# Interference Suppression Using Deep Learning: Current Approaches and Open
Challenges

Taiwo Oyedare1, Vijay K. Shah2, Daniel J. Jakubisin1,3 and Jeffrey H. Reed1,
1Dept. of Electrical and Computer Engineering, Virginia Tech, Blacksburg, USA
2Dept. of Cybersecurity Engineering, George Mason University, Fairfax, USA
3Virginia Tech National Security Institute, Blacksburg, VA, USA (email:
<EMAIL_ADDRESS>
{toyedare, djj<EMAIL_ADDRESS>and<EMAIL_ADDRESS>
###### Abstract

In light of the finite nature of the wireless spectrum and the increasing
demand for spectrum use arising from recent technological breakthroughs in
wireless communication, the problem of interference continues to persist.
Despite recent advancements in resolving interference issues, interference
still presents a difficult challenge to effective usage of the spectrum. This
is partly due to the rise in the use of license-free and managed shared bands
for Wi-Fi, long term evolution (LTE) unlicensed (LTE-U), LTE licensed assisted
access (LAA), 5G NR, and other opportunistic spectrum access solutions. As a
result of this, the need for efficient spectrum usage schemes that are robust
against interference has never been more important. In the past, most
solutions to interference have addressed the problem by using avoidance
techniques as well as non-AI mitigation approaches (for example, adaptive
filters). The key downside to non-AI techniques is the need for domain
expertise in the extraction or exploitation of signal features such as
cyclostationarity, bandwidth and modulation of the interfering signals. More
recently, researchers have successfully explored AI/ML enabled physical (PHY)
layer techniques, especially deep learning which reduces or compensates for
the interfering signal instead of simply avoiding it. The underlying idea of
ML based approaches is to learn the interference or the interference
characteristics from the data, thereby sidelining the need for domain
expertise in suppressing the interference. In this paper, we review a wide
range of techniques that have used deep learning to suppress interference. We
provide comparison and guidelines for many different types of deep learning
techniques in interference suppression. In addition, we highlight challenges
and potential future research directions for the successful adoption of deep
learning in interference suppression.

###### Index Terms:

Deep learning, interference suppression, convolutional neural networks,
autoencoders, neural networks, recurrent neural networks, radio interference,
wireless communication.

##  I Introduction

Interference has always been a dominant and recurring problem in the field of
wireless communications. With recent technological breakthroughs such as 5G
cellular, internet of things (IoT), LTE-U/LAA, vehicle-to-everything (V2X),
and also the increasing availability of low cost software defined radios,
coupled with the finite nature of the radio spectrum, interference may
continue to be a challenge. With the sheer amount of wireless devices sharing
the spectrum band, issues such as homogeneous and heterogeneous technology
interference may continue to impact reliable transmission. For instance, not
only could legitimate secondary users (SUs) sharing a spectrum band cause
interference to one another, rogue SUs could also deliberately interfere with
both legacy users as well as legitimate SUs. In Wi-Fi unlicensed applications,
Wi-Fi has had to contend with interference from baby monitors, wireless
speakers, garage door openers, and more recently, the introduction of
unlicensed cellular users in these bands. While earlier interference
suppression schemes [1] have utilized interference avoidance schemes usually
done in time, frequency, code or space domain, others [2, 3, 4] have utilized
PHY layer techniques to suppress such interference.

We consider a broad range of applications where interference could present
difficult challenges. These include, for example, automotive radar, non-
orthogonal multiple access (NoMA) systems, dense internet-of-things (IoT),
full duplex systems, dynamic spectrum use as well as coexistence studies
between different technologies. Within the context of a communication channel,
interference can be broadly categorized as adjacent channel and co-channel
interference. The main difference between these two is that the former occurs
between adjacent frequency bands while the latter occurs in the same frequency
band. In the context of the technology, interference could be categorized as
homogeneous technology interference and heterogeneous technology interference.
For the former, signals from the same technology interfere with one another
while the latter implies interference between different technologies. In
addition, full duplex systems that are aimed at improving spectral efficiency
also experience self-interference (SI). Often times, SI usually occur when
simultaneous transmission and reception occur in a frequency band. Details of
these categories can be found in section III-A.

Traditional interference suppression techniques relied on the interference
categories to know which approach to follow. These techniques include PHY
layer schemes, blind source separation schemes (BSS) amongst others. PHY layer
schemes rely on suppressing interference via optimizing PHY layer design or
providing redundancy to account for interference at the receiver [2, 3, 4, 5,
6]. They investigate optimal PHY layer schemes with baseline methods such as
heuristic models, optimization techniques, greedy algorithms, amongst others,
to suppress interference. For instance, Carrick et al. [5] investigated
interference suppression methods that exploit spectral redundancies in radar
and orthogonal frequency division multiplexing (OFDM) signals using time-
varying frequency shift filter referred to as TV-FRESH filters. The technique
in [5] utilized a TV-FRESH filter to suppress linear frequency modulated radar
interference. The main approach for the TV-FRESH technique is to use spectral
redundancy to enhance the desired signal or to estimate and subtract the
interfering signal and thus leaving only the signal-of-interest (SOI). In
addition, an adaptive filter can be used to remove interference from a signal
without affecting crucial information [7, 8]. Similar to the TV-FRESH
approach, adaptive filters are able to filter or subtract interference from a
signal to yield a signal with less interference. One key attribute of adaptive
filters is their ability to adjust the filtering criteria based on the type of
interference to be suppressed [7, 9]. Earlier works that utilized these
techniques rely on the use of signal properties such as cyclostationary
features [10] inherent in the signal, signal envelope [11, 9, 12] and channel
bandwidth [13] to identify and suppress interfering signals. As noted by [14],
the major issue with PHY layer techniques that optimize the weights of the
filter using estimated time-frequency statistics is that time-varying
interference result in time-varying weights which tend to reduce the
effectiveness of conventional adaptive filters such as Least Mean Squares
(LMS). Some of the single channel approaches discussed could also be extended
to multiple channels. Furthermore, the reliance on domain expertise or
specific signal knowledge may result in lack of scalability when considering
the variety and the number of wireless devices present in modern wireless
environments.

In investigating BSS techniques, Fabrizio et. al. [15] proposed a method that
estimates interfering waveforms occupying the same channel as the SOI using a
generalized estimation of multipath signals (GEMS) algorithm. In BSS, a set of
signals are separated from a set of mixed signals with or without information
about the mixing process or even the source signals [16]. Simply put, BSS is
used to recover the original component from a mixture signal. The SOI, as the
name implies, is the noise-free component of a signal that we seek to analyze
or recover at the receiver [17]. Although, a lot of works in the literature
considered separating audio signals with fewer dimensions, multi-dimensional
data have also seen the application of BSS techniques. For instance, principal
component analysis (PCA) is an example of a BSS technique that has been used
in the image processing domain for dimensionality reduction. For their BSS
technique, authors in [15] reconstructed interference components at the
receiver from the estimated waveform and then subsequently subtracted these
components from the data in time domain. Specifically, they leveraged a least-
squares suppression technique to uncover synthetic targets from the GEMS
algorithm using waveform filtering. The waveform filtering relies on
interference signal reconstruction and time-domain coherent subtraction.
Details of the GEMS algorithm can be found in [18]. Some other suppression
techniques different from BSS were also studied. For instance, Uysal [19]
proposed a time domain approach to decompose signals so as to separate the
automotive radar interference signal from the SOI. In addition, Li et. al.
[20] first estimated signal parameters (from known interference types) using a
time-modulated windowed all-phase discrete Fourier transform (DFT) and then
reconstructed the estimated signal and consequently subtracted it from the
received signal to obtain the SOI. In general, the major issue with these
approaches is that they tend to require very precise detection of where the
interference is located as well as its duration and other characteristics.
Specifically, we observe that the traditional interference suppression
techniques suffer three main limitations: (1) reliance on domain knowledge and
the interference characteristics; (2) their inability to generalize and (3)
complexity resulting from detecting the precise location of the interference.

Deep learning based interference suppression techniques, which are model-free,
have been found to sufficiently address the limitations of traditional
interference suppression techniques. For instance, while real-time
interference modeling may be difficult, a model-free approach, may remove the
need for domain knowledge and thus make them more practical in real world
deployment. Since the radio frequency (RF) environment is very stochastic in
nature, this goal may seem very difficult to attain especially in the wild
where the inability to suppress such could negatively impact performance.
However, deep learning approaches can learn directly from the data and may not
require any prior knowledge to suppress interference. While it it challenging
to generate enough datasets for different realizations of the channel
environment, we note that because deep learning usually require access to a
large repository of data, software defined radios (SDRs) have simplified the
data collection process since they are relatively inexpensive and can be used
to generate data in different channel scenarios. While this process may take
time, it is important to understand that it would eventually benefit the
wireless communication research community as deep learning for interference
suppression continues to be studied and adopted.

Another key benefit of using deep learning is the ability to learn powerful,
non-linear relationships for interference suppression which would be difficult
to develop through traditional suppression techniques. Many traditional
interference suppression schemes are not robust when the decision boundary is
complex and non-linear. Deep learning techniques are being embraced in the
interference suppression community because they can learn complex non-linear
decision boundaries. Finally, the issue of scalability for which many
traditional suppression techniques are not able to achieve can be done with
deep learning.

In this paper, we seek to address the knowledge gap in the literature with
respect to the state-of-the-art in DL-based interference suppression. To
account for the limitations of DL-based interference suppression techniques,
we discuss several challenges, including lack of interpretability, the
stochastic nature of the wireless channel, issues with open set recognition
(OSR) and challenges with implementation in section VII. To the best of our
knowledge, this is the first paper that explores deep learning based
interference suppression techniques for multiple application areas and various
interference characteristics. Our investigation show that not only do deep
learning techniques reduce computational complexity in comparison to non-deep
learning suppression techniques, they also outperform non-DL techniques in
various channel environment as well as different wireless communications
applications. We believe that this is a very important study since deep
learning is gaining attention in wireless communication research for
interference suppression. This paper would best serve researchers working on
interference suppression issues or the application of deep learning to
wireless communications, as well as, wireless spectrum users and decision
makers. Our contributions are as follows:

  * •

We identify key characteristics and provide a taxonomy of interference-related
issues in wireless communication. This includes key features of adjacent
channel interference, co-channel interference, homogeneous and heterogeneous
technology interference prevalent in wireless communication applications.

  * •

We review a wide range of techniques that have used deep learning to suppress
interference. Techniques include both supervised deep learning (such as CNN,
deep neural networks (DNN) and recurrent neural networks (RNN)), unsupervised
techniques such as deep autoencoders. We also discuss the different wireless
communication applications for which the deep learning techniques have been
utilized.

  * •

We summarize the benefits and drawbacks of using different DL algorithms for
interference suppression. This summary also include the technical approach
utilized as well as the benefits and drawbacks associated with them. Table IV
has been provided to give a high level summary.

  * •

We highlight challenges associated with using deep learning for interference
suppression. While some of these challenges are not unique to DL-based
interference suppression, we note that they have the capability of slowing
down the adoption of DL in interference suppression study.

Figure 1: High Level Structure of the Survey Paper

Fig. 1 shows a high level structure of the paper including a breakdown of the
topics discussed in each section.

###  I-A Abbreviations and Acronyms

ACI

    Adjacent Channel Interference
AI

    Artificial Intelligence
AUC

    Area Under the receiver operating characteristics Curve
AUC

    Area Under the ROC Curve
AWGN

    Additive White Gaussian Noise
BER

    Bit Error Rate
BLE

    Bluetooth Low Energy
BPSK

    Binary Phase Shift Keying
BSS

    Blind Source Separation
CCI

    Co-channel Interference
CNN

    Convolutional Neural Network
Conv

    Convolution
CS

    Chirp Sequence
CSMA/CA

    Carrier Sense Multiple Access/ Collision Avoidance
CTI

    Cross Technology Interference
CVCNN

    Complex Valued Convolutional Neural Network
CWI

    Continuous Wave Interference
DBP

    Digital Backpropagation
DFT

    Discrete Fourier Transform
DL

    Deep Learning
DNN

    Deep Neural Network
DSSS

    Direct Sequence Spread Spectrum
EBPNN

    Extended Backpropagation Neural Network
EVM

    Error Vector Magnitude
FCN

    Fully Convolutional Network
FFT

    Fast Fourier Transform
FMCW

    Frequency Modulated Continuous Wave
GAN

    Generative Adversarial Networks
GEM

    Generalized Estimation of Multi-path Signals
GPS

    global Positioning System
GPU

    Graphical Processing Unit
GRU

    Gated Recurrent Unit
IMAT

    Iterative Method with Adaptive Thresholding
IoT

    Internet of Things
IQ

    In-phase Quadrature
LDBP

    Learned Digital Backpropagation
LQI

    Link Quality Indicator
LR

    Linear Regression
LTE-U

    Long Term Evolution Unlicensed
MAE

    Mean Absolute Error
MCWI

    Multi-tone Continuous Wave Interference
MIMO

    Multiple Input Multiple Output
MLP

    Multi-layer Perceptron
MMSE

    Minimum Mean Square Error
MSE

    Mean Square Error
MSPE

    Mean Square Prediction Error
NoMA

    Non-orthogonal Multiple Access
NS3

    Network Simulator 3
OFDM

    Orthogonal Frequency Division Multiplexing
OOD

    Out of Distribution
OSR

    Open Set Recognition
OTA

    Over The Air
PCA

    Principal Component Analysis
PDP-WPT

    Power Distributing Predominance Wavelet Packet Transform
PHY

    Physical
PPMSE

    Peak Phase Mean Square Error
PRR

    Packet Receive Ratio
ReLU

    Rectified Linear Unit
RFI

    Radio Frequency Interference
RFI

    Radio Frequency Interference
RNN

    Recurrent Neural Network
RSSI

    Received Signal Strength Indicator
SCWI

    Single-tone Continuous Wave Interference
SDR

    Software Defined Radio
SEFDM

    Spectrally Efficient Frequency Division Multiplexing
SER

    Symbol Error Rate
SINR

    Signal to Noise plu Interference Ratio
SIR

    Signal to Interference Ratio
SNR

    Signal to Noise Ratio
SoI

    Signal of Interest
SSFM

    Split-Step Fourier Method
SSIM

    Structural Similarity
STI

    Same Technology Interference
TDMA

    Time Division Multiple Access
UKF-RNN

    Uscented Kalman Filter-based Recurrent Neural Network
V2X

    Vehicle to Everything
VAE

    Variational Autoencoders
WBAN

    Wireless Body Area Network

##  II Related Works

To the best of our knowledge, the current literature lacks a comprehensive
study on deep learning-based interference suppression. As at the time of
writing this paper, we have found only two surveys which consider DL
techniques in the context of interference suppression. In [21], the authors
discussed deep learning techniques for radar applications in general and
briefly highlighted interference techniques that fell into the deep learning
categories. One key distinction of our work from theirs is that the
interference types they considered includes only jamming and clutter. We note
that this investigation is very important and our work goes a step further by
investigating more DL-based interference suppression applications including
radars, optical communications, IoT networks, GPS navigation, full duplex
systems and wireless sensor networks. Further, we consider many other types of
interference such as self-interference, adjacent channel interference,
homogeneous and heterogeneous technology interference. Furthermore, the
jamming suppression techniques considered in [21] focused on target signal
classification and recognition. Our work is fundamentally different from
theirs because we focused more on interference suppression techniques and not
interference classification techniques. In another work, Chen et al. [22]
surveyed both coexistence and interference mitigation techniques, including
traditional (non-DL) and DL approaches for wireless personal area network
(WPAN) and WLAN applications. While this is also another important
contribution, the scope of the application considered is different in
comparison to the one presented in this survey. Also, we found that their work
focused more heavily on non-DL interference suppression and briefly discussed
DL-based suppression techniques. Furthermore, in our own work, we discuss
several critical interference characteristics and why DL techniques are
suitable to suppress them. Also, we point the community to publicly available
dataset for interference suppression.

Many of the other surveys [23, 24, 1, 25, 26] on interference suppression
reviewed papers that utilized signal processing techniques and even non-ML
techniques to avoid interference. For instance, Azmi et. al. [23] discussed
technical papers that used techniques such as transmit power control, dynamic
frequency selection, interferer classification schemes [24], time slotted
channel hopping schemes amongst others to suppress interference in wireless
sensor networks (WSN). Further, Lu et al. reviewed interference mitigation
schemes that utilized adaptive array processing in GPS systems. Le et al. also
surveyed interference mitigation schemes used in wireless body area sensor
networks. Kunert et. al. [1] surveyed six basic techniques that researchers
have used to suppress radar interference. They sorted the suppression
techniques into domains such as polarization, time, frequency, coding and
space. While the techniques discussed in this survey paper typically require
domain expertise to be successful, it is not clear if they will be scalable in
applications such as IoT and 5G where there are multiple devices sharing the
same frequency channel. Furthermore, one of the approaches discussed in the
paper is ”communicate and avoid” in various domains including time and
frequency. While this is a straightforward approach, it involves multiple
steps to achieve and the time to implement in a latency-sensitive applications
such as 5G New Radio (NR) or vehicle to everything (V2X) may be unrealistic.
Some of these limitations can be tackled using deep learning techniques
surveyed in this paper.

For automotive radar suppression algorithms, Toth et. al. [27] investigated
state-of-the-art interference suppression techniques including zeroing, ramp
filtering, IMAT and time domain parametric interpolation (IVMip). Their study
did not include ML or DL approaches that could be leveraged to suppress
interference. Also, their application focused primarily on automotive radar
interference, whereas our survey encompasses many other application areas. In
the same vein, Nabil et. al. [28], discussed advances in self and mutual
interference suppression techniques for wireless networks. They surveyed
redundancy techniques such as MIMO technology to suppress mutual interference
in scenarios when the receiver does not have complete knowledge of the
interfering signal. They also discussed interference cancellation techniques
such as Balun transformer, isolation of the transmitter and receiver paths,
phase shifting and spatial techniques (enabling an appropriate distance
between the transmit antennas).

Our work is different from all the other surveys on interference suppression
because we explored suppression approaches that utilized deep learning for
many application areas and interference characteristic types while also
providing a comparison between them and the major challenges with using deep
learning to suppress interference. To the best of our knowledge, this is the
first paper that explores deep learning based interference suppression
techniques for multiple application areas and various interference
characteristics. There is a lot of promising results that show that DL based
techniques can be very useful in quickly suppressing interference in many
wireless communications applications.

TABLE I: Wireless Technologies sharing the $2.4$GHz band with their interference avoidance methods Technology |  Number of Channels |  Interference Avoidance Method
---|---|---
Wi-Fi (IEEE 802.11b/g/n/ax) |  $3$ $20$MHz static channels |  Collision avoidance
Bluetooth |  $79$ $1$ MHz dynamic channels |  Frequency hopping
Wireless USB (including wireless keyboard, mouse, etc) |  $79$ $1$ MHz dynamic channels |  Frequency agility
IEEE 802.15.4 |  $16$ $5$MHz static channels |  Fixed collision avoidance
Bluetooth Low Energy (BLE) |  $40$ $2$MHz channels |  Frequency hopping
Cordless Phone |  $100$KHz (Analog Phone); $800$KHz (Frequency hopping phone) |  Frequency hopping

##  III Technical Background

###  III-A Interference Types and Characteristics

In this section, we discuss the types and characteristics of interference in
the wireless communications research.

####  III-A1 Adjacent Channel Interference

Adjacent channel interference (ACI) occurs when the power from a signal in a
channel adjacent to it causes interference to another signal in a different
channel. There are several reasons why ACI may occur, for example in frequency
modulated (FM) systems, incomplete filtering may lead to ACI. Furthermore, an
inadequate frequency control in either the interfering channel or the
reference channel may also lead to ACI. In some cases, interference avoidance
schemes can be used to avoid ACI. For instance, while the $2.4$ GHz Wi-Fi band
supports a total of $11$ channels, it is recommended that Wi-Fi users in the
United States utilize non-overlapping channels (take for example: channels
$1$, $6$ and $11$) [29].

####  III-A2 Co-channel Interference

Co-channel interference (CCI), on the other hand, occurs when two devices or
transmitters that share the same frequency for transmission interfere with one
another [30]. In cellular communication applications, CCI is more pronounced
when frequency bands are re-used. Interference avoidance schemes alone are not
sufficient to suppress CCI. In this survey, we focus on the more difficult
problem of CCI.

####  III-A3 Self Interference

Self interference (SI) is a very common phenomenon in full duplex systems
where transmitters and receivers utilize the same frequency band for
simultaneous communication. In cognitive radio (CR) radio applications, in-
band full duplex transmission and reception on the same frequency at the same
time is very useful in doubling spectral efficiency while maximizing the use
of available spectrum resources [31, 32]. Techniques such as antenna, analog
and digital cancellation have been studied in canceling interference, however,
it has been shown that it is very difficult to completely cancel SI due to
imperfections such as IQ imbalance, power amplifier non-linearities, amongst
others [33].

####  III-A4 Homogeneous Technology Interference

Homogeneous technology interference, also referred to as same technology
interference, occurs when a wireless technology is interfered by a varying
number of signals from the same technology. They can occur either in the same
channel (co-channel) or in different channels (adjacent channel). In some
cases, the number of interfering signals could be more than one, depending on
the type of frequency band the dominant signal is utilizing. Such interference
is often due to the use of multiple access technologies such as IEEE 802.15.1
and IEEE 802.15.4 [34]. Farid et al. [35] also studied homogeneous technology
interference avoidance technique such as CSMA/CA-based approach; interference
suppression techniques such as adaptive techniques, graph coloring, TDMA based
approaches, and heuristic techniques, amongst others, for wireless body area
networks (WBAN)111WBAN is a broad term describing wearable sensors connected
in a wireless manner to a hub [35].. Also, code division multiple-access
(CDMA), a multiplexing technology for cellular communication technologies, is
also vulnerable to homogeneous technology interference. In spread spectrum
CDMA, the spread spectrum systems share the band with other communication
systems. To do this successfully, individual users in a spread spectrum CDMA
system has a unique binary psuedo-noise (PN) code that helps the receiver to
distinguish it from the other users in the system. In the past three decades,
several interference suppression techniques, such as linear [36] and non-
linear estimation techniques [37], have been proposed to suppress homogeneous
technology interference in CDMA systems. We note that the research in
homogeneous technology interference has considerably waned in comparison to
heterogeneous technology interference discussed in section III-A5. As a result
of this, many of the DL-based interference suppression techniques explored
heterogeneous technology interference more than they did homogeneous
technology interference.

####  III-A5 Heterogeneous Technology Interference

Understanding heterogeneous technology interference, also referred to as cross
technology interference (CTI), is very crucial to the coexistence of different
wireless technologies sharing similar unlicensed frequency bands. This
interference occurs when a wireless technology is interfered by signals from
the different technology [34]. Classic examples include licensed LTE vs Wi-Fi
[38, 39], unlicensed LTE versions222Unlicensed LTE versions include LTE-
Licensed Assisted Access (LTE-LAA), LTE in Unlicensed Spectrum (LTE-U), LTE-
WLAN Aggregation (LWA) and Multefire. In general, these unlicensed LTE
technologies are either license-anchored or non license-anchored. For license-
anchored systems (LTE-U, LAA) the primary carrier (anchor) uses the licensed
parts of the spectrum and for non license-anchored systems (Multefire) both
control and data traffic are sent through the unlicensed spectrum [40]. vs Wi-
Fi [40, 41], radar333While radars are the incumbent users of the 5.150-5.350
GHz and 5.470-5.725 GHZ bands, Wi-Fi devices operating in the 5GHz band also
share the spectrum when no radar system is transmitting [40]. This can lead to
interference from the Wi-Fi devices to the legacy radar systems. vs Wi-Fi [42,
43], dedicated short range communication (DSRC)444DSRC supports applications
based on vehicular communication [44]. The IEEE 802.11p standard, an amendment
to the IEEE 802.11 standard, was developed to incorporate the wireless access
in vehicular environments (WAVE) vehicular communication system in order to
support intelligent transportation systems. This technology operates between
5.85-5.925 GHz band. Since Wi-Fi also operates as a secondary user in the same
radar band, they can often times cause interference to the legacy radar
technologies. vs Wi-Fi [45, 46].

Due to coexistence issues that can lead to interference between these
heterogeneous technologies, several studies have proposed several interference
suppression techniques [47, 48, 49, 50, 51] to better explore the manner in
which these technologies interact. In most cases, CTI exhibit distinct
recognizable patterns that interference-aware protocols can leverage [47].
Grunau et. al. [34] showed that the impact of CTI for an IEEE 802.11 b/g
signal interfered by IEEE 802.15.1 and IEEE 802.15.1 signals was relatively
benign when compared to the converse. This is because both IEEE 802.15.4 and
IEEE 802.15.1 signals result in lesser overlapping interference compared to
IEEE 802.11 b/g signal because they have narrower bandwidth. However, for low-
power wireless devices, CTI can disproportionately impact their coexistence
with other devices when sharing the same band [48].

Table I shows wireless technologies such as IEEE 802.11 (the standard that
defines Wi-Fi), Bluetooth, Bluetooth low energy (BLE), wireless USB, IEEE
802.15.4555IEEE 802.15.4 is the basis for Zigbee, ISA100.11a, WirelessHART,
IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN) and cordless
phone (including analog and frequency hopping phones). Other technologies
include microwave oven, wireless camera, baby monitors, car alarms, video
devices amongst others. It defines the low-rate wireless personal area
networks (LR-WPANs)[52]., and others sharing the 2.4 GHz band. Despite the
fact that these technologies have different power levels and width of the
occupied spectrum, that they could still cause interference to one another
given the fact that they share the congested unlicensed 2.4 GHz band.

Figure 2: Interference Suppression Techniques

###  III-B Classification of Interference Suppression Techniques

Broadly speaking, interference suppression can be divided into the following
categories (shown in Figure 2): baseline techniques, diversity-based
techniques, thresholding-based techniques, interpolation techniques and deep
learning-based techniques. Furthermore, researchers have investigated
polynomial-based cancellation techniques to cancel SI [53, 54, 55].
Polynomial-based methods used in full duplex systems typically result in a
huge number of parameters which further make their implementation complex.
While this survey focuses on deep learning-based techniques, we briefly
describe non-deep learning techniques in this section to provide some
background information necessary to understand the comparison in Section VI.
Fig. 2 shows a taxonomy that includes both non-deep learning and deep learning
techniques for interference suppression.

Before the adoption of machine learning or deep learning methods to suppress
interference, researchers used various techniques to suppress or excise them.
Because many low-power wireless networks are power- and computation-
constrained, PHY layer information and time diversity of the wireless channel
have been proposed to suppress heterogeneous technology interference. For
example, Hithnawi [56] monitored link quality indicator (LQI) [57] values of
corrupted frames together with conventional threshold mechanisms to detect the
presence of interference. When interference is detected, the changes in
received signal strength indicator (RSSI) is collected. The RSSI changes are
captured by sampling the received signal. The RSSI readings is then sent to
the detection algorithm. The corrupted packet is reconstructed from subsequent
interfered re-transmission and the buffering of the correctly decoded bytes
[56].

While we acknowledge that earlier works have investigated interference
avoidance schemes, such as transmit power control [58], dynamic frequency
switching [59, 60, 61], amongst others, our survey focuses on techniques that
suppress interference. These techniques, shown in Fig. 2, include zeroing of
interference samples [62], iterative method with adaptive thresholding (IMAT)
[63, 64, 65], time domain parametric interpolation [66, 67], ramp filtering
(RFmin) [68, 69, 70] amongst other approaches. While zeroing out interference
components may be the simplest method in suppressing interference, it could
reduce SNR or even potentially zero out actual signal [27]. The RFmin
technique exploits the diversity of range spectrum over ramps [27]. In IMAT
approaches, the interfered samples are first zeroed and the corresponding
zeroed time-domain gap is then iteratively interpolated by replacing the gap
with the inverse Fourier transform of the actual dominant spectral component
[63, 64, 27]. The time domain parametric interpolation approach is similar to
the IMAT approach with the difference being the use of a parametric signal
model to interpolate the gaps resulting from zeroing out interfered signals
[66]. It is important to point out that the list of non-deep learning
techniques for interference suppression mentioned in this section is non-
exhaustive, we only highlight a few of those techniques since the main focus
of this survey paper is deep learning techniques for interference suppression.
Readers who are interested in these other techniques can refer to [1, 25, 26].
Fig. 2 shows popular algorithms for each category.

Deep learning techniques are generally divided into supervised and
unsupervised learning666The major distinction between supervised and
unsupervised learning is the use of labeled datasets. The former relies on
labeled inputs and outputs while the latter uses machine learning algorithms
to discover the structure of the unlabeled input data. For the supervised
learning techniques, CNN-based suppression techniques were discussed in [71,
72, 73, 74], deep neural network (DNN) based techniques were discussed in [75,
76, 77, 78, 79, 80, 33, 81, 82, 83, 84, 85], and recurrent neural network
(RNN) based techniques were investigated in [86, 87, 88, 89, 90]. For the
unsupervised learning techniques, autoencoder-based suppression techniques
were discussed in [91, 92, 93, 94, 95, 96, 97, 98].

Furthermore, machine learning (ML) based channel state information (CSI)
techniques [99] have also been studied to estimate the required transmit power
level necessary to suppress interference. This approach strongly relies on a
precise estimation of transmit power needed to suppress interference in a
wireless sensor network (WSN) application. Other methods, such as [100, 57]
have utilized lightweight feature extraction and lightweight decision trees to
detect and identify interference in Wi-Fi, Bluetooth, and microwave ovens.

Because of the plethora of devices contending to share the wireless spectrum,
conventional interference suppression approaches may not be scalable to
resolve associated interference issues that may result. Since deep learning
approaches have been found to improve classification accuracy for many
interference classification or identification schemes[101, 102, 51, 34],
researchers have started taking their investigation to the next level and
their potentials for interference suppression [72, 71, 86, 92]. We discuss
them in detail in section V.

###  III-C Deep Learning Fundamentals

In the last two decades, we have witnessed tremendous and unparalleled
breakthrough in computing hardware technologies. This breakthrough led to
affordable and pervasive computing resources. Take, for example, graphics
cards which have seen significant growth in computing power. Computers are now
capable of doing complex operations in such a short amount of time. Deep
learning techniques allow computers to build complex concepts out of simpler
concepts [103]. Broadly speaking, there are three types of deep learning
algorithms: supervised, unsupervised and reinforcement learning. Supervised
learning techniques typically learn from labeled data [104, 105] while
unsupervised techniques do not require labels to learn from the data [106].
Reinforcement learning [107] on the other hand deals with an intelligent agent
that take actions in order to maximize its cumulative reward. Semi-supervised
learning [108, 109, 110], a technique that combines supervised (labeled data)
and unsupervised (unlabeled data) learning has also received a lot of
attention in recent times. While earlier works such as Longi et. al. [111]
utilized a semi-supervised learning technique to detect interference sources
from wireless local area networks (WLAN), we note that, to the best of our
knowledge, their use in interference suppression has received little to no
attention.

Deep learning techniques have enjoyed significant success in solving
challenging problems in several application domains, including image
classification, medical bio-informatics, natural language translation amongst
others. Motivated by these developments, researchers in wireless communication
have embraced deep learning to address the most difficult problems in wireless
communications and networking, such as interference identification,
classification [101, 34] and suppression [92], modulation recognition [112],
transmitter classification [113], spectrum sensing [114], traffic
classification [115] amongst other challenges. In addition, deep learning
techniques can be very useful in managing coexistence issues arising from the
deployment of fifth generation (5G) New Radio (NR) and sixth generation (6G)
technologies with regards to scalability, generalization and the ability to
learn from the data rather than reliance on domain expertise. Therefore, using
deep learning to estimate, predict and suppress time-varying interference
strengthens interference resilience of the PHY layer.

Earlier applications of deep learning to wireless communications have
investigated DL-based end-to-end systems for multiple user, multiple antenna
systems that mutually optimize the transmitter and the receiver so as to
improve performance compared to traditional non-DL or non-ML approaches [116,
95]. Deep learning techniques have also been used for channel modeling and
estimation. For instance, generative adversarial networks (GANs)777GANs learn
deep representations by training a pair of networks in competition with each
other. It consists of a generator and a discriminator network in which the
generator aims to trick the discriminator by generating fake samples. The
discriminator on the other hand receives both the fake samples and the real
samples and tries to tell them apart. Both generator and discriminator are
usually implemented with neural networks [117]. Hence, one neural network is
pitted against another neural network with each improving its capabilities
over time. were used by [118, 119] to approximate stochastic channel response
from the raw data for real time channel modeling. Furthermore, DL-based OFDM
receivers have been used to learn the channel between a transmitter and a
receiver and also decode the received signal [120]. DL-based belief
propagation algorithm that cascades CNN to a belief propagation network was
utilized by Liang et al. [121] to accurately estimate noise. While these
techniques are not interference suppression techniques, they may provide
useful information for subsequent interference suppression processing.

####  III-C1 Convolutional Neural Network

CNNs are supervised deep learning techniques that learn the relationship
between input data and their corresponding labels [122]. For instance, in
[123], the authors proposed an approach to suppress radio frequency
interference (RFI) in radio frequency data using the U-Net. A U-Net is a CNN
algorithm that is used to classify clean signal and RFI signatures in 2D time-
ordered data acquired from a radio telescope. While this work did focus on the
use of a deep learning algorithm to classify interference, the technique used
was only validated for cosmology applications that deals with satellite
systems, air traffic communications, radio telescope, etc. Just like RFI
suppression research, radar interference suppression is also another well-
studied research area [86, 71, 92, 124, 72, 125]. Using CNNs to suppress
interference is still an emerging area of research. This is because there are
many factors to the successful adoption of CNNs to interference suppression.
One such factor is the availability of benchmark datasets.

Figure 3: A generic CNN architecture with convolution layers, ReLU activation
function, pooling layer interlaced with each other and two fully connected
layers

Unlike in other deep learning application domains where there is a plethora of
publicly available benchmark datasets, only a handful of datasets are
available for interference suppression research. In addition, it is quite
difficult to create a dataset that can be used for training and testing
different models to address different problems in various wireless
communications/networking applications. This is because the wireless channel
is stochastic in nature and there can be different realizations at different
points in time. More so, wireless channels are highly dynamic, it is very
likely the training data generated in one scenario could not be applicable to
other scenarios. Hence, it is not surprising that there are no publicly
available benchmark datasets for the vast majority of wireless research
problems. Researchers working on these problems need to generate their own
datasets, which is very resource-intensive and time consuming. However,
datasets that capture unique environments are still valuable in training deep
learning algorithms despite the fact that they are not representative of all
channel environments. As noted earlier, SDRs can be leveraged or collecting
data in multiple channel environments.

Fig. 3 shows a generic CNN architecture with convolution layers, rectified
linear unit (ReLU) activation layers and pooling layers interlaced with one
another before the output is then sent to two fully connected layers. This
generic architecture is similar to the one described by [126]. The input data
could be images as seen in the MNIST data with size $28\times 28\times 1$
[127]. The input image is then passed through the convolution layer that uses
convolution filters or kernels to run over the input array and compute a dot
product in order to extract different features from the input array. The
kernel could be slid in increment of one or more cells, this is usually
referred to as the stride. In order to introduce non-linearity to the model,
activation functions are used to learn complex mappings between the input data
and the response variables. Examples of activation functions include ReLU,
sigmoid, tanh, leaky ReLU, etc. The ReLU activation function is the most
common of them because it is a piece-wise linear function defined as follows

$ReLU(x)=max(0,x).$ (1)

From equation 1, if $x>0$, the function is positive and if $x<0$, the output
will be zero. The pooling layer decreases the size of the features that were
convolved so as to reduce overfitting. The fully connected layer is very
useful in capturing non-linear combinations of the high-level features. There
could be more than one fully-connected layers. Depending on the application,
the last fully-connected layer’s output dimension is based on the number of
classes. CNNs are capable of learning over both time and frequency domain,
hence, their usefulness in modelling devices with output that is not
restricted to frequency outputs alone [51]. Furthermore, CNNs are very useful
when the data captures non-linearities in the time domain specifically for
identification or classification tasks [128].

Prior studies have demonstrated that CNN can automatically extract device-
specific features by analyzing a huge amount of training data to improve the
performance. For instance, authors in [128] presented deep learning techniques
useful in fingerprinting several IEEE 802.15.4 devices. Further, another work
[129] investigated different the combination of machine learning algorithms
with wavelet transforms to uniquely identify and classify disparate wireless
transmitters. On a different note, DL-based modulation recognition solutions
have also attracted much attention in recent years. For example, [112, 130]
leveraged CNN algorithms to recognize modulation schemes based on sequences of
constellation points. Several other papers [131, 132, 133] studied the
application of deep learning to modulation recognition problems.

Some authors investigated interference classification using deep neural
networks. For instance, [134] developed a technique for classifying
interference at the PHY layer. The interference classified include single
tone, chirp, filtered noise and unknown modulated signal. They extracted
cyclic spectrum and spectrum information from the received signal. While their
overall finding identified which spectrum information feature to extract, they
did not consider ways to suppress interference sources.

Grunau et al. [34] proposed a wireless interference identification that
classified multiple Bluetooth, Wi-Fi and IEEE 802.15.4 interfering signals in
the presence of the desired signal using a CNN. In their work, they generated
a multi-label dataset that consists of time- and frequency-limited sensing
snapshots that combines the desired signal with multiple interference signals.
While their work classified the interference sources with high accuracy, it
can be argued that given the apparent dissimilarities between the three
technologies, the task may have been trivial for a DL classifier. Furthermore,
there is no discussion on how to suppress the interference from the different
technologies. For interference suppression, however, authors such as [72, 71]
have shown that CNNs can learn to suppress interfering signals. We discuss
these techniques in section V-A.

####  III-C2 Autoencoders

Autoencoders (AE) have been studied as a technique to suppress interference in
applications such as radars and Wi-Fi. AE are neural networks that aim to
learn a compressed latent-space representation of the input data, that can be
successfully reconstructed into the original data [135]. The latent-space
representation can usually encode useful information about the interfering
signal that can then be suppressed at the decoder. They are composed of the
encoder which compresses the input into a latent-space representation and can
be generally represented by the function: $h=f(x)$ while the decoder
reconstructs the input from the latent representation. The decoder can be
represented by the function: $x^{\prime}=g(h)$. For an autoencoder, the main
task is to ensure that $x^{\prime}$ is as close as possible to the original
input, $x$. Fig. 4 shows a block diagram of a generic autoencoder with encoder
and decoder networks.

The latent space representation, $h$, has very useful properties since it
learns very important features in the input data. At a high level, we can
picture the autoencoder as a way of copying the input to the output. However,
when the copying process is constrained, such as, limiting $h$ to have a
dimension smaller than $x$, the autoencoder is compelled to learn the most
important features of the training data [136]. On the other hand, if the
autoencoder has too much capacity, it may end up not learning vital
information about the distribution of the input data [136]. The imposed
constraint is analogous to intentionally introducing a restriction or
bottleneck in the network which results in a compressed useful representation
of the original input. The lack of dependence between the input features could
make reconstruction difficult. On the other hand, if there is some correlation
between the input features, the imposed bottleneck can be leveraged to learn
the structure of the input [137]. Without the bottleneck or constraint, the
network simply memorizes the input values and passes it along through the
network to the output. The loss function is usually either mean square error
(MSE) or reconstruction loss which is the cross-entropy between the input and
the output. The network is penalized for creating outputs different from
input. To prevent the autoencoder from simply memorizing the input, it is
tuned in such a way that it is sufficiently sensitive to the input such that
it can precisely build a reconstruction while also adequately insensitive
enough to not overfit the training data [138]. One potential application of
autoencoders for interference suppression is that $h$ can also be pruned to
suppress or reject interference.

Figure 4: A Generic Autoencoder Architecture

Some general applications of autoencoders include data denoising[139],
dimensionality reduction[140], anomaly detection[141], image in-painting[142],
information retrieval[143], etc. In data denoising applications, an AE learns
a generalizable encoding and decoding function, thus, corrupting the input
data with noise may not significantly impact accurate reconstruction. On the
other hand, for dimensionality reduction applications, when compared to
principal component analysis (PCA) or other dimensionality reduction
techniques, autoencoders are able to easily learn data projections that are
more useful [136]. This helps to map from high feature space to low feature
space. As we discuss in section V-C, these unique features of autoencoders
made them very crucial to detecting and suppressing interference. In section
IV, we discuss publicly available interference datasets.

##  IV Publicly Available Interference Dataset

One of the key requirements for using deep learning to solve any problem is
the access to benchmark datasets. Take for instance, in the image
processing/computer vision and the medical bioinformatics research areas where
deep learning techniques are very pervasive, there are a plethora of benchmark
datasets available for use by their corresponding research communities. It is
important to also point out that utilizing simulated and synthetic dataset is
frowned upon by deep learning experts in fields such as computer vision and
image processing. This is because the performance of such algorithms on these
dataset types often skew their expectation for real world applications. For
example, data collected in a simulated environment with AWGN channel and
fading differ significantly from data collected over-the-air (OTA).
Furthermore, access to benchmark datasets helps to further promote cutting
edge research as well as innovation in the field. For instance, in image
processing domain, the modified National Institute of Standards and Technology
(MNIST) database consists of $70k$ images for both training and testing [127]
while there is also an extended dataset, EMNIST [144] that has $280k$ images
of handwritten digits and characters. Despite the paucity of such benchmark
datasets in the interference suppression research, there are still a few
authors and useful community resources where interference datasets can be
accessed. In this section we provide the readers, who are interested in deep
learning-based interference classification or suppression, high level
information on these resources. Using these datasets and with appropriate
scaling and sample rate conversion, synthetic datasets can be created using
real data.

###  IV-A Radar Interference Dataset

Ristea et al. [71] automatically generated $48k$ samples of an automotive
radar dataset with only one interference source. While this may seem
insufficient, it is indeed a good contribution to the largely unavailable
public generated databases for interference suppression. They fixed parameters
such as bandwidth, sampling frequency, center frequency and sweep time while
other parameters such as SNR, SIR, target amplitude, distance, and phase were
selected using a uniform distribution between a lower and upper bound. Details
of these values can be found in [71]. Their data samples contain three
categories of data such as time domain signal with no interference, time
domain signal with interference and a label vector with complex amplitude
values in target location. The dataset is available for download in [145].
Radar signals are especially important datasets because of the extensive
spectrum sharing interference between radar and communication signals, and the
dynamic behavior of radar makes it difficult to simulate.

###  IV-B Radio Frequency Interference Dataset

The radio frequency interference (RFI) dataset was generated by authors of
paper [146]. The dataset was created by combining the signal of interest (SOI)
with three popular jammers: continuous-wave interference (CWI), multi-
continuous-wave interference (MCWI) and chirp interference (CI). In addition,
the dataset was generated under different signal to noise ratios (SNR). The
desired signal, a real-time video stream, was modulated using GNU radio and
then transmitted via a Universal Software Radio Peripheral (USRP-N210). The
authors utilized a combiner to combine the respective jamming signal with the
SOI. The receiver used was a MegaBee modem. Details of their dataset
generation can be found in [146]. Fig. 5 shows a scalogram plot888The
scalogram represents the squared magnitude of continuous wavelet transform
(CWT), a type of wavelet transform, which is used for non-stationary and
transient signal analysis. The CWT is a non-numerical signal transformation
function that continuously varies translation and scale parameter of the
wavelets. It essentially provides an detailed or over-complete representation
of a signal. Authors in [147] noted that CWT is resistant to noise in the
signal . of the RFI dataset. It is clear from the scalogram plot that distinct
features of each class of interference can be easily learned by a deep
learning algorithm.

Figure 5: A scalogram plot of the RFI dataset from [146]. (Left: The original
signal of interest with no interference; Center Left: The signal of interest
with multi-continuous wave interference; Center Right: The signal of interest
with chirp interference; Right: The signal of interest with continuous wave
interference). The figure was taken from paper [146] titled: An Efficient
Radio Frequency Interference (RFI) Recognition and Characterization Using End-
to-End Transfer Learning; published as an open access article in the Journal
of Applied Sciences.

###  IV-C CRAWDAD Dataset

The Community Resource for Archiving Wireless Data at Dartmouth (CRAWDAD) was
established to bridge the gap in collecting data captured from production
wireless networks so as to understand how real users and devices use networks
under real conditions [148]. At the time of writing, there are 125 datasets
for different wireless communication applications and more than $14k$ users
from more than $120$ countries [148]. The dataset, contributed by Schmidt et
al. [101], contains traces of IEEE 802.11b/g, IEEE 802.15.4 and Bluetooth
packet transmissions with varying SNRs in the baseband [149]. The authors also
added frequency offsets in the baseband [34]. In total there are fifteen
different classes with ten of those classes being IEEE 802.15.1 devices, three
being IEEE 802.11 and the remaining two being IEEE 802.15.4 devices. There are
two other interference datasets on the CRAWDAD database that are targeted
towards the measurement of HTTP requests over 802.11 protocol in a dense
wireless classroom [150] and a trace-set that includes received signal
strength indicator (RSSI) for frames that were correctly received at receiver
nodes when noise was injected [151]. For more on the CRAWDAD datasets, readers
are directed to [148].

###  IV-D Wireless Interference Signal Dataset

Authors in [152] collected experimental data in four different environments
with one of those environments having no detectable interference, another with
controlled interference and the other two environments with uncontrolled
interference from real devices. Interference sources for their experiments
include Bluetooth and Bluetooth Low Energy (IEEE 802.15.1 and IEEE 802.15.1
BLE respectively), ZigBee (IEEE 802.15.4) and WLAN (IEEE 802.11). In total
their dataset consists of more than $110k$ interference bursts of known
origin. One key contribution of their work is the heterogeneous nature of the
interference scenarios they considered which makes for a robust dataset that
can be used by the wireless community at large.

Table II shows a high level comparison between the datasets mentioned in this
paper.

TABLE II: Comparison Between Interference Datasets Dataset |  Interference type |  Signal Types
---|---|---
Radar Interference Dataset [145] |  Homogeneous technology interference |  $3$ classes including:  • Time domain signal with interference • Time domain without interference • Amplitude information for targets
Radio Frequency Interference Dataset [146] |  Heterogeneous technology interference |  $4$ classes including:  • Signal of Interest (SOI) • Continuous-wave interference (CWI) • Multi-continuous-wave interference (MCWI) • Chirp interference (CI)
CRAWDAD Dataset [101] |  Homogeneous and heterogeneous technology interference |  $15$ classes including  • Bluetooth ($10$) • WLAN ($3$) • ZigBee ($2$)
Wireless Signal Interference Dataset [152] |  Heterogeneous technology interference |  $4$ classes including:  • Bluetooth • Bluetooth Low Energy (BLE) • ZigBee • WLAN

##  V Deep Learning-Based Interference Suppression Techniques

In this section, we survey the most popular DL-based interference suppression
techniques shown in Figure 2. The most common deep learning techniques
discussed in this section include CNN, autoencoders, RNN and deep neural
networks (DNNs). While there are other deep learning techniques in general,
our research shows that these four techniques are the most common in the
interference suppression literature.

###  V-A Contributions for Interference Suppression Using CNN

In this section, we investigate CNN-based techniques for interference
suppression. CNNs, an example of a model-free method, have shown impressive
performance in many other domains (e.g. radio fingerprinting [128], image
classification [144, 153], etc). CNNs can utilize only raw IQ data or other
channel-based information to learn the unique features in interfering signals
[154, 155, 156]. Typically, CNN based techniques include layers of
convolution, pooling ReLU and batch normalization operations as shown in Fig.
6. CNNs, unlike feature-based machine learning (ML) techniques do not require
that a domain expert handcraft the features. Instead, the algorithm through
the use of convolutional layers and a feature map, learns salient features
necessary for suppressing interference. CNNs are capable of learning over both
time and frequency domain, hence, they are useful in modeling devices with
output that is not restricted to frequency domains alone [51]. While earlier
works [102, 101, 51, 34] have investigated interference classification using
CNN, several recent works have shown that CNNs can also go beyond classifying
interference to actually suppressing them. For instance, Rock et. al. [72]
utilized CNNs to find structured information in the input data to suppress
automotive radar interference. One key application of their CNN model is to
de-noise and suppress interference using a dataset that consists of clean data
and data with interference. They used metrics such as signal to interference
ratio (SIR) and signal to noise ratio (SNR) to scale the interference and
noise powers in relation to the actual object signal power. In their CNN model
(shown in Fig. 6), the first layer consists of a convolution layer and a ReLU
activation function while the next four layers consist of batch
normalization999Batch normalization is usually used in deep learning training
to reduce the number of epochs and stabilize the learning process by
standardizing the inputs to a layer for every mini-batch [157]., convolution
and ReLU activation function operations, the last layer (before the output
layer) consists of a batch normalization and convolution operations.
Convolutional layers in CNNs often learn unique artefacts in the input data
that help in identifying interfering signals in the data. Their results show
that the CNN based suppression approach outperform non-deep learning
techniques such as zeroing, IMAT and RFmin. For instance their CNN based
suppression model outperformed zeroing, ramp filtering and IMAT techniques by
more than 40% using the signal to interference plus noise ratio (SINR) metric
[72]. Despite these gains, the CNN-based suppression techniques suffered a
relatively high error vector magnitude (EVM) performance when compared to the
conventional suppression approaches such as zeroing, IMAT and RFmin. For radar
applications, such as the one considered in the authors’ study, a high EVM may
result in distortions in object peak values [72]. It is important to point out
that the study done by Rock et. al. [72] utilized a simulation framework. This
may imply that some of parts of their results may not hold for over-the-air
(OTA) applications. This is because real world OTA applications may be subject
to more channel uncertainties such as fading, noise, mobility and other
factors. It is fair to say that one might expect different results for QAM
communication signals with information carried in the signal phase.

Figure 6: CNN Architecture proposed by Rock et al. [72] for radar signal
denoising. The figure was reproduced from paper [72] titled: Complex Signal
Denoising and Interference Mitigation for Automotive Radar Using Convolutional
Neural Networks; published at the 22nd IEEE International Conference on
Information Fusion (FUSION).

Similar to [72], Ristea et al. [71] utilized a fully convolutional neural
network, another CNN approach, to learn the patterns in the input data using a
small amount of learnable parameters. They proposed two deep learning
architectures that outperformed conventional zeroing techniques of
interference rejection [62] by taking spectrograms of input signals (with
$1024$ samples) as input and then arriving at clean signals at the output. The
input signals consist of noise and uncorrelated interference which is learned
using the fully convolutional neural network (FCN). To obtain the beat signal,
they first convert the time domain signal into a spectrogram by using short
time Fourier transform (STFT). To get the ground truth, the authors perform an
FFT on the time domain labels and consequently trained the network to map the
STFT input to the FFT output. Generally, in the wireless communication
community, many researchers utilize the I/Q samples to train the deep learning
architecture. A good lesson from this paper stems from the row dimension of
the I/Q samples having a value of $2$, the filters used in the convolutional
layer may be too small and hence leading to overfitting or further
complicating the CNN algorithm. This is because using small sized filters tend
to make the model less generalizable, and thus, learn specific features in the
training data. In another work, Rock et al. [73] investigated CNN-based
quantization techniques to mitigate or de-noise interfering radar signals.
Specifically, their work focused on memory and computational requirements
associated with using deep learning models. The major motivation for this is
that high memory requirements could limit the energy efficiency and the time
it takes to execute operations when an embedded hardware is used [73]. To
account for this, the authors explore quantization capabilities of different
CNN architectures. Their results showed that the CNN-based mitigation models
performed better than the three traditional interference mitigation methods
considered (zeroing, IMAT and ramp filtering) and are also resilient against
different interference levels and patterns. They also considered interference
signals from real-sensor measurements that was combined with an object signal
from an inner-city measurement campaign, their result showed an SNR
improvement of 7dB after CNN-based mitigation [73].

Further, Fuchs et al. [74] proposed a complex valued CNN (CVCNN) to suppress
mutual interference in automotive radar systems. The main challenge the
authors sought to address was the problem of reconstructing the phase of clean
signals from the real and imaginary parts of the complex spectra. This is
because many object detection algorithms typically rely on only the magnitude
spectrum component of the interference mitigated maps, and thus may not
include important phase information. While including the phase information
adds more complexity to the algorithm, complex-valued analysis can also
restrict the degrees of freedom [74]. In [74], the authors found that such a
restriction can leverage the physical characteristics of the data to enforce
signal transformations thereby reducing the complexity. Their CVCNN model,
thus, uses complex-valued analysis for all the operations. This implies that
the interconnection between real and imaginary parts in the model architecture
are taken into consideration. This also implies that the algorithm does
complex convolution, complex batch normalization and complex ReLU activation
function. Their results showed that the CVCNN method outperformed the real-
valued CNN, zeroing, RFmin and IMAT techniques in terms of F1-score, EVM and
peak phase MSE (PPMSE)101010PPMSE is the measure of the average squared
difference between the angle of the clean range-Doppler signal vector and the
predicted range-Doppler signal of all detected object peaks [74]..

In practice, a CNN model should be able to generalize well so that it can
appropriately classify samples from the test data. To solve this, many
researchers use preprocessing techniques such as spectrograms [71] or
continuous wavelet transforms (CWTs) [158, 159] to further increase the
dimension of the input data along the time-frequency axis. It is very
important to preprocess time domain signals to produce a 2D image
representation so that CNNs can better learn salient features in the input
data. In [71], the authors used a spectrogram which led to an input size of
$154\times 2048$. Their results showed that the FCN algorithm moderately
outperformed the conventional suppression method such as zeroing using the
area under the receiver operating characteristics curve (AUC) metric. We note
that their technique improves the SNR and AUC scores of the actual signal and
not just the spectral representation. This goes to show that combining
traditional signal preprocessing techniques with deep learning can
significantly improve interference suppression performance while reducing
overfitting.

Figure 7: A CDF comparison of the sample-wise F1-Score between two CNN models,
zeroing, IMAT and Ramp filtering interference mitigation techniques (where $x$
is a real-valued number). The figure was taken from paper [73] titled:
Resource-Efficient Deep Neural Networks for Automotive Radar Interference
Mitigation; published in IEEE Journal of Selected Topics in Signal Processing,
vol. 15.

It is not surprising to see that the research community is slowly adopting the
idea of preprocessing input signal dataset since it provides an opportunity to
explore the best of both worlds (i.e. signal processing and deep learning
applications). More so, preprocessing techniques (such as continuous wavelet
transform (CWT)) can be very useful in learning attributes unique to the
transceivers, such as non-linearity in the power amplifier, oscillator
imperfections, amongst other unique attributes. In over-the-air transmissions,
both wireless interference and the stochasticity of the wireless channel can
result in the attenuation of unique attributes in the transmitted signals
[160]. To magnify these attributes, raw IQ signals can be pre-processed using
signal processing transforms similar to CWT such as discrete wavelet transform
(DWT), STFT, Fourier transforms amongst others. Since Fourier transforms does
not mark the start and the end of the sine wave, the STFT analyzes several
smaller section of the signal. In DWT applications, a sparse representation of
the signal is provided. CWT is very useful in enabling a clear representation
of many frequency components of a signal as a function of time. The CWT
introduces spatial correlations that may not be easily observable in the
original signals. The unique features of each transmitter is highlighted in a
2D representation, thus improving the performance of the CNN classifier [158].
As a result of this, CWT is better choice for signal or time series analysis
because of its fine grained resolution. Such a resolution is obtained by
decomposing the signal into wavelets using basis functions (scaled and shifted
versions of the mother wavelet [159, 161]). Finally, a two-dimensional matrix
where the rows correspond to the scales and the column corresponding to the
length of the signal is then generated. It has been shown that combining
signal preprocessing techniques with CNN can significantly improve performance
[159].

###  V-B Contributions for Interference Suppression Using RNN

Some other works including [86] have used recurrent neural networks (RNN) with
gated recurrent unit (GRU) to remove automotive radar interference and to
simultaneously reconstruct the transmit signal. Unlike many feed-forward
neural networks where the collected samples are independent of each other, RNN
leverage the dependencies. RNNs capture the dynamics of sequences via cycles
in the network of nodes [162]. Since the received samples are collected
sequentially in time domain, RNNs can indeed be used since previous studies
show that they are very useful for sequential data. Similar to the deep
learning methods discussed, their technique performed better than time domain
thresholding described in [163] and the threshold-free frequency domain
interference cancellation method that attenuates the magnitude response of the
recorded chirps described in [68].

From the foregoing, we observe that Mun et al. [86] successfully processed
sequential data using an RNN model with a gated recurrent unit (GRU). However,
they were not able to reconstruct the original signal in the face of
interference. To account for the inability of their RNN model to reconstruct
the original signal, Mun [87] proposed an RNN with self attention to mitigate
OFDM-based and frequency modulated continuous waveform (FMCW) radar
interference. To restore the original signal perfectly, the RNN model learned
the information on the surrounding signals and then applied an attention
mechanism [164] to clearly detect the range and velocity of the target signal
using an FFT. Specifically, the authors leveraged the attention blocks to
capture sequential relationship between the time steps since the interference
partially occurred in the entire data. Fig. 8 shows 2D FFT plot of the
received signal with radar interference and the same signal after the radar
interference has been removed. This plot shows the power of RNNs in
suppressing interference in radar applications. It is important to note that
their technique was tested on only simulated data and results of the real
world equivalent is not provided.

On a different note, Mao et al. [88] investigated an interference suppression
technique for global positioning system (GPS) using a derivative-free Kalman
Filter-based RNN. An adaptive unscented Kalman Filter-based RNN (UKF-RNN) was
used to suppress continuous wave interference (CWI), multi-tone CWI, swept CWI
and pulsed CWI. While researchers have shown that RNNs are better at modeling
non-linear time series systems than signal processing methods [165], some
interfering signals may be non-stationary, hence, a model that captures this
phenomenon is even more useful for suppressing non-stationary interference. In
general, results in [88] show that their UKF-RNN algorithm increased SNR
improvement while also attenuating the mean square prediction error (MSPE) of
the received signals in the presence of interference when compared to other
conventional methods. With regards to non-stationary interfering signals, the
authors’ UKF-RNN algorithm offered SNR improvements between 3.34 dB to 8.30 dB
more over other methods [88].

Figure 8: A plot showing the 2D FFT result of an RNN-based Interference
Mitigation Approach Left: With radar interference; Right: After radar
interference has been removed with RNN. The figure was taken from paper [87]
titled: Automotive Radar Signal Interference Mitigation Using RNN with Self
Attention; published in IEEE International Conference on Acoustics, Speech and
Signal Processing (ICASSP).

###  V-C Contributions for Interference Suppression Using Autoencoders

Another important deep learning technique utilized for interference
suppression is the deep autoencoder. Autoencoders are very unique in
interference suppression because the encoder network can learn unique
interfering components of the input signal (with interference) in the latent
space representation. Authors in [92] investigated an CNN-based autoencoder
technique that was used to suppress automotive radar interference. Similar to
[72], their approach involves using a denoising mechanism to suppress
interference. They first trained corrupted input signals with their clean non-
interfered version. Using deep learning models typically require access to a
huge amount of quality data. Their approach ensured that they collected a
large amount of range-Doppler images.

Similarly, Lin et. al. [91] proposed a method that used denoising autoencoders
to learn patterns of interference with unknown structures to suppress
heterogeneous technology interference. The main idea behind this approach is
to allow the algorithm to learn the distribution of the input data and thus
consequently learn the distribution of the interference component so that it
can be suppressed. It is important to note that in [91], the authors did not
assume any form of coordination or synchronization between the transmitter,
receiver and interferer. The authors utilized a generative adversarial network
(GAN) to generate synthetic training samples and an autoencoder (denoising) to
recover a clean Wi-Fi signal. The synthetic training samples were generated as
a means of avoiding the steep cost of collecting a huge amount of data in
terms of time and resources. Hence, the GAN was used to synthetically create
corrupted Wi-Fi signals that the authors used for training their AE-based
suppression model. GAN is basically a network that involves a generator and a
discriminator that play a zero-sum game where the generator generates data
that is as close as possible to the real data and the discriminator determines
whether the generated data is real or fake [117]. Specifically, their
experimental set up included a USRP N200 SDR (within interfering range of a
microwave oven) that was deployed as a receiver to collect the signal
transmitted when the microwave oven is turned on while Wi-Fi signals are being
transmitted. One key lesson learned from their work is that denoising
autoencoders perform well when the interfering power is larger than the
amplitude of the desired signal (Wi-Fi, in their scenario). Another key
benefit of this approach is that it does not incur any signalling overhead
since the algorithm itself is passive [91].

Further, Chen et al. [98] investigated an approach that combined an
autoencoder network with an interference detection filter to suppress
automotive radar interference. The authors found that using a gated
convolution, the encoder network can learn the pattern of the residual signal
free of interference. In the decoder network, the interfering portion of the
signal can then be recovered. Their results showed that the method improved
SINR while also ensuring robustness of the radar measurements in scenarios
that have significant interference not represented in the training dataset.
Fig. 9, taken from [98], shows a plot of the SINR (in $dB$) against the
percentage of discarded samples that compares different interference
mitigation techniques such as zeroing, auto-regressive (AR) models,
autoencoders and the original signals. We see that at 15% (or greater) of
discarded samples, the SINR of both AR and zeroing methods decreased while the
autoencoder has a more robust performance.

Figure 9: Comparison of Autoencoder-based Interference Mitigation technique
with Zeroing and Auto-regressive models. The figure was taken from paper [98]
titled: A DNN Autoencoder for Automotive Radar Interference Mitigation;
published in IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP).

Channel autoencoder-based techniques have also been used in joint optimization
of transmitters and receivers to suppress interference [93, 94, 95, 96, 97].
For instance, a channel autoencoder-based interference suppression technique
where an encoder and a decoder were jointly optimized in the presence of an
interfering signal in a MIMO communication system was investigated by Erpek et
al. [95]. The authors considered a Gaussian co-channel interference scenario,
where the transmitters and the receivers use the same channel. Their technique
exploited the structure of the interfering signal and then minimized symbol
error rate (SER). Their results show that for a $2\times 2$ system at SINR
higher than $16$ dB, the AE-based model performs significantly better than the
baseline (single user single input single output (SISO) systems using QPSK
modulation with no interference and no channel coding). One key benefit of
jointly training the transceiver in the autoencoder system is that the
receivers learn to suppress interference and perform better in comparison to
baseline techniques. Despite the fact that the baseline approach may perform
better when channel coding is utilized, the autoencoder based suppression
scheme from [95] still significantly performed better than the uncoded
conventional communication system because it removes the interference. The
authors also showed that the autoencoder performance will continue to improve
as the network is trained longer at every SINR level.

###  V-D Contributions for Interference Suppression Using DNNs

Next, we look at deep learning techniques that utilized deep neural networks
(DNN) to suppress interference. Typically, many other deep learning algorithms
can be regarded as subcategories of DNNs. In this section we consider neural
networks, without a convolutional structure, that have at least 4 layers with
the inclusion of the input and output layers.

DNN-based methods are particularly useful in self-interference cancellation
(SIC) applications since they can successfully learn a large number of
parameters with a much lower computation complexity. For instance, Muranov et
al. [80] proposed two DNN-based methods - time-invariant & time-varying non-
linear distortion - to mitigate non-linear SI in full duplex relay link. For
both methods, they utilized a feed-forward DNN that has two hidden layers and
ReLU activation function. For the former, they utilized a DNN to estimate to
estimate the non-linear distortion that does not vary with time and then
utilized a least squares method to estimate the SI for a time varying channel.
For the latter method, they used a transfer learning based DNN to estimate the
time varying non-linear distortion. First, they decompose the SI signal into
its linear and non-linear components and then estimated and subtracted the
linear component from the SI which is then fed to the DNN. Fig. 10 shows a
comparison between the investigated approaches (TID and TVD) and other methods
used for SIC. We note that the DNN-based canceller shown in Fig. 10 proposed
by [33] is another non-linear interference cancellation technique that was
developed to outperform non-deep learning non-linear cancellation techniques
that relied on polynomial bases functions. Similar to [80], DNN was used to
construct the non-linear component of the digital signal. The major difference
between [80] and [33] is that the latter utilized only one hidden layer and
thus less complex than the former. This is not surprising since the goal of
the [33] was to improve computation complexity in comparison to non-DNN
techniques like polynomial-based cancellers. In [83], the authors investigated
a hardware implementation of the SIC technique proposed in [33].

Figure 10: Comparison of two DNN-based Self Interference Suppression
Techniques with another DNN-based SIC technique and other non-DNN based
techniques. The figure was taken from paper [80] titled: On Deep Learning
Assisted Self-Interference Estimation in a Full-Duplex Relay Link; published
in IEEE Wireless Communications Letters.

In another effort to suppress SIC, Guo et al. [81] utilized a DNN technique to
cancel non-linear SI. They collected channel data using an SDR testbed
consisting of universal software radio peripheral (USRP) in different
scenarios transmitting images, texts and numbers. Their DNn structure consist
of two hidden layers each followed by ReLU activation function layers. Simply
put, their approach showed that their DNN model is stable, fast and can
successfully cancel SI with a high accuracy. Shi et al. investigated a feed
forward NN to reconstruct SI signal. First, an adaptive linear filter was used
to extract the transmitted baseband signal features and then the features were
utilized to train the NN. Similar to [33], their feed forward NN consist of
one hidden layer and suffers from a similar shortcoming. It is important to
note that while DNN techniques outperform non-DNN techniques in SI suppression
across different channel environment and modulation systems, we also observed
that other DNN-based SI suppression techniques either reduce computation
complexity or require fewer hardware resources and may not necessarily
outperform polynomial-based SI suppression techniques.

Another paper that utilized a DNN for interference suppression is [75]. In
this paper, Xu et. al. [75] developed a technique to cancel interference for
non-orthogonal multiple access (NoMA) techniques using a DNN. In OFDM based
systems, the sub-carriers are orthogonal to one another, hence the issue of
cross interference is prevented. However, OFDM-based techniques usually
require that the sub-carriers are spaced and the cyclic prefix (CP) is used to
ensure that the receiver accurately demodulates the signal. This can sometimes
impact spectral efficiency [166] since some parts of the much needed bandwidth
is required to accommodate the overhead of adding the CPs. On the other hand,
NoMA algorithms such as the spectrally efficient frequency division
multiplexing (SEFDM) waveform utilize non-orthogonal sub-carriers, hence
leading to interference from adjacent sub-carriers [167]. In [75], their
neural network consisted of four layers including two hidden layers. Compared
to some of the CNN algorithms described above that utilized the ReLU
activation function, the authors in [75] utilized a sigmoid activation
function at each layer. They trained a DNN algorithm offline with $40k$ QAM
symbols generated using simulated random data. In evaluating the performance
of their DNN, they utilized the bit error rate (BER)111111Mathematically, BER
can be defined as a ratio of errors to the total number of bits. When a signal
is strong, the corresponding BER is small while the converse is true when a
signal is weak. metric for comparison. Although theirs is simulation-based
investigation, their key conclusion is that deep neural networks can suppress
inter carrier interference (ICI) within SEFDM signal waveform. Also, they
achieved a significant gain in SNR when compared to conventional techniques
that utilize hard decision detectors. Another key findings is that the neural
network performance depends strongly on the waveform characteristics. For
instance, the first neural network considered in [75] is the no connection-
neural network (N-NN) in which each symbol in one sub-carrier is an
independent network and the number of sub-networks is determined by the
quantity of sub-carriers in the SEFDM signals. In another neural network
proposed–partial connection-neural network (P-NN)–two symbols are connected in
one sub-net. Other neural network architectures are discussed in [75].

In another DNN-based interference suppression approach, Hager et al. [76]
investigated a learned digital backpropagation (LDBP) technique to mitigate
non-linear interference for a fiber optic communication application. The
designed network is based on unrolling the split-step Fourier method
(SSFM)121212SSFM is a numerical method to implement DBP [76]. that has a
similar functionality as a DNN [76]. In the LDBP approach, the SSFM serves as
a basis for a complex-valued DNN and then is further used to optimize the
network parameters. The network parameters include the weights, attenuation,
dispersion, and non-linearity parameters. Their results show that the LDBP
achieves a 50% complexity reduction when compared to the DBP. Also, one key
benefit to using this approach is that the LDBP method helps in choosing the
appropriate hyper-parameter choices (e.g., number of network layers, type of
non-linearity, etc.) that usually plagues DNN algorithm adoption. Compared to
standard “black-box” deep NNs, this approach leads to clear hyper-parameter
choices.

Further, Promsuk et al. [77] proposed a multi-layer perceptron (MLP) algorithm
to reduce ACI in an IoT network. In their proposed model, they utilized a FFT
data and amplitude as input to the MLP model. The input signals were binary
phase shift keying (BPSK) and 16QAM modulated signals that were synthetically
generated with path loss and small scale fading to mimic a more realistic IoT
network environment. To mitigate the ACI, they combined a MLP with a minimum
mean square error (MMSE) filter. The output from the model when the input data
is the amplitude is the estimated amplitude of the interference signal. The
estimated amplitude of the received signal is then evaluated using a system of
equation used in [77]. On the other hand, with an FFT input signal, the output
is the estimated FFT of the interference signal. An inverse FFT of the signal
is then computed. Their results showed that the performance of the MLP model
for both types of input data outperforms the method that utilized the MMSE
alone in terms of accuracy, precision, recall and F1-scores.

In another effort, Mosavi et al. [78] investigated a DNN interference
suppression technique for mitigating narrow-band interference for global
positioning systems (GPS) navigation application. Their method leverages
adaptive notch filters as a cascade filter and a simple DNN structure. The DNN
structure can be trained quickly because the adaptive notch filters are used
instead of updating weights and threshold typically done in MLP models. This
technique also reduces exponential computation incurred while updating weights
and thus makes it attractive for hardware implementation [78]. Their
simulation results showed that they were able to improve SNR of the intended
receiver in the presence of single-tone continuous wave interference (SCWI)
and multi-tone continuous wave interference (MCWI) when compared to MLP, RNN
and other compound methods.

Finally, Shi et al. [79] proposed an approach to suppress interference in a
direct sequence spread spectrum (DSSS) system using a combination of a power
distributing predominance wavelet packet transform (PDP-WPT) and an extended
backpropagation neural network (EBPNN) in a satellite navigation system. The
process involves a step that first tracks the interfering signal effectively
and then identifies the transmitted signal using the EBPNN based on its
structural simplicity. Their results show that the proposed method can
eradicate strong interference in the received signal with a good signal
correlation. Also, the mean square error (MSE) of the proposed technique is
smaller compared with other conventional techniques. Another benefit of the
technique is its higher convergence rate when compared to other time-domain
interference suppression methods [79].

In summary, deep neural networks can be used to model and extract
interference. One major benefit of DNNs over analytical models is that they
can easily adjust to real-world imperfections [76]. Next, we compare the
benefits and drawbacks of each deep learning techniques. This comparison helps
the reader to gain some insights into why deep learning techniques, in
general, are being adopted in interference suppression and what challenges may
be associated with using them.

##  VI Comparison Between the Approaches

Table III contains a summary of the DL-based approaches used by existing works
in the interference suppression literature. We look at features such as
application/type of interference, deep learning methods used, the type of
dataset and the performance metrics used for evaluation. The interference
types considered include radar, optical, and a variety of wireless
communication standards, amongst others. From our findings, there are more
papers on CNN for interference rejection or suppression compared to other deep
learning techniques.

###  VI-A Application/Interference Type

In papers where the type of interference considered is not explicitly stated
we provide the application considered (for example [75] with optical
communication systems). Other interference types considered include
heterogeneous technology interference between different wireless technologies
sharing the same frequency band such as those of [91, 168]. Some of the most
popular applications where interference suppression was studied is the
automotive radar applications such as [71, 92, 72, 73, 74].

###  VI-B Deep Learning Algorithm

The deep learning algorithms range from supervised learning techniques such as
vanilla deep neural networks (DNN), CNNs, RNNs; to unsupervised learning
techniques such as autoencoders that utilize a network of encoders and
decoders to learn salient features of interfering signals from input data and
then consequently removing them from the signals. It is not uncommon to train
the encoder or decoder using supervised algorithms such as CNN as done by
[92]. It is important to point out that using deep learning techniques usually
require a lot of data to be able to perform well. For some of these works [91,
92, 72], however, the quantity of the datasets used is relatively small when
compared to the recommendations from sample complexity study in [158].

###  VI-C Dataset Type

We also investigate the dataset types used for these tasks. Some works
utilized OTA data collected from real hardware transmitters and receivers in
the wild. Some other works also utilized simulated datasets generated from
MATLAB, NS3 and other simulation engines. Other researchers utilized synthetic
data which sometimes could be a combination of simulated and real devices.
Some papers [73, 98] also utilized simulation data in training and then tested
with actual OTA data. Although there are numerous sound simulation models, the
assumptions made by their creators do not always hold for OTA transmissions in
the wild. This negatively impacts the acceptability of their results by other
experts in the community.

TABLE III: Summary of Selected DL-Based Interference Suppression Techniques References |  Application/ interference type |  DL Algorithm |  Dataset type |  Dataset Size |  Performance Metric
---|---|---|---|---|---
[71] |  FMCW radars |  CNN |  Simulation |  $48k$ |  SNR, area under the receiver operating characteristics curve (AUC), mean absolute error (MAE) in dB
[72] |  FMCW radars/Chirp Sequence (CS) |  CNN |  Simulation |  2,250 scenarios |  SINR; EVM
[73] |  FMCW radars/CS |  CNN |  Simulation & OTA $+$ simulated interference |  $3k$ range Doppler (RD) maps |  F1 Score; RD magnitude spectra; SNR
[74] |  FMCW radars/CS |  CNN |  OTA $+$ simulated interference |  $2.5k$ range Doppler (RD) maps |  F1 Score; EVM; PPMSE
[75] |  Optical Communication Systems |  DNN |  Simulation |  $40k$ |  Bit error rate (BER)
[76] |  Non-linear interference in optical communication systems |  Learned DBP |  Simulation |  Unknown |  Transmit power vs Q-factor
[77] |  IoT Network |  Artificial Neural Network - Multi-layer Perceptron (MLP) |  Simulation |  Unknown |  CDF of BER
[78] |  Narrow-band interference for GPS navigation application |  DNN |  Simulation |  $62.854k$ test samples |  SNR; RMS
[79] |  Direct sequence spread spectrum (DSSS) system |  DNN - Extended backpropagation NN (EBPNN) |  Simulation |  Unknown |  BER; MSE; Correlation value
[80] |  Full Duplex Relay Link |  DNN |  Simulation |  $8k$ (training data size) |  BER, SNR
[81] |  In-band Full Duplex Wireless |  DNN |  Simulation & OTA |  Unknown |  MSE, BER
[82] |  Full Duplex Wireless Communication Systems |  Feed Forward Neural Network |  Simulation |  Unknown |  PSD
[83] |  Full Duplex Radios |  Neural Network |  Hardware Experimentation |  Unknown |  Throughput, Efficiency, Si Cancellation in dB
[85] |  Direct Sequence CDMA (DS-CDMA) |  Feed Forward Neural Network |  Simulation |  Unknown |  BER, SINR, SNR
[86] |  Time domain interference for chirp sequence radars |  Gated Recurrent Units (GRU), recurrent neural network (RNN) |  Simulation |  $150k$ |  Signal to remaining interference noise ratio (SRINR)
[87] |  Triangle FMCW; CS; multiple frequency shift keying (MFSK); OFDM radar interference |  Attention RNN |  Simulation |  $150k$ |  SINR; 2D- FFT
[88] |  Single-tone CWI; Multi-tone CWI (MCWI); Pulsed CWI, Linear FMCW |  RNN |  Simulation |  Unknown |  SNR Improvement; mean square prediction error (MSPE)
[91] |  Heterogeneous technology interference (Wi-Fi, ZigBee, LTE-U) |  Denoising Autoencoders (DAE) |  OTA, synthetic (GAN augmented data) |  $8.7k$ |  Symbol error rate (SER), signal-to-noise ratio (SNR), interference power (dBm)
[92] |  FMCW radars |  Autoencoder-based convolutional neural network |  OTA |  $8.78k$ |  Error vector magnitude (EVM), signal-to-noise-plus interference ratio (SINR), absolute & phase structural similarity index (SSIM)
[98] |  FMCW/CS for automotive radar applications |  Autoencoder |  Simulated training data; real OTA data for validation data |  $2k$ |  SINR
[99] |  Wireless sensor networks |  Linear regression (LR) without regularization |  Simulation |  Unknown |  Classifier score; packet receive ratio (PRR); battery power savings

###  VI-D Performance Metrics

Performance metrics used include bit error rate (BER), symbol error rate
(SER), SNR, SINR, EVM, signal-to-remaining-interference-noise ratio (SRINR),
interference power, packet receive ratio (PRR), F1 score, PPMSE, mean square
prediction error (MSPE) amongst others. Xu et. al. [75] utilized a plot of the
BER with optical SNR (OSNR) to evaluate their interference cancellation
technique. A comparison was made between the conventional hard decision
technique and a variation of deep neural networks algorithms. For BER, a lot
of data would need to be processed before having a single error since
practical BERs must be very low ($10^{-1}$ to $10^{-9}$) and $10$-$100$ errors
are required for confidence. It is often used in computer simulations to
evaluate the performance of digital communication systems [169]. Similar to
BER, SER is usually plotted against some measure of signal-to-noise ratio
(SNR). SER is the ratio of the number of symbols in error to the total number
of symbols transmitted. While SNR is a metric that is used to parameterize the
performance of a signal processing system that uses a Gaussian distributed
noise [170]. This is a very intuitive way to measure the performance of a
digital communication system. For instance, Lin et. al. [91] plotted the SER
with respect to interference power in order to compare the performance of
their denoising autoencoder. In understanding how deep learning can
effectively suppress an interfering signal, [91] compared corrupted Wi-Fi
signal before suppression and the signal after suppression by looking at a
plot of the SNR versus the interference power. The resultant signal post
suppression can be compared to a clean signal (interference-free) and the
neural network output SNR can be very useful in understanding the impact of
such a suppression approach. SINR defines the ratio of the power of a signal
of interest to the sum of the interference power and the power of the
background noise [171]. For radar systems application, the ratio of the power
of a single object reflection to the average remaining interference power
after cancellation is referred to as signal to remaining interference plus
noise ratio (SRINR) [68]. Mun et. al. [86] utilized this metric to compare the
performance of the RNN-based automotive interference suppression technique.

We provide some details on other common performance metrics used in deep
learning literature which are not typically used as wireless communication
metrics, yet may provide utility for evaluating interference suppression.

####  VI-D1 Area under the receiver operating characteristics curve (AUC)

The AUC metric is typically used to ascertain detection or classification
performance for a multi-class classification problem at various threshold
settings. The receiver operating curve (ROC) is a probability curve while the
area under the curve (AUC) defines the degree or measure of separability
[172]. In essence, it describes how well a model can distinguish between
classes. It is a plot of the true positive rate (TPR) against the false
positive rate (FPR). The higher the AUC, the better the model is at
distinguishing between classes. Ristea et. al. [71] used this metric to
compare the performance of their suppression technique with other conventional
suppression techniques. In their specific application, the closer the value
was to $1$ the better. When compared to non-deep learning technique such as
zeroing, their deep fully convolutional neural network performed better for
both the validation and test sets.

####  VI-D2 Mean Absolute Error (MAE)

MAE is another simple performance metric that measures the error between the
values of an actual quantity and the predicted quantity. For instance, [71]
computed the MAE in dB between the range profile amplitude of targets computed
from label signals and the amplitude of target from predicted signals. Similar
to SNR and AUC, [71] used MAE to compare between their technique and other
suppression techniques. Hence, this metric can be applied at the application
level.

####  VI-D3 Error Vector Magnitude (EVM)

The EVM is the root mean square (RMS) average amplitude of the error vector (a
vector in the I/Q plane between the ideal constellation point and the point
received by the receiver [173]) that is normalized to the ideal signal
amplitude reference [174]. Fuchs et. al. [92] used the EVM to compare the
detection in the original image with the predicted image. Rock et. al. [72]
also used the EVM to compare the performance of their scheme with the state-
of-the-art. This metric is thus a metric that is applicable to the I/Q data.

####  VI-D4 Structural Similarity (SSIM) Index

The SSIM index is typically used for qualitative visual assessment. Research
has shown that they perform better than mean squared error (MSE) [175].
According to [92], it compares image characteristics such as luminance,
contrast and structure which are useful when removing interference since
images may preserve target peaks resulting from interference. Hence, Fuchs et.
al. [92] utilized this technique to compare the spectrum free of interference
with the reconstructed spectrum after interference removal. Hence, if the
output can be viewed as a two dimensional array of I/Q or application output,
it is applicable.

###  VI-E Benefits and Drawbacks of Deep Learning-based Interference
suppression Schemes

In this section, we compare the benefits and the drawbacks of the deep
learning-based interference suppression approaches. This is crucial because
despite the gains brought about by applying deep learning to interference
suppression, there are some drawbacks. This is also important as it helps the
reader to understand what the DL algorithm solves as well as gaining a better
understanding of which algorithm may fit their need. Details of the benefits
and drawbacks of each of the DL-based suppression techniques can be found in
Table IV

##  VII Open Challenges

Despite the successes with using deep learning algorithms in suppressing
interference, there are still some challenges that need to be overcome before
they can be adopted in the production environment. We note that some of these
challenges also hold true for other deep learning-based wireless communication
tasks.

###  VII-A Lack of Interpretability of Deep Learning Algorithms

One of the major challenges with using deep learning to classify interference
is their lack of interpretability. Interpretability is a concept that deals
with the model transparency and its ability to explain the intricacies of the
model after the fact [51]. As expected, this is very important in suppressing
interference sources since this can be useful in subsequently identifying
spectrum offenders or devices misusing the spectrum in an opportunistic
spectrum access paradigm such as can be found in the industrial, scientific,
and medical (ISM) radio bands. This implies the need for interpretable
artificial intelligence (AI). If the deep learning engine is viewed as a black
box, it will be difficult for end users to trust the system. As researchers
make progress in applying deep learning to interference suppression, privacy
concerns may start to grow as we have seen in other deep learning applications
[176, 177].

###  VII-B The Stochasticity of the Wireless Channel

Another issue with using deep learning in interference suppression is the
issue of the randomness of the wireless channels. Most of the papers reviewed
in this survey have used data trained in only one type of environment (e.g.
an-echoic, AWGN, etc). Meanwhile, in practical deployments, test signals go
through a variation in channel environments which can result in the
incapability of the trained model to adapt to new test cases. Since, there are
numerous channel environments at any given time, it is important to be able to
interpret deep learning models so that they can better learn different RF
environments. In other words, any difference between training and testing
environment could impact the performance of deep learning algorithm.
Developing effective GAN approaches for training data creation could be very
valuable. Given that deep learning algorithms require huge amount of data to
train them, the nexus between the quality and quantity of data results in
another challenge that impairs the use of deep learning in interference
suppression. Training data collection is very laborious and can be more
difficult when a supervised learning algorithm that requires labeling the data
is considered.

Often times, researchers use data augmentation techniques to further increase
the quantity of their data. For instance, in the image processing domain, one
way to improve the quantity of training data is to augment the data using both
simple and complex techniques. Simple transformation techniques include
flipping, cropping or rotating the images to increase the quantity of such
data. Since these techniques are not transferable to I/Q data used in
interference suppression, complex techniques that leverage popular generative
models such as generative adversarial networks (GAN) and variational
autoencoders (VAE) have been proposed to augment the input data image in such
a way as to generate new data that belong to the same distribution as the
training data but not exactly in the training data. In most data augmentation
for wireless communications tasks (including interference suppression, emitter
and device classifications), such augmentation techniques have had very little
success since the algorithm often times learn noise and other unwanted
artefacts in the signal. In addition, the stochasticity of the RF environment
makes it difficult to augment datasets for wireless communication
applications.

TABLE IV: Benefits and Drawbacks of DL-Based Interference Suppression Approaches DL Algorithm |  References |  Technical Approach |  Benefits |  Drawbacks
---|---|---|---|---
Convolutional neural network (CNN) |  [71, 72, 73, 74] |  • Uncorrelated interference is learnt via FCN [71]. • STFT can be used to convert time domain signal into a spectrogram [71]. • CNN is used to denoising and thus suppress interference using both interfered data and clean data. • SIR & SNR can be used to scale interference and noise powers in relation to the object signal power[72]. |  • The CNN algorithms both outperformed the conventional suppression method such as zeroing, ramp filtering and IMAT techniques [72]. • Very useful for learning when the amount of data is relatively smaller. • Similar to Autoencoders, CNN can be used for denoising [72]. |  • Could result in very high EVM performance when compared to zeroing, ramp filtering and and IMAT). • High EVM could also result in distortions for radar applications [72]. • Usually require high memory to train large datasets.
Deep neural network (DNN) |  [75, 76, 77, 78, 79, 80, 81, 82, 83, 84] |  • DNNs can be useful in cancelling interference for NoMA systems [75, 167]. • MLPs can be combined with filters to suppress interference [77]. • NoMA algorithms such as the SEFDM waveform utilize non-orthogonal sub-carriers which can result in ACI. [167]. • Performance depends on waveform characteristics • They are usually structurally simpler when compared to other deep learning techniques. |  • Useful in mitigating non-linear interference and reducing complexity [76] • DNNs can suppress inter carrier interference (ICI) within certain signal waveform e.g. SEFDM used in [75]. • Capable of learning a huge number of parameters in suppressing SI. • They can be used to reduce computation complexity for non-linear interference suppression. • Gains in SNR when compared to techniques that utilize hard decision detectors. |  • High computational complexity • Simulation-based studies that utilize DNNs are very susceptible to impractical assumptions. • Could result in smaller MSE compared to conventional techniques [79]. • Varying complexity (such as number of hidden layers, number of parameters, etc) in the DNN model could result in significant performance degradation.
Recurrent neural network (RNN) |  [86, 87, 88, 89, 90] |  • The dynamics of sequences can be captured with RNNs using a network of nodes [162]. • Interference can be suppressed in time domain for radar systems. • Transmit signal is reconstructed in the presence of various interference signals. • They are typically used when input data is temporally correlated (e.g. time domain signals). |  • Low processing time • Useful for applications where frequency changes linearly. • RNNs with attention blocks can capture sequential relationship between time steps. • Useful in reconstructing original signal. • RNNs can take in arbitrary input or output sequences. |  • RNNs are vulnerable to exploding gradient problem. • Most of the implementations have only considered simulation data for interference mitigation. • For some applications, RNN-based methods have slow convergence rate and thus unable to track fast time varying interference [85].
Autoencoders (Denoising AE and CNN-based AE) |  [91, 92, 93, 94, 95, 96, 97, 98] |  • CNN-based AE is used to de-noise interfered range-Doppler (RD) images [92]. • Autoencoder is used to learn the distribution of the interference component so that it can be suppressed. • Signal is reconstructed without any knowledge about symbol structure and channel response. • AE is combined with an interference detection filter to suppress automotive radar interference in [98] |  • Using real-world data measurement further improves the performance of AE methods. • DAEs perform well when the interfering power is larger than the amplitude of the desired signal. • Typically, AEs do not not incur any signalling overhead since there’s no synchronization between the transmitter, receiver and interferer. • Signal can be reconstructed without any knowledge about symbol structure and channel response. |  • AE techniques often perform poorly at low SNR • AEs perform poorly with insufficient data • They also incur a huge processing time, hyper-parameter tuning and there is also a need to validate the model.

###  VII-C Performance Analysis

As we have shown in this survey, many of the researchers utilized different
metrics to show the performance of their algorithms. Also, the majority of the
DL-based interference suppression techniques utilized I/Q data as input into
the algorithm. Compared to rigorous and well established mathematical
foundation of signal processing methods developed by earlier pioneers of the
wireless communications field (such as Claude Shannon’s information theory
mathematical derivations, amongst others), the lack of a robust mathematical
underpinning for analyzing results from deep learning algorithms may hinder
their practical adoption for interference suppression. On the other hand, it
has been shown that training and testing data do not usually belong to the
same distribution [178], hence, information-theoretic techniques may still be
explored to help with performance metrics that would be suitable for
interference suppression research. Furthermore, metrics such as BER, while
ideal, might be slow in evaluating performance since extreme amounts of data
would need to be processed for one error.

###  VII-D Issues with Open Set Recognition (OSR) or Out-of-Distribution (OOD)
Data

Open set recognition (OSR) is a common phenomenon in which unknown classes not
present in the training phase during learning are found in the testing phase
[179, 180]. Also known as out-of-distribution (OOD) data [178], this issue can
result in a lack of trust for deep learning models since the test set in real
world applications is infinite, and even more so in wireless communications.
For example, we discussed earlier how the random nature of the wireless
channel can result in a drastic degradation in the performance of a deep
learning classifier and also the fact that the number of possible channels is
infinite. Typically, in deep learning-based algorithms, researchers make the
assumption that the training and testing data are sampled from the same
distribution. As a result of this assumption, the ability of the model to
generalize well to unseen testing data is often accounted for. However, when
the deep learning algorithm is deployed in real-world applications, testing
data is not restricted to the same distribution as the training data. Hence,
it is feasible and probable for the testing data to differ significantly from
the training data.

Some prior works [181, 182] have shown that DL models usually make high
confidence predictions for inputs that are either irrelevant or cannot be
recognized. Although, this phenomenon was briefly mentioned in [180], we
believe that it has not received enough attention in the DL-based interference
suppression research. For instance, in safety- and mission-critical
applications, such as autonomous driving, OSR or OOD data can lead to
perception errors and cause accidents or system failures. Further, the ability
to detect and consequently suppress interference using these deep learning
techniques could be negatively impacted by OOD or OSR. Another reason why this
issue may be consequential is that because they do not result in explicit
model errors, they may be undetected. Thus, we believe that in order for deep
learning to be safely deployed in interference suppression research, there is
an important need to distinguish between the known and unknown classes in the
testing data.

###  VII-E Challenges with Implementation

Training a deep learning algorithm is usually time-consuming and computation-
intensive, thus most researchers train the algorithms offline and then deploy
a trained algorithm in their application. For instance, Grimaldi et al. [152]
observed that an online classifier, distinguishing between Bluetooth (also
known as IEEE 802.15.1) and Bluetooth low energy (BLE)131313Bluetooth is a
terrestrial, adhoc, short range communication protocol [183]. While BLE was
designed for a reduced power consumption and cost with a similar communication
range as Bluetooth[184], it is important to note that Bluetooth and BLE are
independent of each other. technologies is ten times more likely to mis-
classify when compared to offline methods which only mis-classified $2\%$ of
the time. This occurred because the two technologies have very similar PHY
layer with both of them utilizing the Gaussian frequency-shift keying
(GFSK)141414GFSK filters the data pulses with a Gaussian filter instead of
changing the frequency at the beginning of each symbol period so as to make
the transitions smooth. GFSK is used for low power design requirements such as
BLE [185]. modulation. Furthermore, Schmidt et al. [101] ran their multi-
layered CNN algorithm using expensive graphical processing units (GPU) servers
which were used to train the algorithm, this expensive hardware could pose a
barrier to the adoption of deep learning to practical interference
suppression. While offline training works well in the computer vision research
where noise and other factors do not disproportionately impact performance,
the same could not be said for interference suppression application. Since the
interference characteristics of the received signal used to train an offline
algorithm changes with time, researchers may find that their trained algorithm
may not be suitable in different scenarios or channel environment or even at
another time period. This would imply that retraining needs to be done during
deployment. This retraining may happen more frequently than is done in other
applications. For computation-constrained applications, this would quickly
result in a bottleneck. One possible solution to this is to implement the deep
learning algorithm directly on the deployed hardware so that training time can
be more manageable in real-time or near-real-time systems. Again, for
resource-constrained IoT devices, this may be infeasible.

##  VIII Conclusion

In this survey paper, we have identified key characteristics of interference
in several wireless communication applications. We have reviewed deep learning
techniques that have been utilized by researchers to suppress interference. We
also provided a comparison of such techniques for interference suppression.
Our findings show that deep learning techniques have been shown to
significantly improve interference suppression when compared to traditional
suppression techniques. Also, deep learning techniques can learn complex non-
linear relationship in the input data, thereby making them useful in learning
complex non-linear decision boundaries. We also noted that deep learning
techniques require access to a large dataset so that the algorithm can be
sufficiently trained thereby improving test performance. It is important to
emphasize that the application of deep learning to interference suppression
will continue to grow. In conducting this survey, the authors found that deep
learning techniques such as autoencoders and CNN are of particular interest,
given their unique characteristics that can be leveraged in interference
suppression. While there are a lot of merits to using deep learning to
suppress interference, we have also found some challenges that need to be
addressed to ensure success. One key challenge with using deep learning is the
availability of benchmark dataset, the CRAWDAD database is a potent tool that
can serve as a reservoir of key datasets that can help in interference
suppression. Lastly, we note that there is still a need to further investigate
concepts such as explainable artificial intelligence (AI) so we can better
understand the inner workings of deep learning models.

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