved in 2019 defined the scope of
this amendment to the IEEE 802.11 PHY and MAC layers [
12
]. The key modifications
include:
•
At least one operational mode that supports a minimum of 30 Gbps of maximum
throughput at the service access point (SAP).
• Operation at the frequency range between 1 and 7.250 GHz.
•
Backward compatibility with legacy standards operating at 2.4, 5 and 6 GHz fre-
quency bands.
• At least one operational mode for worst-case latency and jitter.

Sensors 2022,22, 6209 3 of 16
However, the upcoming Wi-Fi 7 amendment is also set to have new features enabling
time-sensitive networking (TSN) [
13
]. These proposed features will enable this amendment
to support high quality-of-service (QoS) applications such as real-time health monitoring
and remote robotic control [
14
]. OFDMA was introduced in the Wi-Fi 6 to improve the
spectral efficiency and to support large deployments [
15
], but Wi-Fi 7 is expected to enhance
the OFDMA performance by including new features in the OFDMA operation. Using an
access point (AP) as a scheduler to allocate the spectral resources can affect the delay per-
formance. The AP acts as a scheduler which can solicit uplink (UL) transmissions from the
Wi-Fi stations (STAs) when it gains the TXOP. Therefore, an optimal delay performance can
be achieved by utilizing an optimal scheduling methodology. Intel Corporation outlined
potential approaches to enable TSN in WLANs [
16
], including defining a low-latency access
category (LL-AC) that can provide a deterministic service with a defined latency. Addi-
tionally, a provisioning mechanism needs to be developed for the the LL-AC that would
entail modifications in the current QoS-aware standards. Provisioning the resources using
an AC-aware mechanism can bridge the gap between the performance of the current Wi-Fi
standards and the performance requirements of delay-sensitive applications such as H-IoT.
Some applications have well-defined bounds on their delay and reliability performance as
outlined in [
17
]. Therefore, we propose a scheduling algorithm that utilizes a two-pronged
approach, utilizing the buffer size of the different access categories (AC) and tracking
the previous transmissions of the STAs. Since H-IoT data transmissions are sensitive to
delay, we distinguish between the various traffic classes that are identified by their AC
while scheduling UL transmissions from the STAs. This distinction allows the scheduler to
determine which STAs have a higher priority over other STAs in transmitting their queued
frames. This approach results in a lower latency for the higher priority traffic along with
ensuring fairness among the STAs.
The literature review presented in Section 3reveals that the QoS information remains
under-utilized for scheduling transmissions using OFDMA in the Wi-Fi networks. Ad-
ditionally, the new amendment to the existing Wi-Fi standards, IEEE 802.11be, is under
development that considers TSN as a significant goal. Several research works list latency
as a critical metric in healthcare-based IoT applications, and numerous works focus on
improving the latency performance in context of healthcare-based applications [
18
–
21
]. The
work [
19
] identifies scheduling transmissions in time as a potential strategy to reduce delay.
Therefore, we can list the following as the contributions of this work:
•
We propose an OFDMA scheduler that utilizes the priorities of various ACs for
scheduling UL transmissions in delay-sensitive IEEE 802.11be-based IoT networks,
especially for healthcare applications.
•
We also evaluate the performance of the proposed methodology on a network simula-
tor. NS-3, and compare its performance with state-of-the-art mechanisms.
The remaining part of this paper is structured as follows. Section 2introduces the
enhancements in the IEEE 802.11be amendment. Section 3presents an overview of the
related work. The proposed OFDMA scheduler is introduced in Section 4, and its perfor-
mance evaluation is presented in Section 5. Finally, the authors conclude the discussion in
Section 6. The term EHT is interchangeably used with the name of the amendment “IEEE
802.11be”. Table 1lists the frequently used abbreviations used in the manuscript.

Sensors 2022,22, 6209 4 of 16
Table 1. List of abbreviations used in the manuscript.
Abbreviation Description
AAL Ambient Assisted Living
AC Access Category
AID Association ID
AP Access Point
BSR Buffer Status Report
BSRP Buffer Status Report Poll
BSS Basic Service Set
CW Contention Window
EDCA Enhanced Distributed Channel Access
EHT Extremely High Throughput
IoT Internet of Things
MCS Modulation and Coding Scheme
OCW OFDMA Contention Window
OFDMA Orthogonal Frequency Division Multiple Access
QAM Quadrature Amplitude Modulation
QoS Quality-of-Service
RTA Real-time Applications
RU Resource Units
STA Wi-Fi Station
TSN Time-Sensitive Networking
TXOP Transmission Opportunity
WLAN Wireless Local Area Network
2. IEEE 802.11be Extremely High Throughput Standard
The IEEE 802.11be amendment will include modifications to the IEEE Std 802.11 PHY
and MAC layer. These enhancements are aimed to increase the throughput to a minimum
of 30 Gbps while considering the limits on latency for time-sensitive applications. The
groundwork for these changes has already been laid in the IEEE 802.11ax standard, and this
amendment builds upon them. Like its predecessor, EHT uses OFDMA for the allocation
of bandwidth resources to the STAs associated with it. Authors in [
22
] offer a detailed
discussion on the new and improved features of the EHT. These proposals can be divided
into two sub-groups: enhancements at the PHY Layer and enhancements at the MAC Layer.
2.1. Enhancements at the PHY Layer
The role of the PHY layer in the WLANs is to define the electrical transmission
specifications of the device and its interaction with the transmission medium. The PHY
layer functions include modulation of the data, establishment of connection over a medium
and allocation of resources among the participating nodes in the network. The proposed
enhancements improve the transmission protocols and allocation of physical resources at
the PHY layer. With the opening of the 6 GHz band, the available bandwidth has effectively
doubled, and the TGBe is planning to exploit this newly opened spectrum. The 2.4 and
5 GHz uses a 20 MHz primary channel. In the 5 GHz band, up to 160 MHz channels are
available in 80 + 80 MHz or 160 MHz configuration. Additionally, in the 6 GHz band, wider
channel bands are available in multiple configurations. The 320 MHz channels can be
realized by aggregating contiguous channels in the 6 GHz band entirely or by aggregating
non-contiguous channels across 5 and 6 GHz bands [
23
]. The EHT is inclined to allow
aggregations across the 2.4, 5 and 6 GHz bands. Therefore, EHT has included various
schemes for channel aggregation configurations from across the different frequency bands
such as contiguous 240 and 320 MHz bands, non-contiguous bandwidths in 160 + 160 MHz,
or 240/160 + 80 MHz [
24
]. The tone plans for up to 160 MHz wide channels are expected to
remain similar to the IEEE 802.11ax 5 GHz band. A 2018 document [
25
] of TGbe discusses
the throughput enhancement utilizing multi-band transmissions.

Sensors 2022,22, 6209 5 of 16
The use of higher-order modulation schemes increases the throughput under favorable
received signal quality. Therefore, as an evolutionary step, the recommendations for 4096-
Quadrature Amplitude Modulation (QAM) is on the books [
22
]. The higher the order of
the constellation is, the more information can be transmitted per symbol. The 4096-QAM
allows 12-bit symbols that result in a 20% increase in the data rate compared to 1024-QAM
in 802.11ax WLANs [
26
]. To achieve high SNR for 4K-QAM, STA will require multiple
spatial streams (SS), therefore affecting the multi-user (MU) performance, which must
sacrifice the benefits of spatial multiplexing in favor of spatial diversity. Therefore, this
feature will require further exploration before being accepted by the EHT.
The PHY preamble design for the EHT will incorporate changes due to the added
features, but at the same time, it should be able to maintain backward compatibility. Each
preamble since the IEEE 802.11n standard can be identified by frame design, which would
include modulation information. The EHT PHY frame will include a universal signal
field (U-SIG) as well as EHT specific signal field (EHT-SIG). The U-SIG field is expected
to introduce forward compatibility. However, some of the information will be version
dependent, while some information will be version independent. The EHT-SIG field will
convey information regarding the new features of the EHT standard [26].
2.2. Enhancements at the MAC Layer
Many significant MAC features from IEEE 802.11ax such as MU multiple input multi-
ple output (MU-MIMO), OFDMA and spatial reuse will be extended in IEEE 802.11be. The
support for more SS will also enable more flexible MU-MIMO arrangements. However, the
current explicit channel state information (CSI) acquisition procedure may not cope well
with such a high number of antennas, and for that reason, TGbe is currently evaluating
several alternatives to enhance explicit sounding, even considering the introduction of
an implicit procedure. As for OFDMA, enhanced resource unit (RU) allocation schemes
will allow allocating multiple contiguous and non-contiguous RUs to a single STA. Con-
sequently, these novel schemes could significantly increase spectral efficiency and overall
network throughput and, even better, satisfy timely data delivery. Whether based on
MU-MIMO or OFDMA, MU transmissions are key to reducing the channel access latency,
as packets from/to different users can be de-queued simultaneously. MU transmissions
reduce contention by minimizing the negative impact of collisions, which is exacerbated by
the exponential backoff used in the legacy medium access. The multi-link operation (MLO)
will likely become the most representative feature of IEEE 802.11be, being able to yield an
order-of-magnitude reduction in the worst-case latency experienced by Wi-Fi devices and
meet the stringent requirements of real-time applications (RTAs) even under dense traffic
conditions [24,26,27].
3. Related Work
The OFDMA essentially divides the resources, both in frequency and time, which
enables the allocation of resources to multiple users at a time instant. In the frequency
domain, a channel is divided into sub-carriers that are allocated for a time interval that
is several symbols long. These sub-carriers are grouped in standard combinations. These
combinations of sub-carriers or “tones” form the RUs which are allocated to multiple users.
The IEEE 802.11ax was the first Wi-Fi standard to utilize the OFDMA. A 20 MHz channel in
the 802.11ax is composed of 256 sub-carriers which are combined in groups of 26, 52, 106 and
242 tones. With the sub-carrier spacing of 78.125 kHz, these RUs constitute sub-channels of
2, 4, 8 and 20 MHz bandwidth, respectively. MU-OFDMA aims at improving the spectral
efficiency of the wireless medium by allowing multiple STAs to transmit simultaneously
during a TXOP by using different orthogonal frequency division multiplexing (OFDM) sub-
carriers.The process of UL is depicted in Figure 2. The high-efficiency WLANs (HE-WLAN)
use UL-OFDMA-based random access (UORA) for randomly selecting the STAs for UL
transmission. UORA assigns the different RUs in UL. The RUs for random access (RA) are
termed as random access resource units (RA-RUs) and have an have an Association ID

Sensors 2022,22, 6209 6 of 16
(AID) value of 0. The RA-RUs for unassociated STAs have an AID equal to 2045. The STA in
scheduled access (SA) do not contend for RUs in the RA process and are assigned a non-zero
AID other than 0 or 2045. All STAs supporting UORA maintain an OFDMA backoff (OBO)
counter that is randomly selected between 0 and OFDMA contention window (OCW)
value. The OCW value, where
𝑂𝐶𝑊 ∈ (
0,
𝑂𝐶𝑊𝑚𝑎 𝑥 )
, is initialized at the
𝑂𝐶𝑊𝑚𝑖𝑛
value. The
𝑂𝐶𝑊𝑚𝑖𝑛
and
𝑂𝐶𝑊𝑚𝑎 𝑥
values are conveyed by the AP using the UORA Parameter Set. The
UL OFDMA process is initiated by the AP by sending a Trigger Frame (TF) that conveys
the number of RA-RUs that are available for UL transmission [
28
]. Each STA decrements
the number of RA-RUs from its respective OBO value. The STAs for which the difference
(𝑂𝐵𝑂 −𝑁𝑅 𝐴−𝑅𝑈 )
is zero or a negative value are allowed to randomly select a RU from their
respective set of eligible RA-RUs (AID 0 or 2045) to transmit the frame. The STAs with a
non-zero difference retain this value for the next TF to attempt transmission using it as their
OBO value. After a successful transmission, the OCW is set to the latest
𝑂𝐶𝑊𝑚𝑖𝑛
value
from the latest available OFDMA Parameter Set. If the STA fails to successfully transmit
a frame, it doubles its OCW each time until the
𝑂𝐶𝑊𝑚𝑎 𝑥
is reached. The re-transmission
may also be attempted by the simultaneous Enhanced Distributed Channel Access (EDCA).
This process is depicted in Figure 3.
DL
DL
DL
DL
DL
DL
DL
DL
DL
UL
UL-MU
UL-MU
UL-MU
TF-R MU-BA TF-R
AP
User 1
User 2
User 3
Trigger Interval (TI)
SIFS SIFS
1. Transmission
Request Phase
2. UL-MU Frame
Transmission Phase
3. DL-MU Frame
ACK Phase
Figure 2. Trigger-based UL multi-user transmission sequence in IEEE 802.11ax WLAN.
The authors in [
11
] evaluate the impact of OFDMA on the system throughput and
latency in saturated and unsaturated conditions. The authors conclude, that in saturated
conditions, throughput and latency performance is considerably degraded. However, the
efficiency and throughput gains using OFDMA are maximized by introducing a scheduling
mechanism at the AP. The work [
29
] analyzes the performance of various scheduling strate-
gies. The UL-OFDMA scheduling is designed as an optimization problem, where a utility
function associated with the RU allocation is maximized under constraints. The work
in [
29
] also explores some classic schedulers to evaluate the effect on uplink data trans-
mission rate. To enable real-time operation over a Wi-Fi network, UL OFDMA scheduling
can be implemented. In enabling real-time operation, a sub-millisecond latency is highly
desirable with a reliability of 99.999%. Due to the random nature of the backoff mechanism
in UORA, the delay performance is highly unpredictable. Authors in [
30
] attempt to ensure
real-time operation. The authors propose a Cyclic Resource Assignment (CRA) algorithm to
minimize the delay for the RTA frames with high reliability. However, the authors assume
that the collisions occur only if two STAs select the same RU during the random access
(RA) period. If there are no collisions during the RA,
𝑓
STAs with data frames are allocated

Sensors 2022,22, 6209 7 of 16
RUs where
𝑓
is the number of RA-RUs. However, if a collision occurs, the AP allocates
the RUs cyclically to
(𝑁𝑅𝑈 −𝑓)
STAs without contention in the next slot, where
𝑁𝑅𝑈
is
the total number of RUs. The cycle continues until all STAs are allocated a RU without
collision. When the cycle ends, the UORA resumes its normal operation. However, the
results also show that when the number of STAs becomes large, the delay performance
worsens, especially in default UORA. The CRA algorithm for RTA STAs occupies more
bandwidth resources than the non-RTA STAs, thus showing unfair behavior. In [
31
], the
authors implement the MU-OFDMA on the NS-3 simulator to evaluate the throughput
performance and to validate their analytical model. The authors assume that all the STAs
in the basic service set (BSS) are HE-STAs and explicitly solicit a buffer status report (BSR)
from the STAs. They consider two performance indicators: the network throughput and
the BSR delivery rate and show that in IEEE 802.11ax networks there is a trade-off between
these indicators, which can be regulated by changing the number of RUs allocated for
UORA. The authors discuss the impact of the BSR delivery rate parameter on the through-
put. The simulations and the analytical model closely resemble each other and reveal
that the RA-RU allocation is detrimental to the throughput performance compared to the
deterministic access. It is also noteworthy, that at the time of writing this paper, there is no
validated NS-3 implementation of UORA.
Start
[For all STA]
If OBO(STA) ≤ 0
No
Assign a Random RU from number of
Random Access-RUs
Transmit
Yes
Trigger Frame (TF) Received
Initialize a OFDMA Contention
Window w = OCWmin+1
[At all STAs with TF] Select a random OFDMA
backoff window
OBO = random [0, w-1]
[At all STAs with TF] Decrement OBO
OBO = OBO-number of RA-RUs
The TF informs the STA
about the number of
random access RUs
Do Not Assign and
wait for next TF
After TF is received
Is TX Successful Increment w= 2*w until W=CWmax
Yes
No
Figure 3. UORA operation for random access in UL-OFDMA .
The paper [
32
] is focused on optimizing the UORA OBO mechanism. In principle,
the OBO is decremented by the number of RU that are available and conveyed in a TF.
However, it is not an efficient approach considering the impact it has on dense deployments.
The authors propose an Efficient-OBO mechanism for the UORA process that does not
incur additional control overhead. Instead, the authors consider the probabilities of RUs
being allocated and derive an idea about the network congestion. The OBO is decremented
by a value determined by a congestion parameter that takes into consideration the network
congestion during that time slot. This also ensures that the starvation of STAs is avoided.

Sensors 2022,22, 6209 8 of 16
In [
33
], the authors propose a hybrid approach to increase the throughput by introducing a
carrier-sensing approach as an additional backoff mechanism in the UL-OFDMA operation.
The AP senses if all RUs are occupied and attempts to fill all the RUs in a TXOP. It determines
if a RU is unallocated by using a p-persistent carrier sense multiple access (CSMA) trial
over a RU. It ensures high throughput without considering the buffer status of the STAs or
requiring any additional signaling, but it suffers from high latency.
The current literature points out the fact that the support for RTAs is a major goal
in Wi-Fi 7 networks, and scheduling the UL transmissions can significantly improve the
latency performance in a WLAN. It is evident from the literature that the QoS requirements
of the STAs are not individually catered to, and neither is the BSR information widely
exploited for scheduling the UL transmissions. Therefore, this work envisages devising
delay-sensitive scheduling for UL OFDMA considering QoS traffic, while at the same time
ensuring that the non-QoS STAs do not starve. The unexploited BSR information can allow
the AP to schedule the transmissions based on the buffer status in the ongoing TXOP while
considering their QoS requirements.
4. Delay-Sensitive OFDMA Scheduling for H-IoT in Wi-Fi 7
To ensure fulfillment of the delay requirements, an OFDMA scheduling algorithm is
proposed for the H-IoT. The proposed algorithm takes the queue sizes of the various ACs
into account in addition to the previous scheduling decisions to allocate an RU during UL
OFDMA. Since the AP is aware of the queue size of the STAs, an AP can act as a scheduler
in a BSS. In the proposed delay-sensitive scheduler, we assume that 𝑛STAs are associated
with an AP. The STAs transmit and receive frames from the AP. There are two ways in
which the STAs and AP can transmit data, either by gaining a TXOP using the EDCA
or using OFDMA. The STAs and the AP all compete for TXOP using EDCA. However,
when an AP gains the TXOP, it can communicate with the STAs using OFDMA, both in
the UL and DL. This work focuses on the UL transmission from the STAs to the AP. The
STAs transmit a BSR in response to a buffer status report poll (BSRP) transmitted by the
AP. The BSR relays traffic information of each AC at the STAs to the AP, which can be
utilized in making scheduling decisions. Additionally, the header of the QoS-Data frames
transmitted by the STAs contains the queue size and the traffic identifier associated with it
in the QoS Control Field. This work proposes utilizing this key information to schedule UL
transmissions from STAs to minimize the transmission delay for time-sensitive data frames
without utilizing additional signalling overhead.
Each AC has a different priority that is enforced in the EDCA with the different EDCA
parameter set values. However, in the case of OFDMA, there is no distinction between
the different ACs. From the discussion in Section 3, it is clear that the scheduling for
the UL transmission does not consider the queue sizes of different ACs. The proposed
algorithm takes into confidence the following two parameters: (1) the queue size of each
AC at the STA, and (2) previous transmissions at the STA. Initially, the scheduler initializes
a priority against each STA that is based on the queue size of each AC and the previous
transmissions. With each subsequent TXOP, the priority is updated to consider the current
queue size of each AC. The higher priority ACs are given more consideration compared
to the low priority ACs while calculating the priority. Additionally, the priority of the
STA also considers the previous transmissions of an STA. The STAs with a non-zero queue
size for higher priority AC and a history of transmission in previous TXOPs indicate
that the STA has accumulated critical data frames. Equation (1) denotes the mathematical
equation representing the priority calculation for each STA. Figure 4illustrates the proposed
algorithm as a flowchart.

Sensors 2022,22, 6209 9 of 16
Start
If
(Buffer STAi) != 0
No
Compute the Priority of STAi
Using Equation:
Attempt Transmission
Yes
Initialize
BSR of all ACs of STAi
Previous Tx of STAi
Do Not Assign and
wait for next TF
Trigger Frame Received
Sort the top R STAs according to priority
󰇭 ()×()
4
=1 󰇮×
From TF
R= Number of
RUs
End
Figure 4. The flowchart of the proposed algorithm.
𝑃𝑟𝑖 𝑜𝑟𝑖𝑡 𝑦 (𝑆𝑇 𝐴𝑖)=
4
Õ
𝑗=1
𝑄𝑢𝑒𝑢𝑒(𝐴𝐶𝑗) × 𝑊𝑒𝑖 𝑔ℎ𝑡 (𝐴𝐶 𝑗) × 𝑇 𝑋𝑂 𝑃𝑃 𝑟 𝑒𝑣 𝑖𝑜 𝑢𝑠 (𝑆𝑇 𝐴𝑖)(1)
where
𝑄𝑢𝑒𝑢𝑒(𝐴𝐶𝑗)
is the queue length of each AC,
𝑊𝑒𝑖𝑔 ℎ𝑡 (𝐴𝐶𝑗)
is the weight or priority of
each AC, and
𝑇 𝑋𝑂 𝑃𝑃 𝑟 𝑒𝑣 𝑖𝑜𝑢 𝑠 (𝑆𝑇 𝐴𝑖)
is the indicator of previous transmissions by that STA.
The list of ACs
𝐴𝐶 ={𝐴𝐶_𝐵𝐸
,
𝐴𝐶_𝐵𝐾
,
𝐴𝐶_𝑉 𝐼
,
𝐴𝐶_𝑉𝑂}
includes the four ACs defined in
the IEEE 802.11e standard [
34
]. These ACs correspond to the best effort, background, video
and voice, respectively. Therefore, the STAs with the larger queue of high-priority AC
traffic are given a higher priority. Additionally, the weight of each AC is determined by
the use case which reflects the importance of each AC for that specific application. For
the vital health monitoring applications in H-IoT, best effort traffic (AC_BE) has a greater
significance over the voice traffic (AC_VO) or video traffic (AC_VI).
Complexity Analysis
Since the time complexity of an algorithm is a function of the size of the input it takes,
the time-complexity of the proposed scheduler depends on the number of STAs in the BSS.
Equation (1) depicts the process of calculating the priority of an STA based on its traffic
patterns and the previous transmissions. For
𝑛
STAs in a BSS, the buffer status and previous
transmissions are obtained and are then used to calculate the priority of each STA. The
statement for calculating the priority has a complexity
O(
1
)
which is calculated
𝑛
times for
𝑛
STAs. Our implementation in NS3 uses an emplace function to arrange the STAs in an
array according to the priority of the STA. The C++ emplace function has a complexity of
O(𝑛)
[
35
]. Therefore, the overall time complexity of the algorithm is
O(𝑛)
, as the algorithm
loops through 𝑛STAs without any nested operations.
5. Experimental Evaluation
To evaluate the performance of the proposed algorithm, an event-based network
simulator for wireless networks, known as Network Simulator-3 (NS-3) is used. The NS-3

Sensors 2022,22, 6209 10 of 16
version 3.35 was utilized to simulate the proposed algorithm. Since the IEEE 802.11be
is an incremental development over the 802.11ax standard, most of the basic functions
remain common in both standards. Therefore, we evaluate the performance using 802.11ax
implementation as a basis.
Section 3discusses UORA, which is the default UL random access method in OFDMA.
However, the UORA algorithm is not yet included in the NS-3. Therefore, we implement
our version of UORA in NS-3 while considering some key assumptions. We assume that
only a single RU can be allocated to a single STA in a TXOP and that the collision occurs
when two STAs randomly select the same RU. However, we do not consider collision
occurring during the transmission, and we limit the
𝑂𝐶𝑊𝑚𝑎 𝑥
value range in between
[
32, 1024
]
. We also compare our proposed method with the OFDMA scheduler presented
in [
11
] and validated in NS-3. The scheduler in [
11
] uses a history-aware approach for
scheduling the transmissions. The authors determine the priorities based on the previous
allocations and then schedule transmissions in a round-robin fashion. The AP considers
the previous transmissions between the AP and STA to determine a metric for scheduling
the transmissions in DL and UL. Table 2compares the focus of this work with the state of
the art.
Table 2. Comparison of the proposed algorithm with the state of the art.
Scheduler Latency Throughput Fairness
Proposed X X X
History Aware X
UORA X
Equation (1) has three variables: queue size of each AC, the weight reflecting the
priority associated with the corresponding AC and the previous transmissions of the STA.
The weight of each AC is determined by the use case. Its value reflects the urgency of
transmission of a frame belonging to an AC. In the evaluation, we consider two ACs: best
effort (BE) and voice (VO), which are distinguished in their priority. The weight for each AC,
denoted by
𝑊𝑒𝑖𝑔 ℎ𝑡 (𝐴𝐶𝑗)
reflects which traffic class should be favoured over the other when
making the scheduling decisions. We assign a value between zero and one to each AC while
ensuring the sum of all the
𝑊𝑒𝑖𝑔 ℎ𝑡 (𝐴𝐶𝑗)
values is equal to one. This implies that, if one AC
has a higher priority, its queue size effects the STA’s priority more significantly than the AC
with a lower
𝑊𝑒𝑖𝑔 ℎ𝑡 (𝐴𝐶𝑗)
value. However, the
𝑊𝑒𝑖𝑔 ℎ𝑡 (𝐴𝐶𝑗)
can be selected experimentally
based on the traffic patterns. The offered load is generated as a UDP application of different
AC by setting the traffic identifier (TID) value specific to a given AC. The transmissions
are scheduled for both the DL and UL, but the UL transmission remains the focus of this
work. Additionally, the RU size is dynamically selected for each TF but remains the same
for all STAs during a TXOP. The simulator selects the number of RUs based on the number
of STAs that have UL traffic during that TXOP, and the maximum number of available
RUs is determined by the channel width. In this evaluation, the maximum number of RUs
is 18 when 26-tone RUs are generated, as we utilize a 40 MHz channel. We evaluate the
performance of the proposed algorithm according to three key metrics: latency, fairness and
average throughput. We also evaluate the satisfaction levels for different applications in
terms of delay performance. Additionally, we evaluate the delay performance as a function
of the Modulation and Coding Scheme (MCS) index. Table 3lays out the parameters that
are used in the evaluation.

Sensors 2022,22, 6209 11 of 16
Table 3. Parameters used in the simulation.
Parameter Value
Frequency Band 5 GHz
Channel Width 40 MHz
Number of RUs 1, 2, 4, 8, 18
MCS Value 11
Guard Interval 0.8 μs
Number of STAs 10, 20, 30, 40, 50, 60
UORA CW range [32, 1024]
Packet Size 1472 bytes
Figure 5represents the average latency in UL transmissions. For the proposed OFDMA
scheduler, the latency up to 30 STAs remains below the 10 ms but increases with the further
increase in the number of STAs. Comparatively, UORA has a significantly higher latency
caused by the large number of STAs leading to an increased probability of collisions due to
more STAs selecting the same RUs. The history-aware scheduler performs better than the
UORA and is surpassed by our proposed OFDMA scheduler.
1 0 2 0 3 0 4 0 5 0 6 0
0
2 0
4 0
6 0
8 0
100
A v e r a g e L a t e n c y ( m s )
N u m b e r o f S T A s
U O R A
H i s t o r y A w a r e
P r o p o s e d
Figure 5. Average UL latency. As the number of STAs increases, the average latency increases.
Figure 6illustrates the fairness of the allocation compared to the UORA and history
aware scheduler. We compute the fairness as the standard deviation of the average through-
put of each STA. Therefore, the lower the standard deviation is, fairer the algorithm will be.
As shown in the figure, the performance of the proposed scheduler is significantly better
than the other mechanisms. The allocation becomes fairer with the increasing number of
devices as the distinction in priority becomes lower, owing to a larger number of devices
having larger queues due to scheduling delays. However, at the lower number of STA,
the scheduling delay is lower; therefore the difference in priorities between the STAs is
comparatively significant. This leads to a relative unfair allocation of RUs at lower number
of STAs. Therefore, for STAs with similar traffic patterns, the proposed scheduler acts with
fairness. In the case of UORA, the allocations are random; therefore the allocation may
be lopsided and cause unfairness in the short term. For the history-aware mechanism,
the allocations are made based on the previous transmissions, which may cause some of
the STAs to be starved when they are not allocated an RU in the initial TXOPs. Moreover
it is compounded with every TXOP, as the priority is calculated using channel resource
allocation in time units. The throughput performance is shown in Figure 7. The throughput
of the proposed OFDMA scheduler is higher than UORA and the history-aware scheduler.

Sensors 2022,22, 6209 12 of 16
The proposed OFDMA scheduler can maintain a higher average throughput owing to its
knowledge of the queue sizes of the STAs. The throughput tends to saturate when the
number of STAs is increased beyond 40 STAs. However, it fares better compared to the
other two mechanisms.
1 0 2 0 3 0 4 0 5 0 6 0
0 . 0 0
0 . 0 1
0 . 0 2
0 . 0 3
0 . 0 4
0 . 0 5
0 . 0 6
0 . 0 7
0 . 0 8
F a i r n e s s
N u m b e r o f S T A s
U O R A
H i s t o r y A w a r e
P r o p o s e d
Figure 6. Fairness in RU allocation. The lower the value is , fairer the RU allocation will be.
1 0 2 0 3 0 4 0 5 0 6 0
0
5 0
100
150
200
250
Total Throughput (M bps)
N u m b e r o f S T A s
U O R A
H i s t o r y A w a r e
P r o p o s e d
Figure 7.
Average throughput achieved. The increase in the number of STAs increases the total
average throughput.
The evaluation and its analysis shows that scheduling the transmissions while con-
sidering the traffic categories can significantly improve the performance of a QoS-aware
network. The improvements, in terms of latency, can enable new applications, especially
in the healthcare sector where timely monitoring can be of critical importance. Compli-
ance with the QoS requirements can be ensured by exploiting the queue size information
without incurring any additional overhead. Different H-IoT applications impose different
QoS requirements in terms of delay as stated in [
18
]. Those requirements are used in our
evaluation to establish a level of satisfaction for the each application. Figure 8a–c represent
the satisfaction level in terms of delay for different H-IoT applications. The proportion of
traffic flows above the line at satisfaction value one satisfies the requirements of a given
application. Figure 8a shows the proportion of traffic flows that satisfies the requirements

Sensors 2022,22, 6209 13 of 16
for remote control applications such as haptic control for remote robotic operation using
the proposed OFDMA scheduler. Figure 8b,c depict the performance of the proposed
scheduler for remote monitoring applications. The performance of patient monitoring
applications and real-time health and activity monitoring is comfortably within the accept-
able range. With the increase in the number of STAs, the delay increases due to a limited
number of RUs which can be allocated. The MCS index determines the performance of a
Wi-Fi network while considering numerous factors such as the data rate, number of SS,
modulation scheme, error correction and channel width. Therefore, the impact of the MCS
index on latency performance is presented in Figure 9. It is observed that with the increase
in the MCS index value in the given 40 MHz channel and constant guard interval, the
latency performance improves. Higher MCS values correspond to higher order modulation
and error coding schemes.
1 0 2 0 3 0
0 . 0
0 . 2
0 . 4
0 . 6
0 . 8
1 . 0
1 . 2
1 . 4
1 . 6 R e m o t e O p e r a t i o n ( 2 m s )
N u m b e r o f S T A s
S a t i s f a c t i o n
(a)
1 0 2 0 3 0
0
1
2
3
4
5
6
7C r i t i c a l M o n i t o r i n g ( ~ 1 0 m s )
N u m b e r o f S T A s
S a t i s f a c t i o n
(b)
1 0 2 0 3 0
- 5
0
5
1 0
1 5
2 0
2 5
3 0
3 5
4 0 A c t i v i t y R e c o g n i t i o n ( ~ 5 0 m s )
N u m b e r o f S T A s
S a t i s f a c t i o n
(c)
Figure 8.
Satisfaction level of the proposed OFDMA scheduler in terms of delay for three different
application classes in H-IoT. The red line at 1 depicts the threshold value for meeting the satisfaction
levels of different applications. (
a
) Remote control applications with haptic control. (
b
) Critical
monitoring of health parameters in real-time. (c) Activity recognition using real-time video.

Sensors 2022,22, 6209 14 of 16
Figure 9. Impact of MCS index on the average latency.
6. Conclusions
The upcoming IEEE 802.11be standard is the next iteration in the IEEE 802.11 family
of WLAN standards. The introduction of OFDMA in the IEEE 802.11ax standard enabled
large deployments, thereby enabling more applications, especially for IoT. To support a
wider range of applications, additional enhancements are being proposed to the currently
developing amendment. Scheduling transmissions between the AP and STAs is a critical
operation that significantly affects the performance of the WLANs, especially for UL
transmissions in QoS-bound applications. The traffic in a WLAN is classified into various
ACs which have different priorities, and these priorities are enforced in EDCA but not
in OFDMA. The AP can act as a scheduler to allocate RUs for transmissions using their
priorities as an allocation criteria. Therefore, we propose scheduling the UL transmissions
based on the priority determined by the queue size of each AC and the weight associated
with each AC. We evaluate the performance of the proposed OFDMA scheduler using
the NS-3 simulator. We observe that the performance, in terms of latency, fairness and
throughput is superior compared to the state of the art. The proposed scheduler can
enable H-IoT applications, such as continuous health monitoring for real-time disease
diagnosis and AAL. We plan on extending this work by including artificial intelligence in
the scheduling mechanism for the upcoming IEEE 802.11be standard.
Author Contributions:
Conceptualization; methodology, writing—original draft preparation, Y.A.Q.;
software, Z., Y.A.Q.; writing—review and editing, A.N., A.M., E.G.-V.; supervision, S.W.K. All authors
have read and agreed to the published version of the manuscript.
Funding:
This research was supported in part by Basic Science Research Program through the National
Research Foundation of Korea(NRF) funded by the Ministry of Education (NRF-2021R1A6A1A03039493),
in part by the NRF grant funded by the Korea government (MSIT) (NRF-2022R1A2C1004401), and in
part by Spanish MCIN/AEI/10.13039/501100011033 through project PID2019-106808RA-I00.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.

Sensors 2022,22, 6209 15 of 16
References
1.
Ageing and Health. 2021. Available online: https://www.who.int/news-room/fact-sheets/detail/ageing-and- health (accessed
on 2 July 2022).
2.
Hasenauer, R.; Belviso, C.; Ehrenmueller, I. New Efficiency: Introducing Social Assistive Robots in Social Eldercare Organizations.
In Proceedings of the 2019 IEEE International Symposium on Innovation and Entrepreneurship (TEMS-ISIE), Hangzhou, China,
24–26 October 2019; pp. 1–6. [CrossRef]
3.
Marques, G.; Pitarma, R.; Garcia, N.M.; Pombo, N. Internet of Things Architectures, Technologies, Applications, Challenges, and
Future Directions for Enhanced Living Environments and Healthcare Systems: A Review. Electronics 2019,8, 1081. [CrossRef]
4.
Dayo, Z.A.; Aamir, M.; Dayo, S.A.; Khoso, I.A.; Soothar, P.; Sahito, F.; Zheng, T.; Hu, Z.; Guan, Y. A novel compact broadband and
radiation efficient antenna design for medical IoT healthcare system. Math. Biosci. Eng.
2022
,19, 3909–3927. [CrossRef] [PubMed]
5.
Ranieri, C.M.; MacLeod, S.; Dragone, M.; Vargas, P.A.; Romero, R.A.F. Activity Recognition for Ambient Assisted Living with
Videos, Inertial Units and Ambient Sensors. Sensors 2021,21, 768. [CrossRef] [PubMed]
6.
Cubo, J.; Nieto, A.; Pimentel, E. A Cloud-Based Internet of Things Platform for Ambient Assisted Living. Sensors
2014
,
14, 14070–14105. [CrossRef]
7.
Sarabia-Jácome, D.; Usach, R.; Palau, C.E.; Esteve, M. Highly-efficient fog-based deep learning AAL fall detection system. Internet
Things 2020,11, 100185. [CrossRef]
8.
Hajar, M.S.; Al-Kadri, M.O.; Kalutarage, H.K. A survey on wireless body area networks: Architecture, security challenges and
research opportunities. Comput. Secur. 2021,104, 102211. [CrossRef]
9.
Islam, S.M.R.; Kwak, D.; Kabir, M.H.; Hossain, M.; Kwak, K.S. The Internet of Things for Health Care: A Comprehensive Survey.
IEEE Access 2015,3, 678–708. [CrossRef]
10.
Qadri, Y.A.; Nauman, A.; Zikria, Y.B.; Vasilakos, A.V.; Kim, S.W. The Future of Healthcare Internet of Things: A Survey of
Emerging Technologies. IEEE Commun. Surv. Tutor. 2020,22, 1121–1167. [CrossRef]
11.
Avallone, S.; Imputato, P.; Redieteab, G.; Ghosh, C.; Roy, S. Will OFDMA Improve the Performance of 802.11 WiFi Networks?
IEEE Wirel. Commun. 2021,28, 100–107. [CrossRef]
12.
PAR Details. 2019. Available online: https://development.standards.ieee.org/myproject-web/public/view.html#pardetail/6886
(accessed on 13 July 2022).
13.
Bankov, D.; Khorov, E.; Lyakhov, A.; Sandal, M. Enabling real-time applications in Wi-Fi networks. Int. J. Distrib. Sens. Netw.
2019,15, 1550147719845312. [CrossRef]
14.
Gokhale, V.; Eid, M.; Kroep, K.; Prasad, R.V.; Rao, V.S. Toward Enabling High-Five Over WiFi: A Tactile Internet Paradigm. IEEE
Commun. Mag. 2021,59, 90–96. [CrossRef]
15.
IEEE Std 802.11ax-2021; IEEE 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 1: Enhancements for High-Efficiency WLAN. IEEE: Piscataway, NJ,
USA, 2021. [CrossRef]
16.
Cavalcanti, D.; Venkatesan, G. 802.1 TSN over 802.11 with Updates from Developments in 802.11be. 2022. Available online: https:
//www.ieee802.org/1/files/public/docs2020/new-Cavalcanti-802-1TSN-over-802-11-1120-v02.pdf (accessed on 13 July 2022).
17.
Shukla, S.; Hassan, M.F.; Khan, M.K.; Jung, L.T.; Awang, A. An analytical model to minimize the latency in healthcare
internet-of-things in fog computing environment. PLOS ONE 2019,14, e0224934. [CrossRef] [PubMed]
18.
Qureshi, H.N.; Manalastas, M.; Ijaz, A.; Imran, A.; Liu, Y.; Al Kalaa, M.O. Communication Requirements in 5G-Enabled Healthcare
Applications: Review and Considerations. Healthcare 2022,10, 293. [CrossRef] [PubMed]
19.
Rodrigues, V.F.; Righi, R.d.R.; da Costa, C.A.; Antunes, R.S. Smart Hospitals and IoT Sensors: Why Is QoS Essential Here? J. Sens.
Actuator Netw. 2022,11, 33. [CrossRef]
20.
Peng, D.; Sun, L.; Zhou, R.; Wang, Y. Study QoS-aware Fog Computing for Disease Diagnosis and Prognosis. Mob. Netw. Appl.
2022,27, 1572–8153. [CrossRef]
21.
Zaman, U.; Imran; Mehmood, F.; Iqbal, N.; Kim, J.; Ibrahim, M. Towards Secure and Intelligent Internet of Health Things: A
Survey of Enabling Technologies and Applications. Electronics 2022,11, 1893. [CrossRef]
22.
Deng, C.; Fang, X.; Han, X.; Wang, X.; Yan, L.; He, R.; Long, Y.; Guo, Y. IEEE 802.11be Wi-Fi 7: New challenges and opportunities.
IEEE Commun. Surv. Tutor. 2020,22, 2136–2166. [CrossRef]
23.
L’opez-P’erez, D.; Garcia-Rodriguez, A.; Galati-Giordano, L.; Kasslin, M.; Doppler, K. IEEE 802.11 be extremely high throughput:
The next generation of Wi-Fi technology beyond 802.11 ax. IEEE Commun. Mag. 2019,57, 113–119. [CrossRef]
24.
Park, E.; Lim, D.; Kim, J.; Choi, J. Tone Plan Discussion. Document: IEEE 802.11-19/1066r3. 2019. Available online: https:
//mentor.ieee.org/802.11/dcn/19/11-19-1066-03-00be-tone- plan-discussion.pptx (accessed on 15 August 2022)
25.
Park, M.; Lee, W.B.; Wu, T.; Cariou, L.; Stacey, R. Beyond 802.11ax-Throughput Enhancement Utilizing Multi-bands across 2.4 5 6
GHz Bands. 2018. Available online: https://mentor.ieee.org/802.11/dcn/18/11-18-0857-00-0wng-beyond-802-11ax-throughput-
enhancement-utilizing-multi-bands-across-2-4-5- 6ghz-bands.pptx (accessed on 15 August 2022) .
26.
Khorov, E.; Levitsky, I.; Akyildiz, I.F. Current Status and Directions of IEEE 802.11be, the Future Wi-Fi 7. IEEE Access
2020
,
8, 88664–88688. [CrossRef]
27.
Yang, M.; Li, B. Survey and Perspective on Extremely High Throughput (EHT) WLAN — IEEE 802.11be. Mob. Netw. Appl.
2020
,
25, 1765–1780. [CrossRef]

Sensors 2022,22, 6209 16 of 16
28.
IEEE Std 802.11-1997; IEEE Standard for Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications.
IEEE: Piscataway, NJ, USA, 1997. [CrossRef]
29.
Bankov, D.; Didenko, A.; Khorov, E.; Lyakhov, A. OFDMA Uplink Scheduling in IEEE 802.11ax Networks. In Proceedings of the
2018 IEEE International Conference on Communications (ICC), Kansas City, MO, USA, 30 July 2018; pp. 1–6. [CrossRef]
30.
Avdotin, E.; Bankov, D.; Khorov, E.; Lyakhov, A. OFDMA Resource Allocation for Real-Time Applications in IEEE 802.11ax Net-
works. In Proceedings of the 2019 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom),
Sochi, Russia, 3-6 June 2019; pp. 1–3. [CrossRef]
31.
Naik, G.; Bhattarai, S.; Park, J. Performance Analysis of Uplink Multi-User OFDMA in IEEE 802.11ax. In Proceedings of the 2018
IEEE International Conference on Communications (ICC), Kansas City, MO, USA, 30 July 2018; pp. 1–6. [CrossRef]
32.
Kosek-Szott, K.; Domino, K. An Efficient Backoff Procedure for IEEE 802.11ax Uplink OFDMA-Based Random Access. IEEE
Access 2022,10, 8855–8863. [CrossRef]
33.
Lanante, L.; Ghosh, C.; Roy, S. Hybrid OFDMA Random Access With Resource Unit Sensing for Next-Gen 802.11ax WLANs.
IEEE Trans. Mob. Comput. 2021,20, 3338–3350. [CrossRef]
34.
IEEE Std 802.11e-2005; Amendment to IEEE Std 802.11, 1999 Edition (Reaff 2003), IEEE Standard for Information Technology–Local
and Metropolitan Area Networks–Specific Requirements–Part 11: Wireless LAN Medium Access Control (MAC) and Physical
Layer (PHY) Specifications—Amendment 8: Medium Access Control (MAC) Quality of Service Enhancements. IEEE: Piscataway,
NJ, USA, 2005. [CrossRef]
35.
Bajaj, T. Vector Emplace() Function in C++ STL. Available online: https://www.geeksforgeeks.org/vector-emplace-function-in-
c-stl/ (accessed on 15 August 2022 ).