3,307 research outputs found
Underwater Optical Wireless Communications, Networking, and Localization: A Survey
Underwater wireless communications can be carried out through acoustic, radio
frequency (RF), and optical waves. Compared to its bandwidth limited acoustic
and RF counterparts, underwater optical wireless communications (UOWCs) can
support higher data rates at low latency levels. However, severe aquatic
channel conditions (e.g., absorption, scattering, turbulence, etc.) pose great
challenges for UOWCs and significantly reduce the attainable communication
ranges, which necessitates efficient networking and localization solutions.
Therefore, we provide a comprehensive survey on the challenges, advances, and
prospects of underwater optical wireless networks (UOWNs) from a layer by layer
perspective which includes: 1) Potential network architectures; 2) Physical
layer issues including propagation characteristics, channel modeling, and
modulation techniques 3) Data link layer problems covering link configurations,
link budgets, performance metrics, and multiple access schemes; 4) Network
layer topics containing relaying techniques and potential routing algorithms;
5) Transport layer subjects such as connectivity, reliability, flow and
congestion control; 6) Application layer goals and state-of-the-art UOWN
applications, and 7) Localization and its impacts on UOWN layers. Finally, we
outline the open research challenges and point out the future directions for
underwater optical wireless communications, networking, and localization
research.Comment: This manuscript is submitted to IEEE Communication Surveys and
Tutorials for possible publicatio
Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey
The Internet of Things (IoT) is expected to require more effective and
efficient wireless communications than ever before. For this reason, techniques
such as spectrum sharing, dynamic spectrum access, extraction of signal
intelligence and optimized routing will soon become essential components of the
IoT wireless communication paradigm. Given that the majority of the IoT will be
composed of tiny, mobile, and energy-constrained devices, traditional
techniques based on a priori network optimization may not be suitable, since
(i) an accurate model of the environment may not be readily available in
practical scenarios; (ii) the computational requirements of traditional
optimization techniques may prove unbearable for IoT devices. To address the
above challenges, much research has been devoted to exploring the use of
machine learning to address problems in the IoT wireless communications domain.
This work provides a comprehensive survey of the state of the art in the
application of machine learning techniques to address key problems in IoT
wireless communications with an emphasis on its ad hoc networking aspect.
First, we present extensive background notions of machine learning techniques.
Then, by adopting a bottom-up approach, we examine existing work on machine
learning for the IoT at the physical, data-link and network layer of the
protocol stack. Thereafter, we discuss directions taken by the community
towards hardware implementation to ensure the feasibility of these techniques.
Additionally, before concluding, we also provide a brief discussion of the
application of machine learning in IoT beyond wireless communication. Finally,
each of these discussions is accompanied by a detailed analysis of the related
open problems and challenges.Comment: Ad Hoc Networks Journa
Configuration Learning in Underwater Optical Links
A new research problem named configuration learning is described in this
work. A novel algorithm is proposed to address the configuration learning
problem. The configuration learning problem is defined to be the optimization
of the Machine Learning (ML) classifier to maximize the ML performance metric
optimizing the transmitter configuration in the signal processing/communication
systems. Specifically, this configuration learning problem is investigated in
an underwater optical communication system with signal processing performance
metric of the physical-layer communication throughput. A novel algorithm is
proposed to perform the configuration learning by alternating optimization of
key design parameters and switching between several Recurrent Neural Network
(RNN) classifiers dependant on the learning objective. The proposed ML
algorithm is validated with the datasets of an underwater optical communication
system and is compared with competing ML algorithms. Performance results
indicate that the proposal outperforms the competing algorithms for binary and
multi-class configuration learning in underwater optical communication
datasets. The proposed configuration learning framework can be further
investigated and applied to a broad range of topics in signal processing and
communications
A Comparative Survey of Optical Wireless Technologies: Architectures and Applications
New high-data-rate multimedia services and applications are evolving
continuously and exponentially increasing the demand for wireless capacity of
fifth-generation (5G) and beyond. The existing radio frequency (RF)
communication spectrum is insufficient to meet the demands of future
high-datarate 5G services. Optical wireless communication (OWC), which uses an
ultra-wide range of unregulated spectrum, has emerged as a promising solution
to overcome the RF spectrum crisis. It has attracted growing research interest
worldwide in the last decade for indoor and outdoor applications. OWC offloads
huge data traffic applications from RF networks. A 100 Gb/s data rate has
already been demonstrated through OWC. It offers services indoors as well as
outdoors, and communication distances range from several nm to more than 10000
km. This paper provides a technology overview and a review on optical wireless
technologies, such as visible light communication, light fidelity, optical
camera communication, free space optical communication, and light detection and
ranging. We survey the key technologies for understanding OWC and present
state-of-the-art criteria in aspects, such as classification, spectrum use,
architecture, and applications. The key contribution of this paper is to
clarify the differences among different promising optical wireless technologies
and between these technologies and their corresponding similar existing RF
technologie
Identification of Smart Jammers: Learning based Approaches Using Wavelet Representation
Smart jammer nodes can disrupt communication between a transmitter and a
receiver in a wireless network, and they leave traces that are undetectable to
classical jammer identification techniques, hidden in the time-frequency plane.
These traces cannot be effectively identified through the use of the classical
Fourier transform based time-frequency transformation (TFT) techniques with a
fixed resolution. Inspired by the adaptive resolution property provided by the
wavelet transforms, in this paper, we propose a jammer identification
methodology that includes a pre-processing step to obtain a multi-resolution
image, followed by the use of a classifier. Support vector machine (SVM) and
deep convolutional neural network (DCNN) architectures are investigated as
classifiers to automatically extract the features of the transformed signals
and to classify them. Three different jamming attacks are considered, the
barrage jamming that targets the complete transmission bandwidth, the
synchronization signal jamming attack that targets synchronization signals and
the reference signal jamming attack that targets the reference signals in an
LTE downlink transmission scenario. The performance of the proposed approach is
compared with the classical Fourier transform based TFT techniques,
demonstrating the efficacy of the proposed approach in the presence of smart
jammers
CNN-Based Signal Detection for Banded Linear Systems
Banded linear systems arise in many communication scenarios, e.g., those
involving inter-carrier interference and inter-symbol interference. Motivated
by recent advances in deep learning, we propose to design a high-accuracy
low-complexity signal detector for banded linear systems based on convolutional
neural networks (CNNs). We develop a novel CNN-based detector by utilizing the
banded structure of the channel matrix. Specifically, the proposed CNN-based
detector consists of three modules: the input preprocessing module, the CNN
module, and the output postprocessing module. With such an architecture, the
proposed CNN-based detector is adaptive to different system sizes, and can
overcome the curse of dimensionality, which is a ubiquitous challenge in deep
learning. Through extensive numerical experiments, we demonstrate that the
proposed CNN-based detector outperforms conventional deep neural networks and
existing model-based detectors in both accuracy and computational time.
Moreover, we show that CNN is flexible for systems with large sizes or wide
bands. We also show that the proposed CNN-based detector can be easily extended
to near-banded systems such as doubly selective orthogonal frequency division
multiplexing (OFDM) systems and 2-D magnetic recording (TDMR) systems, in which
the channel matrices do not have a strictly banded structure
An Electrocommunication System Using FSK Modulation and Deep Learning Based Demodulation for Underwater Robots
Underwater communication is extremely challenging for small underwater robots
which typically have stringent power and size constraints. In our previous
work, we developed an artificial electrocommunication system which could be an
alternative for the communication of small underwater robots. This paper
further presents a new electrocommunication system that utilizes Binary
Frequency Shift Keying (2FSK) modulation and deep-learning-based demodulation
for underwater robots. We first derive an underwater electrocommunication model
that covers both the near-field area and a large transition area outside of the
near-field area. 2FSK modulation is adopted to improve the anti-interference
ability of the electric signal. A deep learning algorithm is used to demodulate
the electric signal by the receiver. Simulations and experiments show that with
the same testing condition, the new communication system outperforms the
previous system in both the communication distance and the data transmitting
rate. In specific, the newly developed communication system achieves stable
communication within the distance of 10 m at a data transfer rate of 5 Kbps
with a power consumption of less than 0.1 W. The substantial increase in
communication distance further improves the possibility of electrocommunication
in underwater robotics.Comment: IROS202
Detection Algorithms for Communication Systems Using Deep Learning
The design and analysis of communication systems typically rely on the
development of mathematical models that describe the underlying communication
channel, which dictates the relationship between the transmitted and the
received signals. However, in some systems, such as molecular communication
systems where chemical signals are used for transfer of information, it is not
possible to accurately model this relationship. In these scenarios, because of
the lack of mathematical channel models, a completely new approach to design
and analysis is required. In this work, we focus on one important aspect of
communication systems, the detection algorithms, and demonstrate that by
borrowing tools from deep learning, it is possible to train detectors that
perform well, without any knowledge of the underlying channel models. We
evaluate these algorithms using experimental data that is collected by a
chemical communication platform, where the channel model is unknown and
difficult to model analytically. We show that deep learning algorithms perform
significantly better than a simple detector that was used in previous works,
which also did not assume any knowledge of the channel
Doppler Invariant Demodulation for Shallow Water Acoustic Communications Using Deep Belief Networks
Shallow water environments create a challenging channel for communications.
In this paper, we focus on the challenges posed by the frequency-selective
signal distortion called the Doppler effect. We explore the design and
performance of machine learning (ML) based demodulation methods --- (1) Deep
Belief Network-feed forward Neural Network (DBN-NN) and (2) Deep Belief
Network-Convolutional Neural Network (DBN-CNN) in the physical layer of Shallow
Water Acoustic Communication (SWAC). The proposed method comprises of a ML
based feature extraction method and classification technique. First, the
feature extraction converts the received signals to feature images. Next, the
classification model correlates the images to a corresponding binary
representative. An analysis of the ML based proposed demodulation shows that
despite the presence of instantaneous frequencies, the performance of the
algorithm shows an invariance with a small 2dB error margin in terms of bit
error rate (BER)
A Novel Method for Classification and Modelling of Underwater Acoustic Communication through Machine Learning and Image Processing Technique
The increasing prevalence of underwater activities has highlighted the urgent need for reliable underwater acoustic communication systems. However, the challenging nature of the underwater environment poses significant obstacles to the implementation of conventional voice communication methods. To better understand and improve upon these systems, simulations of the underwater audio channel have been developed using mathematical models and assumptions. In this study, we utilize real-world information gathered from both a measured water reservoir and Lake to evaluate the ability of machine learning and machine learning methods, specifically Long Short-Term Memory (LSTM) and Deep Neural Network (DNN), to accurately reconstruct the underwater audio channel. The outcomes validate the efficiency of machine learning methods, particularly LSTM, in accurately simulating the underwater acoustic communication channel with low mean absolute percentage error. Additionally, this research also includes an image processing to identify the objects present the in the acoustic environmen
- …