# FEDERATED SPATIAL REUSE OPTIMIZATION IN NEXT-GENERATION DECENTRALIZED IEEE
802.11 WLANS

Francesc Wilhelmi  CTTC (Spain)  Jernej Hribar  CONNECT Centre, Trinity
College Dublin (Ireland)  Selim F. Yilmaz  Imperial College London (United
Kingdom)  Emre Ozfatura  Imperial College London (United Kingdom)  Kerem
Ozfatura  Imperial College London (United Kingdom)  Ozlem Yildiz  New York
University (USA)  Deniz Gündüz  Imperial College London (United Kingdom)
University of Modena and Reggio Emilia (Italy)  Hao Chen  Xiamen University
(China)  Xiaoying Ye  Xiamen University (China)  Lizhao You  Xiamen University
(China)  Yulin Shao  Imperial College London (United Kingdom)  Paolo Dini
CTTC (Spain)  Boris Bellalta  Universitat Pompeu Fabra (Spain)

(NOTE: Corresponding author: Francesc Wilhelmi<EMAIL_ADDRESS>
)

###### Abstract

As wireless standards evolve, more complex functionalities are introduced to
address the increasing requirements in terms of throughput, latency, security,
and efficiency. To unleash the potential of such new features, artificial
intelligence (AI) and machine learning (ML) are currently being exploited for
deriving models and protocols from data, rather than by hand-programming. In
this paper, we explore the feasibility of applying ML in next-generation
wireless local area networks (WLANs). More specifically, we focus on the IEEE
802.11ax spatial reuse (SR) problem and predict its performance through
federated learning (FL) models. The set of FL solutions overviewed in this
work is part of the 2021 International Telecommunication Union (ITU) AI for 5G
Challenge.

Keywords – Federated learning, IEEE 802.11ax, ITU Challenge 2021, machine
learning, network simulator, spatial reuse

## 1\. Introduction

Wireless networks are evolving towards artificial intelligence (AI) / machine
learning (ML)-driven systems able to address the overwhelming requirements of
future mobile communications [1, 2], namely the fifth generation (5G), beyond
5G (B5G), and the sixth generation (6G). The application of ML for networking
can be found at different communication layers and parts of a network, e.g.,
network management to drive the self-organizing networks (SON) paradigm [3],
optimization of the medium access control (MAC) layer in decentralized channel
access [4], or AI-native physical communication protocols [5, 6]. The fact is
that AI/ML can leverage the vast amount of network and user data to generate
new knowledge that allows improving the network performance and, hence, making
progress in the development of novel network applications such as those based
on extended reality.

Nevertheless, the use of ML in communications also raises concerns of
different nature. First, ML-based solutions typically require a lot of energy
for training complex models (e.g., neural networks) and high bandwidth for
exchanging training data, which typically needs to be centralized to a single
point. Moreover, the massive usage of networking data for ML may threaten
security and users’ privacy. The privacy issue may be exacerbated in
decentralized networks such as IEEE 802.11 wireless local area networks
(WLANs), whereby the lack of a central network manager may make inter-WLAN
interactions unreliable.

To address some of the challenges posed by traditional ML training, Federated
Learning (FL) optimization was introduced in [7] as a distributed training
paradigm that allows keeping the data at its source. Since then, a significant
number of FL applications have flourished across different fields, such as
medicine [8], autonomous driving [9], UAV-based wireless networks [10]. FL has
become attractive to foster collaboration among different parties interested
in solving a common problem. Under the management of a central server
(typically, a neutral entity), FL participants contribute to building a common
ML model, by sharing model weights generated using its own local data, rather
than forwarding raw data for centralized training.

In this paper, we study the application of FL models to the IEEE 802.11
spatial reuse (SR) problem, which aims to enhance spectral efficiency by
adjusting the devices’ carrier sense area to increase the number of concurrent
transmissions in overlapping deployments. IEEE WLANs are an important part of
the B5G ecosystem as it represents a cost-effective but high-performance
solution for the access network. In particular, we overview the output of the
problem statement entitled “ITU-ML5G-PS-004: Federated Learning for Spatial
Reuse in a multi-BSS (Basic Service Set) scenario”, which was part of the 2021
International Telecommunication Union (ITU) AI for 5G Challenge [11].111The
ITU AI/ML challenge is a global competition that gathers professionals,
researchers, practitioners, and students from all around the globe to solve
relevant problems on ML for communications. The purpose of the challenge was
the exploration of federated solutions to predict the performance of IEEE
802.11ax (11ax) networks applying SR. Such a performance prediction solution
is called to be an essential part of ML-assisted networks, which overarching
goal is optimization. To address the performance prediction problem, a dataset
with simulated measurements on crowded 11ax deployments applying SR was
provided, which was used to develop the FL solutions presented in this paper.
The usage of simulated data for enriching training datasets is another
relevant topic for enabling ML in communications [12].

The main contributions of this paper are as follows:

  * •

We overview the SR technology for both 11ax and future amendments and propose
the usage of FL to address it.

  * •

We provide a dataset with 11ax SR measurements for next-generation WLANs. The
dataset is open and can be accessed at [13].

  * •

We overview the set of FL solutions proposed by the participants of the 2021
ITU AI for 5G Challenge to predict the performance of novel IEEE 802.11ax SR
WLANs. Table 1 briefly summarizes the proposed models, as well as the main
motivation behind them.

Table 1: Summary of the ML models proposed by the participants of the
challenge.

Team | Proposed Model | Motivation | Ref.
---|---|---|---
FederationS |  | DNN with two
---
parallel branches
| Exploit relationships among
---
training features
[14]
| FedIPC
---
| NN with a Multi-Output
---
Regression Objective
| Take advantage of knowledge
---
on wireless operation
[15]
| WirelessAI
---
| CNN with FCNN
---
| Exploit graph representations
---
in wireless networks
[16]

The rest of the paper is structured as follows. Section 2 introduces the SR
problem in 11ax and future WLANs. Section 3 provides some basics on FL with
special emphasis on its applications in networking. Section 4 overviews the
provided SR dataset for training ML models. The solutions proposed by the
challenge participants are described in detail in Section 5 and evaluated in
Section 6. Section 7 concludes the paper with some remarks and future
directions.

## 2\. Spatial Reuse in 802.11ax WLANs: overview and research gaps

IEEE 802.11 technology, commonly known as Wi-Fi, is one of the most popular
solutions for the access network due to its ease of deployment and low cost
(it operates on unlicensed bands). However, its fundamental operation is based
on carrier sense multiple access (CSMA), whose performance is well known to
degrade when dealing with a large number of concurrent users [17]. To address
the issues raised by network density and to meet the increasingly strict
requirements posed by next-generation applications (e.g., virtual reality),
802.11 amendments introduce novel functionalities and protocol enhancements.
For instance, standards 802.11n (2009) and 802.11ac (2013) provided high
throughput (HT) and very high throughput (VHT) devices by including, for
instance, the application channel bonding (CB), whereby basic channels could
be aggregated to increase the capacity of a single transmission.

As for the SR operation [18], it was recently introduced by the IEEE 802.11ax
(2021) standard [19] to increase the number of parallel transmissions in
overlapping basic service sets (OBSS). Among other features like orthogonal
frequency division multiple access (OFDMA), or downlink/uplink multi-user
multiple input-multiple-output (MU-MIMO), SR aims at enhancing the performance
and efficiency. To do so, it provides two different operational modes:

  1. 1.

OBSS Packet Detect-based SR (OBSS/PD-based SR).

  2. 2.

Parametrized Spatial Reuse (PSR).

The main difference between the two mechanisms lies in the way SR transmission
opportunities (TXOPs) are detected by devices implementing them. While
OBSS/PD-based SR operates in the downlink, PSR is designed for the uplink. In
what follows, we focus on OBSS/PD, which has gained more interest and is under
consideration for evolution in the future amendments, such as the IEEE
802.11be (11be) [20]. A comprehensive overview of these two mechanisms can be
found in [18].

In essence, OBSS/PD-based SR allows devices to transmit in parallel with
others that gained channel access beforehand. To do so, a new OBSS/PD
threshold is defined to be applied when an incoming detected transmission
marks the radio channel as busy through clear channel assessment (CCA)
operation. CCA allows overlapping devices to share a common channel and is
triggered when the preamble of a Wi-Fi transmission is identified. Provided
that the OBSS/PD threshold allows initiating a new SR transmission, a transmit
power limitation must be applied so that the generated interference does not
affect the original transmission.

(a)

(b)

Fig. 1: IEEE 802.11ax OBSS/PD-based SR operation: (a) signal reception areas,
(b) diagram of packet exchange.

The OBSS/PD SR operation is illustrated in Fig. 1 for two overlapping access
points (APs), AP${}_{\text{A}}$ and AP${}_{\text{B}}$. As shown in Fig. 1(a),
AP${}_{\text{A}}$ detects the signals from all the considered devices
(represented by the red area), including AP${}_{\text{B}}$ and station B
(STA${}_{\text{B}}$), which belong to a different BSS. In particular,
AP${}_{\text{B}}$ is inside the carrier sense area of AP${}_{\text{A}}$
(represented by the gray area), so they both must contend for the channel
whenever the other starts a transmission (e.g., to its associated STAs).
Nevertheless, thanks to the OBSS/PD-based SR operation, AP${}_{\text{A}}$ can
ignore AP${}_{\text{B}}$’s transmissions when applying the OBSS/PD threshold
(represented by the green area). At the packet level (shown in Fig. 1(b)),
AP${}_{\text{A}}$ starts decoding the preamble of a new transmission from
AP${}_{\text{B}}$, which has initially gained the access to the medium using
CSMA with collision avoidance (CSMA/CA). From the preamble reception,
AP${}_{\text{A}}$ determines that the channel is busy at the MAC layer due to
the CCA operation. But, using the SR mechanism, AP${}_{\text{A}}$ identifies
an SR TXOP because the incoming signal is below the OBSS/PD threshold. Hence,
AP${}_{\text{A}}$ can initiate a transmission before AP${}_{\text{B}}$ leaves
the channel, provided that a transmit power restriction is applied, denoted by
$\text{TX\\_PWR}_{\text{max}}$, for the sake of not affecting
AP${}_{\text{B}}$’s transmission [21]:

$\text{TX\\_PWR}_{\text{max}}=\text{TX\\_PWR}_{\text{ref}}-(\text{OBSS/PD}-\text{OBSS/PD}_{\text{min}}),$
((1))

where $\text{TX\\_PWR}_{\text{ref}}$ is the transmit power reference (set to
21 dBm or 25 dBm, depending on the device’s antenna capabilities), OBSS/PD is
the selected OBSS/PD threshold for detecting SR TXOPs, and
$\text{OBSS/PD}_{\text{min}}$ is the minimum OBSS/PD threshold (fixed to -82
dBm).

While SR promises to enhance spectral efficiency in dense OBSS deployments,
its actual performance is hindered by the proper selection of the OBSS/PD
threshold, which may not be trivial due to the complex inter-device
interactions in a WLAN. The fact is that the OBSS/PD-based SR operation is a
decentralized mechanism that only considers the interactions between principal
transmitters (i.e., devices gaining access to the channel for transmitting),
but does not account for either the interference at the recipients of such
transmissions or the impact of uplink control frames (e.g., acknowledgment
packets). Since the standard does not provide any method for selecting the
proper OBSS/PD threshold, there is an imperative need for finding effective
mechanisms to leverage the SR operation.

ML, in this context, is considered a promising tool to capture the complex
interactions among IEEE 802.11 devices applying SR. In general, ML has been
applied to a plethora of problems in IEEE 802.11 networks, including PHY
optimization (rate selection [22], resource allocation [23]), assisting
management operations (e.g., AP selection and handover [24], channel band
selection [25]), or supporting novel features like MU-MIMO or channel bonding
with enhanced monitoring, analytics, and decision-making [26, 27]. For further
details on ML application to Wi-Fi, we refer the interested reader to the
comprehensive survey in [28].

In the particular case of SR, most of the literature has so far focused on
reinforcement learning (RL) and online learning techniques, whereby agents
attempt to learn the best OBSS/PD configuration sequentially. In [29, 30], the
authors modeled the decentralized SR problem as multi-armed bandits (MAB), an
online learning framework whereby agents attempt to address the exploration-
exploitation trade-off. While [29] studied the problem by using selfish
rewards in a competitive environment, [30] considered shared rewards for the
sake of maximizing fairness. Other RL-based approaches can be found in [31]
and [32].

The online learning paradigm turns out to be a cost-effective solution to the
decentralized SR problem thanks to its ability for solving complex partial
information problems. In addition, WLANs typically experience a high
variability both in terms of devices’ mobility and activation/deactivation, so
past learned information may become easily outdated. However, as shown in
[30], online learning may have some pitfalls when applied to dense WLANs,
mainly raised by the high action-decision space, the non-stationarity of
agents’ rewards in competitive settings, or the complexity of finding a proper
shared reward that enables maximizing the overall network performance.

For those reasons, in this paper, we focus on the suitability of supervised
learning methods, mostly based on deep learning (DL), for the SR problem in
WLANs. To the best of our knowledge, this approach has not been studied before
in the context of SR. A centralized DL-based method was proposed in [33] to
jointly select the transmission power and the CCA, but not in the context of
11ax SR operation. DL was also applied in [34] to address the channel bonding
problem in dense WLANs. This and other DL solutions for the dynamic channel
bonding problem in IEEE 802.11ax WLANs were overviewed in [35].

We note that the overviewed works on DL consider centralized approaches, which
require data to be gathered at a single point for training a static model,
which is then used homogeneously across all the AI-enabled devices.
Nevertheless, in practice, some deployments (e.g., residential WLANs) may have
limitations in terms of computation, storage, or communication capabilities
(for instance, low-throughput connections, intermittent availability).
Moreover, separate WLAN deployments can be substantially different, thus
requiring specialized models (rather than general ones). To address these
limitations of centralized learning on heterogeneous deployments, we focus on
the FL paradigm, introduced in the next section.

## 3\. An Introduction to Federated Learning for Networking

The FL optimization paradigm was first introduced in [36] to address some
critical issues of traditional centralized ML mechanisms. In FL, training is
done at end devices (or clients), which do not share their training data with
others. Instead, ML model updates are provided and aggregated under the
management of a typically central server. By removing the procedures related
to data exchange, FL decreases the communication overhead and enhances user
privacy and security. Moreover, FL is an appealing solution for dealing with
heterogeneous sets of clients, thus allowing to create specialized models
according to clients’ characteristics. With that, FL has the potential to
revolutionize ML implementations, bringing them closer to practical
applications and use cases. Many examples of FL have emerged in recent years,
including, but not limited to, in medicine [37], finance [38], industry 4.0
[39], or telecommunications [40, 41].

In the telecommunications realm, novel ML solutions require handling a vast
amount of data, often highly distributed across the network. These kinds of
resource-demanding applications may threaten the stability of the network on
the one hand and may experience low performance due to the communication
bottleneck on the other hand. FL can potentially alleviate some of these
issues by reducing the overheads generated by the ML operation while providing
good performance. FL also contributes to enhancing privacy, which is a
critical issue in communications. FL applications in communications [42]
include autonomous driving [43], unmanned aerial vehicle (UAV)-based wireless
networks [44], edge computing [45], physical layer optimization [46], or
Internet-of-Things (IoT) intelligence [47].

The generic FL algorithm operates iteratively, generating a global model
update at each iteration with the help of a subset of clients. Each
algorithm’s iteration follows the following general steps (see Fig. 2):

  1. 1.

A set of $\mathcal{K}=\\{1,2,...,K\\}$ clients download the current model
parameters, $w_{t}$, from the central server (also called the parameter
server).

  2. 2.

Clients perform training in parallel using their local datasets
$\mathcal{D}^{(k)}$ (with size $N^{(k)}$) and update the model weights
accordingly, denoted by $w^{(k)}\in\mathbb{R}^{d}$.

  3. 3.

The server pulls the model updates from the participating clients (a subset of
clients may be selected in each FL round for the sake of performance) and
orchestrates weight aggregation to generate an updated global model
$w_{t+1}\in\mathbb{R}^{d}$.

  4. 4.

Above steps are repeated until convergence, i.e., until a time horizon is
completed or a certain accuracy goal is met.

Fig. 2: FL operation with WLAN contexts.

At this point, it is important to highlight the federated averaging (FedAvg)
method [48], which is based on stochastic gradient descent (SGD) optimization
and performs well for non-convex problems. In FedAvg (shown in Alg. 1),
clients perform several batch updates at each iteration using local data to
update the global model parameters. Unlike in classical federated stochastic
gradient descent (FedSGD), where gradients are exchanged, FedAvg considers
sharing model updates (e.g., the parameters of a neural network). By applying
multiple rounds of training, FL seeks to minimize a global finite-sum cost
function $l(w)$ by optimizing the global model parameters $w$:

$\min_{w\in\mathbb{R}^{d}}l(w)=\min_{w\in\mathbb{R}^{d}}\sum_{k=1}^{K}\frac{N^{(k)}}{N}l^{(k)}(w,\mathcal{D}^{(k)}),$
((2))

where $l^{(k)}(w,\mathcal{D}^{(k)})$ is the loss experienced by client $k$
when using the global model $w$ on its local data, and $N$ is the total size
of the distributed dataset, i.e., $N=\sum_{\forall k\in\mathcal{K}}N^{(k)}$.

To compute local updates, clients run $E$ epochs of SGD based on the target
local loss function $l^{(k)}$ and the batch size $B$ applied to local data
$\mathcal{D}^{(k)}$. Using a learning rate $\eta$, local updates are obtained
by:

$w_{t+1}^{(k)}\leftarrow w_{t}^{(k)}-\eta\nabla
l^{(k)}(w_{t},\mathcal{D}^{(k)})$ ((3))

Finally, being $\eta$ the learning rate, the server aggregates clients’
weights based on the importance $\alpha_{k}$ assigned to each client which may
be set according to local dataset lengths (as indicated in Eq. ((2))):

$w_{t+1}=\sum_{k=1}^{K}\alpha^{(k)}w_{t+1}^{(k)}$ ((4))

Algorithm 1 Federated Averaging (FedAvg)

1:for $t=1,2,\ldots,T$ do

2: for $k\in\mathbb{K}_{tr}$ do in parallel

3: Pull $\boldsymbol{w}_{t}$ from central server:
$\boldsymbol{w}^{(k)}_{t,0}=\boldsymbol{w}_{t}$

4: for $e=1,\ldots,E$ do

5: Update model:
$\boldsymbol{w}^{(k)}_{t,e}=\boldsymbol{w}_{t,e}^{(k)}-\eta_{t}\nabla
l^{(k)}_{t,e}$

6: end for

7: Push $\boldsymbol{w}_{t+1}^{(k)}\leftarrow\boldsymbol{w}_{t,E}^{(k)}$

8: end for

9: FedAvg:
$\boldsymbol{w}_{t+1}=\frac{1}{|\mathbb{K}_{tr}|}\sum_{k\in\mathbb{K}_{tr}}\boldsymbol{w}_{t+1}^{(k)}$

10:end for

Beyond FedAvg, other optimization mechanisms have been proposed to improve the
convergence and efficiency of FL [49, 50]. For further details on FL, we refer
the interested reader to the works in [51, 52], and to [53] for the
implementations of FL over wireless networks in particular.

## 4\. Open simulated dataset on IEEE 802.11ax SR

Supervised ML methods typically require a significant amount of high-quality
data to perform well. Training data is usually obtained either from network
activity [54] or from measurement campaigns [55]. However, obtaining real
traces from networks can be challenging due to proprietary limitations (data
owners are reluctant to share their assets), data privacy issues (most network
data is generated by final users), or difficulties in obtaining data from a
rich set of situations (anomalies are hard to reproduce and identify). In this
sense, the usage of synthetic datasets for model training is gaining attention
[12]. Such datasets can be obtained, for instance, from network simulators
(e.g., ns-3, OMNET++, OPNET). Simulators are a cost-effective solution for
generating comprehensive datasets. Some prominent examples of synthetic
datasets oriented to ML training can be found in [56, 57].

As for the provided dataset on 11ax SR [13], it has been generated with
Komondor [58], an open-source IEEE 802.11ax-oriented simulator that includes
features like channel bonding or SR. Komondor does not implement the targeted
functionalities, but its execution is also lightweight, thus allowing for
generating large datasets corresponding to massive WLAN deployments.

The dataset contains both training and test files, which include the results
obtained from several simulated random deployments applying 11ax SR (see the
example random deployment in Fig. 3). More specifically, a set of three
baseline scenarios was considered to represent different types of deployments.
Considering the features in each type of scenario (e.g., maximum number of
STAs per BSS, minimum distance between APs), 1,000 random deployments of each
type were generated for training. Each simulated deployment corresponds to a
context $k\in\mathbb{K}$, where the BSS of interest (namely,
BSS${}_{\text{A}}$) is used as a client for FL optimization. To enrich
contexts with data, each BSS includes information for each possible OBSS/PD
configuration $\tau$ (i.e., from -82 dBm to -62 dBm with 1 dBm precision).
Finally, for the test dataset, 1,000 more deployments were simulated using
more relaxed constraints. In this case, a single random OBSS/PD configuration
was selected in each deployment. Table 2 provides an overview of the entire
dataset.

Fig. 3: Example of a simulated WLAN deployment. Table 2: Summary of the
scenarios of the dataset.

| Sce id | Num. APs | Num. STAs | d_min(APs) |  | Context
---
variations
Training | training1 | 2-6 | 1 | 10 m | None
training2 | 1-4 | 10 m | None
training3 | 1-4 | None |  | Up to 20
---
locations
Test | test | 2-4 | None | None

Training scenario training1 considers BSSs with only one STA, which is useful
to minimize the impact of uplink transmissions, thus allowing to focus on
inter-AP interactions only. In contrast, scenarios training2 and training3
consider up to 4 STAs per AP, which contribute generating more traffic in the
uplink. As for the minimum distance between APs (d${}_{\text{min}}$), it is
set to 10 m in scenarios training1 and training2, whereas the rest have no
limitation. Furthermore, contexts in scenario training3 contain richer
datasets by simulating variations of the same deployments using different STA
locations.

The information included in simulated files is divided into features and
label. Concerning features for training, we find the following key elements:

  1. 1.

Type of node: indicates whether the node is an AP or an STA.

  2. 2.

BSS id: identifier of the BSS to which the node belongs.

  3. 3.

Node location: {x,y,z} position of nodes in the map.

  4. 4.

Primary channel: main frequency channel used for transmitting and for carrier
sensing.

  5. 5.

Transmit power: default transmit power used for transmitting frames.

  6. 6.

OBSS/PD threshold: sensitivity used within the OBSS/PD-based SR operation.

  7. 7.

Received signal strength indicator (RSSI): average signal quality experienced
by STAs during reception phases.

  8. 8.

Inter-BSS interference: average power sensed from devices belonging to other
BSSs.

  9. 9.

Signal-to-interference-plus-noise ratio (SINR): average SINR experienced by
STAs when receiving data from their AP.

Fig. 4: Correlation between different input and output variables of the
dataset.

It is worth noting that most of the extracted information is typically
obtained on a continuous basis in a real system. Indeed, the RSSI, SINR and
throughput measurements can be reported periodically by STAs. Interference
powers can be measured during the listen-before-transmit (LBT) phase at the
AP, employing multi-antenna processing techniques to separate the different
interfering sources. In addition, time-of-arrival (TOA) ranging techniques can
determine the distance between STAs and APs.

As for the label, we provide the throughput $\gamma^{(k)}_{j,\tau}$ obtained
by each STA $j$ in context $k$ during the simulation, provided that the
OBSS/PD configuration $\tau$ is used. Predicting the throughput is the goal of
the implemented FL solutions described in the next section. Notice, as well,
that other Key Performance Indicators (KPIs) such as the average delay or the
number of SR TXOPs could have been considered.

To conclude this section, we show the correlation matrix between input and
output variables in Fig. 4, which is later used as a motivation for some of
the proposed ML solutions. Correlation values close to $0$ indicate a lack of
relations and structure between the data corresponding to these variables,
while correlation values close to $-1$ and $+1$ indicate a perfect negative
and positive correlation between variables, respectively. Finally, Fig. 5
shows the histogram of some of the most relevant features from the entire
dataset.

Fig. 5: Histogram of relevant features from the dataset.

## 5\. Federated Learning Solutions for Spatial Reuse

In this section, we describe the solutions proposed by the participants of the
2021 ITU AI for 5G Challenge: FederationS, FedIPC, and WirelessAI.

### 5.1 FederationS

This solution is designed in three stages. In the first stage, we analyze and
pre-process available datasets. In the second stage, using gained insights
from the data analysis, we define a deep neural network (DNN) model running in
each client. In the final stage, we describe the proposed FL algorithm.

In the data analysis stage, we consider scenarios training2 and training3,
containing $2000$ different IEEE 802.11ax deployments (see Table 2 for further
details). We extract several features available in the simulator’s output
files from these scenarios, namely the OBSS/PD configuration, the RSSI, the
interference at the reference AP from other APs, the SINR, and the throughput
of each STA. We also obtain additional information using available data in the
simulator’s input files. In particular, we extract the coordinates of APs and
STAs to compute the Euclidean distances among them. In addition, we obtain the
number of STA served by the reference AP and the number of interfering APs.

To obtain the final dataset to be used to train our model, we pre-process the
data through different steps. First, we clean the input and output data parsed
from the simulator files removing all non-numerical values from the dataset.
Then, we arrange the data of each STA to form 1-D vectors with $11$ numerical
entries used as the input of the model and containing all measurements and
system parameters. Conversely, we define the STA throughput as the target
variable and output of the model. To note that the features present entirely
different ranges between maximum and minimum values and are expressed with
different units of measurements, e.g. dBm for RSSI and interference power, dB
for SINR, and meters for distances. To balance each feature’s contribution to
the overall model predictions, we re-scale the features with the Min-Max
normalization method that transforms all features’ in the range $[0,1]$.
Finally, when input data are missing, like when the number of interfering AP
reported is less than the minimum recorded according to our system settings,
we assign those values with $0$s. The activation function that we will explain
later is chosen to keep neurons inactive when $0$s are present at the input.

To decide the ML method to be used, we make the following two main
observations from the correlation analysis done in Fig. 4:

  1. 1.

Most features show a strong positive or negative correlation with the output
variable (throughput). As expected, only the OBSS/PD feature does not directly
affect the throughput. Otherwise, the problem would be trivial to model.
Indeed, the OBSS/PD value is correlated to the RSSI, which affects SINR and
throughput variables, showing that relationships between input and output
values of the system are not straightforward to characterize with domain-based
models. This justifies the adoption of DNN, which are extensively used for
their capabilities to model nonlinear relationships.

  2. 2.

Two regions depicted with lighter colors at the top left and at the bottom
right of the correlation matrix identify two groups of features that show a
strong positive correlation between input variables. Thus, in the DNN
architecture design, these inputs of the model need to be fully connected. In
contrast, the two regions at the top right and bottom left are characterized
by elements with close to zero correlation values, meaning that the
relationship between features is weak. Therefore, these connections are
expected to bring a low contribution to the predictions and can be dropped in
the DNN architecture design.

Based on this, in the following design stage, we model DNN architecture as
represented in Fig. 6. First, we split the DNN model into two parallel
branches. The inputs of the first branch are the features constituting the
first block, i.e. RSSI, SINR, distance STA-AP, and OBSS-PD threshold. At the
same time, features like the number of STA, the power received from
interfering AP, and the number of interfering AP form the second block of
features and are used as input of the second branch. The input layers are
followed by two hidden layers defined for each branch separately. We use a
concatenation layer to merge the output of these two branches. The result of
the concatenation is then used as input of two additional hidden layers, which
are connected to the output layer of the model. We adopt the hyperbolic
tangent (_tanh_) activation function to provide positive and negative outputs
and keep neurons inactive when the inputs are $0$s. Finally, we add dropout
layers after each layer before the output layer to reduce overfitting.

[width=.5]tikz_figures/ann_small

Fig. 6: Visualization of FederationS’ DNN structure.

As for the FL solution, it is based on the implementation outlined in [59].
However, the FederationS algorithm combines the trained weights in a novel
way, which is designed specifically for the problem of performance prediction
in WLANs. Moreover, the aggregation process at the central server is tailored
to maximise the gain from contexts with more data samples. In particular, our
proposed FL solution follows the steps described in Algorithm 2.

Algorithm 2 FederationS solution.

1:Init set $\mathbb{K}$, i.e., init $K$ contexts with data samples

2:From $\mathbb{K}$, select $N_{eval}$ to create $\mathbb{K}_{val}$

3:Create a new set of contexts
$\mathbb{K}_{tr}=\mathbb{K}\cap\mathbb{K}_{val}$

4:Server initializes model parameters $\theta_{0}$ and $W_{0}$

5:The server transmits $\theta^{(k)}_{0},w^{(k)}_{0}$ to $k$-th contexts

6:for communication epoch $t=1,2,...,T$ do

7: Rand. select $N_{tr}$ contexts from $\mathbb{K}_{tr}$ to get
$\mathbb{K}_{ep}$

8: for $i$-th context in $\mathbb{K}_{ep}$ do

9: Split samples in $\beta$ ($\frac{n_{i}}{B}$ batches of size $B$)

10: Where $n^{(i)}$ is the number of data samples

11: for batch $b$ in $\beta$ do

12: $\theta^{(i)}_{t}\leftarrow\theta^{(i)}_{t-1}-\eta\nabla
l(\theta^{(i)}_{t-1};b)$

13: $w^{(i)}_{t}\leftarrow w^{(i)}_{t-1}-\eta\nabla l(w^{(i)}_{t-1};b)$

14: end for

15: Determine weight $\alpha^{(i)}=n^{(i)}/N_{STA}$

16: Transmit $\theta^{(i)}_{t}$, $w^{(i)}_{t}$, and $\alpha^{(i)}$ to central
server

17: end for

18: Calculate data samples weight $\alpha_{t}=\sum^{N_{tr}}_{i=1}\alpha^{(i)}$

19: Update model:
$\theta^{(k)}_{t}=\sum^{N_{tr}}_{i=1}\frac{\alpha^{(i)}*\theta^{(i)}_{t}}{\alpha_{t}}$,
$w^{(k)}_{t}=\sum^{N_{tr}}_{i=1}\frac{\alpha^{(i)}w^{(i)}_{t}}{\alpha_{t}}$

20: The server transmits $\theta_{k}^{t},w^{(k)}_{t}$ to $k$-th contexts

21:end for

22:Output: $\theta_{T}$ and $w_{T}$

In steps 1-5, we initialize the contexts and split them into train and
validation sets. After the initialization, the training takes place locally in
a subset of $N_{tr}$ contexts, which are selected randomly at every
communication epoch. The training results, i.e., the trained neural network
$\theta^{(i)}$ and its weights $w^{(i)}$ along with the number of data sample
$n^{(i)}$, are then transmitted from the $N_{tr}$ contexts to the central
server for aggregation. After the aggregation, the central server sends back
the global trained model to every context, which updates their local DNN
models. The training cycle repeats for $T$ communication epochs.

One of the most important aspects of the FL training consists of aggregating
the weights at the central server. Initially, we weighted the updates from
each context equally, but such an approach resulted in a skewed performance
toward contexts with more STA. Such behavior can be attributed to the fact
that contexts with four STA have twice as many samples as contexts with only
two STA. Instead, our solution proposed to weight each context update based on
the number of data samples used for the training in each context is normalized
by the number of STA in the contexts. We denote this normalization weights
with $\alpha$. This approach improves the accuracy of the predictions of the
throughput.

To evaluate the performance of the proposed solution (shown in Fig. 7), we
consider the mean average error (MAE). In particular, we use a neural network
trained for $T=250$ communication rounds. In Table 3, we report the list of
hyper-parameters used in the submitted solution and related pre-trained DNN
can be found in the GitHub repository [14]. We perform the validation using
five percent of available contexts, randomly sampled from $\mathbb{K}$. In
total, we had $K=1946$ available contexts. Five percent of available contexts
are used for evaluation, i.e., $N_{eval}=97$. At each communication round, we
select 500 contexts randomly to perform training on, i.e., $N_{tr}=500$.
Furthermore, the contexts we use during the training and validation set are
kept separated to prevent the data leakage and to recognize when the solution
starts to over-fitting to the training data, i.e., $\mathbb{K}_{val}$, where
$\mathbb{K}_{val}\cap\mathbb{K}_{tr}=\emptyset$.

[width=0.5]tikz_figures/mea_plot

Fig. 7: MAE obtained by FederationS’ over the number of communication epochs.

As shown in Fig. 7, the MAE decreases over the number of communication epochs.
The same trend occurs for the contexts that we use during the training process
(Training set) as well as for the contexts that we use only for validation
(Validation set). However, the validation throughput decrease is noisier as it
sometimes increases between two consecutive communication epochs. Such
behavior is due to the random sampling approach as not all randomly selected
sets $\mathbb{K}_{ep}$ wholesomely represent the system.

Table 3: FederationS hyper-parameters Neural Network training options | Solver | Adam [60]
---|---|---
Batch size ($B$) | $21$
Dropout | $10\%$
Learning rate | $10^{-4}$
L2 regularization | $10^{-5}$

### 5.2 FedIPC

We design an NN model that predicts the throughput of the STAs of a given BSS
for a chosen SR configuration. The goal of this solution is to find the
optimal OBSS/PD threshold maximizing the network throughput. To this end, we
aim to design a neural network model which predicts the throughput of each STA
in the given BSS for a chosen OBSS/PD threshold from a certain range, thus one
can tune the OBSS/PD threshold by using NN architecture. In the federated
learning setting, we model each context as a node, where their data consist of
simulations with different thresholds. We assume these nodes cannot
communicate with each other, but communicate with a parameter server in
rounds, which aggregates the weights of the nodes to update the global model.
Then, the parameter server distributes the updated global model to the
clients.

To train the NN model we employ the federated learning framework in the
following way; we call a Wi-Fi deployment with specific characteristics such
as node locations and number of interfering BSSs as a context. We consider $n$
contexts in total, where for each context, we have $s_{k}$ STAs per AP for
context $k$. We also define $a$ as the maximum number of access point and $b$
as the maximum number of STAs per AP. Note that all contexts may have
different number of STAs per AP. We also have interference sensed by APs, RSSI
of the STAs assigned to $\mathrm{AP}_{\mathrm{A}}$, and the average SINR of
each STA in $\mathrm{BSS}_{A}$. We can control threshold
$\tau_{k}\in\\{-82,-81,\ldots,-62\\}$. To simplify the problem, we can only
change the threshold of the $\mathrm{BSS}_{A}$, and all other BSSs’ thresholds
are fixed to -82 dBm. Thus, we only consider the STAs in $\mathrm{BSS}_{A}$.
Let $\gamma_{j,\tau_{k}}^{(k)}\in\mathbb{R}$ be the throughput of
$j^{\mathrm{th}}$ STA in the $\mathrm{BSS}_{A}$ of the $k^{\mathrm{th}}$
context, where threshold of the $\mathrm{BSS}_{A}$ is chosen as $\tau_{k}$.
For context i, our objective is to find $\tau_{k}$ that maximizes the
throughput for all STAs in the $\mathrm{BSS}_{A}$ of context $k$, i.e,

$\operatorname*{arg\,max}_{\boldsymbol{\tau}}\sum_{k=1}^{n}\sum_{j=1}^{s_{k}}\gamma_{j,\tau_{k}}^{(k)},$
((5))

where $\boldsymbol{\tau}=\left[\tau_{1},\tau_{2},\ldots,\tau_{n}\right]^{T}$,
$s_{k}$ is the number of STAs connected to each AP (or the number of STAs in
each BSS) in context $k$. Having the knowledge of throughput values
$\gamma_{j,\tau^{{}^{\prime}}}^{(k)}$, $\forall k,j,\tau_{{}^{\prime}}$, which
is not likely, one can easily calculate $\boldsymbol{\tau}$ using ((5)). Thus,
to determine the best threshold for each context $k$, we estimate
$\gamma_{j,\tau^{{}^{\prime}}}^{(k)}$ via $\hat{\gamma}_{j,\tau_{i}}^{(k)}$
for all STA $j$ and threshold $\tau^{{}^{\prime}}$ combinations.

Since we cannot directly calculate or know the throughput
$\gamma_{j,\tau}^{(k)}$, we estimate it via a model
$\hat{\gamma}^{(k)}_{j,\tau}=f^{(k)}_{j,\tau}$, where $i$ is the context
index, $j$ is the index of STA connected to $\mathrm{AP}_{\mathrm{A}}$ and
$\tau$ is the threshold. Moreover, estimating one STA’s throughput is highly
related to estimating another STA’s throughput in the same context. Thus, to
exploit this relation, we formulate the throughput regression problem as
multi-output regression, as the following:

$\boldsymbol{f}^{(k)}_{\tau}(\boldsymbol{x}_{\tau}^{(k)},\boldsymbol{W}_{i})=\left[f^{(k)}_{1,\tau}\,\,f^{(k)}_{2,\tau}\ldots
f^{(k)}_{b,\tau}\right]^{T},$ ((6))

where $k$ is the context index, $\boldsymbol{x}_{\tau}^{(k)}$ is the input
vector and $\boldsymbol{W}_{i}$ is the neural network weights of the model at
context $k$. The input vector
$\boldsymbol{x}_{\tau}^{(k)}\in\mathbb{R}^{4b+a}$ includes each STA’s features
in order (for STAs in $\mathrm{BSS}_{A}$). Each STA’s features are as the
following: interference sensed by APs, RSSI, the average SINR and the
threshold, respectively. When a context has less than $b$ STA per AP, we zero
pad for the remaining places until the vector reaches the maximum in the
dataset. This is possible since $a$ (the maximum number of APs) and $b$ (the
maximum number of STAs per AP) are fixed.

Since every context may have different number of STAs per AP, we mask the
nonexistent STAs as the following:

$f^{(k)}_{k,\tau}=\hat{\gamma}^{(k)}_{k,\tau}=\gamma^{(k)}_{k,\tau}=0,\,\,\,\,\forall
k\in\\{s_{k}+1,\,\ldots,b\\}.$

This way, we do not backpropagate any loss for nonexistent STAs, and the model
becomes suitable for variable number of STAs per AP for every context. Then,
we define the ground truth vector as:

$\boldsymbol{\gamma}^{(k)}_{\tau}=\left[\gamma_{1,\tau}^{(k)}\,\,\gamma_{2,\tau}^{(k)}\,\,\ldots\,\,\gamma_{b,\tau}^{(k)}\right]^{T}$

For the context $k$ (local node), our objective is to minimize mean-squared
error for regression task for any
$(\boldsymbol{x}_{\tau}^{(k)},\boldsymbol{\gamma}^{(k)}_{\tau})$ data point
among all contexts, i.e.,

$\operatorname*{arg\,min}_{\boldsymbol{W}_{k}}\sum_{\forall\tau,k}\left|\left|\boldsymbol{f}^{(k)}_{\tau}(\boldsymbol{x}_{\tau}^{(k)},\boldsymbol{W}_{i})-\boldsymbol{\gamma}^{(k)}_{\tau}\right|\right|_{2}^{2}.$

We use a feed-forward neural network with one hidden layer as our model
$\boldsymbol{f}^{(k)}_{\tau}(\boldsymbol{x}_{\tau}^{(k)},\boldsymbol{W}_{k})$
with weights
$\boldsymbol{W}_{k}=\left[\boldsymbol{W}_{k}^{(1)}\,\,\boldsymbol{W}_{k}^{(2)}\right]$,
where
$\boldsymbol{f}^{(k)}_{\tau}(\boldsymbol{x}_{\tau}^{(k)},\boldsymbol{W}_{i})=\boldsymbol{W}_{k}^{(2)}\mathrm{ReLU}(\boldsymbol{W}_{k}^{(1)}\boldsymbol{x}_{\tau}^{(k)})$,
$\boldsymbol{W}_{k}^{(2)}\in\mathbb{R}^{b\times h}$ and
$\boldsymbol{W}_{k}^{(1)}\in\mathbb{R}^{h\times(a+3b)}$. As seen, we use
rectified linear unit (ReLU) as our activation function. Note that this neural
network can easily be generalized to a neural network with multiple hidden
layers, but in our case, the neural network with only 1 hidden layer has
worked the best on the validation set.

To train the proposed NN architecture under the FL paradigm, FedAvg is applied
(see Section 3). We consider full participation during FL rounds, meaning that
all the users’ updates are used for averaging in each communication step.
Furthermore, we fix the batch size to $B=21$ (matching the size of local
datasets) and the number of local epochs to $E=1$. Regarding data splits, we
only use the scenario training3, as it is the one containing more complex and
complete data, and use the 80% of the contexts for training, the 10% of the
contexts for the first validation, and the remaining 10% for the second
validation. Notice that we use the first validation set for early stopping of
the global model, whereas the second one it to perform hyper-parameter tuning.
We tune our method by using Tree Parzen Estimator of the Optuna library [61]
and choose the model with the lowest MAE on the second validation set.

Finally, we evaluate our global model after every 20 rounds and stop training
if no improvement in validation MAE is achieved after $T=100$ rounds. We
evaluate the prediction results of our method via the MAE metric. Recall that
we estimate the throughputs by multi-output regression task and each context
may have different number of throughputs to be predicted. Thus, we flatten the
predictions for existing STAs before calculating the MAE. We normalize the
data by minimax normalization. We use the standard SGD implementation of
Pytorch [62] to implement federated averaging.

Table 4: Evaluation results for the best-performing neural networks with
different numbers of layers in FederationS’ hyperparameter optimization.

# hidden layers | Neurons per layer | MAE (Mbps)
---|---|---
1 | 256 | 5.10
2 | 256, 16 | 5.57
3 | 256, 32, 16 | 5.88

Table 4 shows the evaluation results for neural network architectures with
different number of layers. We only report the best configuration for each
different number of hidden layers. We choose the hyperparameters with the
least MAE on the first validation set and report the results on the second
validation set for different numbers of layers. The network with only 1 hidden
layer containing 256 neurons performs the best. As the number of layers
increases, we observe a decrease in performance. This is probably because our
network starts to overfit the data when the network becomes more complex. This
reasoning also supports that, although our network is much simpler, it is more
accurate than other participants’ networks, as shown in Table 7.

### 5.3 WirelessAI

We follow the FL framework to address the complex SR problem in multiple 11ax
WLAN cells. Individual agents first train their local NN models (with the same
network structure) using their local datasets, and then exchange and average
model weights through a centralized parameter server.

Typical NN models use simple data structures such as vectors to encode inputs
and outputs. However, in wireless networks characterized by graphs
$G=(V,~{}E)$, where $V$ is the set of nodes, and $E$ is the set of wireless
links, the number of nodes and the number of links can vary depending on the
networking scenarios. It is difficult to fix the vector dimension to fit all
networking scenarios. Even if we can fix the dimension and pad zeros to the
unused dimension fields, it is meaningless to use these fields.

To overcome the graph representation problem, we treat the whole network as an
image. More specially, we first fix a maximum range and treat the whole
network as a 1$\times$100$\times$100 gray-scale image with a default value of
0. Then we map nodes to values by their roles (i.e., AP role with value 1, the
target AP with OBSS/PDD value, other APs with value 1, and STAs with value 2),
and place the values to their corresponding locations. In this way, we can
represent any networks with arbitrary APs and STAs. Note that the topology
information is encoded into the image.

Then, we adopt two NNs to predict the performance: one part is a convolutional
neural network (CNN), and the other part is a fully connected neural network
(FCNN). We first use CNN to capture the interactions between STAs and APs to
predict the RSSI, SINR of the BSS of interest, and interference to the AP of
interest. The input of the CNN is the above processed gray-scale image, and
the used OBSS/PD value, and the output of the CNN is the RSSI, SINR, and the
caused interference. Then, we use the output of CNN as the input of FCNN to
predict the downlink throughput of the AP of interest.

The key rationale of using such architecture is to reduce computation
complexity. RSSI, SINR, and the caused interference are the key factors that
impact the final performance. Compared with using a whole FCNN to predict the
performance, if we can first use CNN to model the relationship among
{topology, OBSS/PD value} and {RSSI, SINR, and the caused interference}, and
then use a small dimension of FCNN to predict the performance, the computation
complexity is reduced.

To empower our FL algorithm, we treat each context as a local client and let
each local client use its own data to train the above two NNs. In particular,
we follow the standard FL training procedure, and run the training in rounds:
the above two NN models are trained by each local client, and the weights of
the local models are averaged to generate the global shared model, which is
used in the next round. For a dataset, there are overall 1000 local clients,
and we randomly choose 10 local clients in each round to generate the average
model. The global shared model has been updated using $T=20$ rounds in total.

The proposed NN model and FL algorithm are implemented in Pytorch.222The code
used to implement all the proposed methods by WirelessAI is available in
Github [16]. Table 5 and Table 6 summarize the architecture of the proposed NN
models. In particular, there are 13 layers in our CNN model including 5
convolution layers, 4 max-pooling layers, 1 adaptive average pooling layer,
and 3 fully-connected layers. For each convolution layer, the layers are
convolved with kernel size 3. In order to keep the size of the image after
each convolution operation and obtain more information on the image edge
position, we fill the images (i.e., padding) before each convolution
operation. After every convolution layer, a max-pooling operation is applied
to the feature maps. The kernel size of the max-pooling layer is 2. The
purpose of max-pooling is to reduce the size of the feature maps. The output
size of the adaptive average pooling layer is 1. The fully-connected layers
consist of respectively 512 and 64 and 17 output neurons. There are 1 input
layer, 2 hidden layers, and 1 output layer in our FCNN model. These layers
consist of respectively 512 and 128 and 64 and 6 output neurons. The ReLU is
used as an activation function for convolution layers and fully-connected
layers.

Table 5: A Summary Table of the Proposed CNN Model.

Layers | Type | Output Size | Kernel Size | Stride
---|---|---|---|---
1 | Convolution | 128$\times$100$\times$100 | 3 | 1
2 | Max-pooling | 128$\times$50$\times$50 | 2 | 2
3 | Convolution | 256$\times$50$\times$50 | 3 | 1
4 | Max-pooling | 256$\times$25$\times$25 | 2 | 2
5 | Convolution | 512$\times$25$\times$25 | 3 | 1
6 | Max-pooling | 512$\times$12$\times$12 | 2 | 2
7 | Convolution | 1024$\times$12$\times$12 | 3 | 1
8 | Max-pooling | 1024$\times$6$\times$6 | 2 | 2
9 | Convolution | 2048$\times$6$\times$6 | 3 | 1
10 | Adaptive average pooling | 1$\times$2048 | - | -
11 | Fully-Connected | 512 | - | -
12 | Fully-Connected | 64 | - | -
13 | Fully-Connected | 17 | - | -

Table 6: Summary Table of the WirelessAI FCNN Model. Layers | Type | Output Size
---|---|---
1 | Fully-Connected | 512
2 | Fully-Connected | 128
3 | Fully-Connected | 64
4 | Fully Connected | 6

## 6\. Performance Evaluation

In this section, we show the results obtained by the challenge participants’
models presented in Section 5. During the competition, the test dataset was
released without revealing the actual throughput of the simulated deployments.
Table 7 summarizes the performance accuracy obtained by each participating
team on the test dataset.

Table 7: Mean average error obtained by the solution proposed by each team. Team | MAE (Mbps)
---|---
FederationS | 6.5534
FedIPC | 5.8572
WirelessAI | 8.913

Next, we analyze the results obtained by each participant in more detail.
First, Fig. 8 showcases the empirical cumulative distribution function (CDF)
of the test error obtained by each solution. The results are compared to the
ones obtained by a vanilla centralized mechanism, which consists of a feed-
forward NN with $1024$, $512$, and $256$ neurons in each of its three layers,
with ReLU activation.333For further details on the centralized mechanism,
refer to the provided open-access repository [63]. In addition, to remark the
need for ML for the prediction problem in Wi-Fi, we provide the results of a
baseline analytical model, based on Continuous Time Markov Networks (CTMNs).
Such a baseline model implementation was presented in [64], as an extension of
the Spatial Flexible Continuous Time Markov Network (SFCTMN) framework [65].
To the best of our knowledge, the targeted analytical implementation of IEEE
802.11ax SR is one of the first of its kind and suits the SR operation because
it characterized both PHY and MAC phenomena in BSSs.

Fig. 8: Empirical CDF of the error obtained by each participants’ solution
over the test dataset. Results of baseline SFTCMN modeling and vanilla
centralized NN are included for comparison purposes.

As shown, all the proposed FL solutions improve the performance of both the
SFCTMN analytical model and the naive centralized method. The SFCTMN model,
while allowing to provide insights on the 11ax SR operation, is shown to fail
at faithfully representing the realistic phenomena observed in WLANs through
simulations. This result points to the need for ML models to capture the
complex phenomena in WLANs. Concerning the ML models presented in this paper,
FedIPC is the one providing the highest accuracy, with the 82.4% of the
predictions below a 10 Mbps error, compared to the 80.5% and the 60.3%
achieved by FederationS and WirelessAI, respectively.

Finally, to provide more insights on the generalization capabilities of each
model, Fig. 9 shows the error obtained by each solution, for each possible
number of APs and STAs in the test deployments. As shown, contrary to
intuition, all the models perform better as complexity increases, i.e., as the
number of APs and STAs is bigger. This result is mainly motivated by the fact
that DL allows capturing the most complex interactions in dense deployments,
which reinforces the role of AI-enabled solutions for network optimization.

(a)

(b)

Fig. 9: Generalization capabilities of the proposed models on test data with
respect to: (a) the number of STAs, and (b) the number of APs.

## 7\. Discussion

### 7.1 Contributions

In this paper, we have presented the main results gathered from problem
statement “ITU-ML5G-PS-004: Federated Learning for Spatial Reuse in a multi-
BSS (Basic Service Set) scenario” in the 2021 ITU AI for 5G Challenge. First,
we have overviewed the SR problem in IEEE 802.11ax and formulated a novel
optimization use case via FL. To evaluate the potential of this solution, we
have provided a dataset containing synthetic data on 11ax SR measurements in
random deployments. The dataset is open for the sake of reproducibility and to
engage other researchers to work on this topic. The provided dataset has been
used by the participants of the challenge to develop the models introduced in
this paper.

### 7.2 Lessons learned

We extract the following insights from the models and results overviewed in
this paper:

  1. 1.

Predicting WLAN performance accurately is key to optimize these kinds of
networks. However, mechanisms like OFDMA or SR add further complexity to
accurately modeling WLANs. In this regard, DL-based models have shown a great
potential for capturing the complex interactions of IEEE 802.11ax WLANs
applying SR in dense deployments. This provides a paradigm shift with respect
to mostly adopted online learning mechanisms. Nevertheless, for the sake of
addressing spatial interactions in dynamic WLAN settings, both types of
mechanisms are envisioned to be combined.

  2. 2.

FL suits the decentralized nature of WLANs and, despite contexts count on
limited data, its performance has been shown to outperform vanilla centralized
ML. Thus, FL provides opportunities for (i) training ML models
collaboratively, (ii) enhancing user privacy by not sharing data directly but
model weights, (iii) reducing the communication overhead of traditional
centralized ML mechanisms, and (iv) providing portability by reducing the
computation capabilities for the training of ML models.

  3. 3.

Finally, using synthetic datasets for training ML models contributes to
enriching ground knowledge on certain network technologies and deployments.
Concerning this, we remark the importance of cost-effectively generating data
with network simulators, which can complement real networking data to, for
instance, reproduce anomalies.

### 7.3 Future research directions

The SR mechanism is expected to evolve towards a more sophisticated operation
in future IEEE 802.11 amendments. At the moment of writing this paper, task
group IEEE 802.11be (TGbe) is defining the coordinated SR (c-SR) mechanism as
part of the multi-AP operation [66]. Through c-SR, APs collaborate to further
improve the performance gains achieved by applying SR. More specifically, APs
exchange relevant information (e.g., measurements) to select the best SR
configuration, based on the recipient STAs to which transmissions are expected
to be held.

Fig. 10 illustrates the basics on c-SR. Considering the deployment shown in
Fig. 10(a), where two contending APs (namely APA and APB) are within the same
sensitivity area when using the default CCA/CS. Nevertheless, when applying
c-SR, both APs can transmit in parallel, thus enhancing spectral efficiency
and potentially reducing latency. To do so, the AP gaining channel access
after completing the backoff (BO) takes the role of sharing AP (in Fig. 10(b),
APA is the sharing AP). Likewise, APB acts as a shared AP. The sharing AP
sends a c-SR Trigger Frame (TF) to indicate the capabilities of the upcoming
transmission to selected STA (STAA), including the maximum acceptable
interference level (obtained through measurements). Based on the information
provided in the TF, the shared AP decides which STA to transmit and selects
the best configuration (e.g., transmit power, MCS) to that end. Notice, as
well, that a transmit power limitation is imposed by the sharing AP so that
its transmission is not affected by shared APs. Finally, once both
simultaneous transmissions take place, a block ACK (BA) is sent by STAs to
confirm successful downlink transmissions.

(a)

(b)

Fig. 10: c-SR operation: (a) deployment, and (b) exchange of packets.

Given the added complexity of evolved SR, the role of ML, and more
specifically FL, gains ground. Notice, as well, that in order to set the best
configuration that maximizes the overall network performance, both APs and
STAs perform measurements related to spectral utilization. Such a rich source
of data can be exploited by FL to drive intelligent-based network
optimization.

## Acknowledgement

The authors would like to thank enormously everyone that made possible the ITU
AI for 5G Challenge, with special mention to Vishnu Ram OV, Reinhard Scholl,
and Thomas Basikolo. Likewise, we would like to thank the valuable feedback
provided by Dr. Andrea Bonfante.

The present work has received funding from the European Union’s Horizon 2020
Marie Skłodowska Curie Innovative Training Network Greenedge (GA. No. 953775)
and has been partially supported by WINDMAL PGC2018-099959-B-I00
(MCIU/AEI/FEDER,UE). This work was also in part funded by the SFI-NSFC
Partnership Programme Grant Number 17/NSFC/5224.

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