6,011 research outputs found
Sparse Signal Processing Concepts for Efficient 5G System Design
As it becomes increasingly apparent that 4G will not be able to meet the
emerging demands of future mobile communication systems, the question what
could make up a 5G system, what are the crucial challenges and what are the key
drivers is part of intensive, ongoing discussions. Partly due to the advent of
compressive sensing, methods that can optimally exploit sparsity in signals
have received tremendous attention in recent years. In this paper we will
describe a variety of scenarios in which signal sparsity arises naturally in 5G
wireless systems. Signal sparsity and the associated rich collection of tools
and algorithms will thus be a viable source for innovation in 5G wireless
system design. We will discribe applications of this sparse signal processing
paradigm in MIMO random access, cloud radio access networks, compressive
channel-source network coding, and embedded security. We will also emphasize
important open problem that may arise in 5G system design, for which sparsity
will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
The Convergence of Machine Learning and Communications
The areas of machine learning and communication technology are converging.
Today's communications systems generate a huge amount of traffic data, which
can help to significantly enhance the design and management of networks and
communication components when combined with advanced machine learning methods.
Furthermore, recently developed end-to-end training procedures offer new ways
to jointly optimize the components of a communication system. Also in many
emerging application fields of communication technology, e.g., smart cities or
internet of things, machine learning methods are of central importance. This
paper gives an overview over the use of machine learning in different areas of
communications and discusses two exemplar applications in wireless networking.
Furthermore, it identifies promising future research topics and discusses their
potential impact.Comment: 8 pages, 4 figure
Towards 6G Networks: Use Cases and Technologies
Reliable data connectivity is vital for the ever increasingly intelligent,
automated and ubiquitous digital world. Mobile networks are the data highways
and, in a fully connected, intelligent digital world, will need to connect
everything, from people to vehicles, sensors, data, cloud resources and even
robotic agents. Fifth generation (5G) wireless networks (that are being
currently deployed) offer significant advances beyond LTE, but may be unable to
meet the full connectivity demands of the future digital society. Therefore,
this article discusses technologies that will evolve wireless networks towards
a sixth generation (6G), and that we consider as enablers for several potential
6G use cases. We provide a full-stack, system-level perspective on 6G scenarios
and requirements, and select 6G technologies that can satisfy them either by
improving the 5G design, or by introducing completely new communication
paradigms.Comment: The paper has been accepted for publication at the IEEE
Communications Magazine, 202
Diffusion-KLMS Algorithm and its Performance Analysis for Non-Linear Distributed Networks
In a distributed network environment, the diffusion-least mean squares (LMS)
algorithm gives faster convergence than the original LMS algorithm. It has also
been observed that, the diffusion-LMS generally outperforms other distributed
LMS algorithms like spatial LMS and incremental LMS. However, both the original
LMS and diffusion-LMS are not applicable in non-linear environments where data
may not be linearly separable. A variant of LMS called kernel-LMS (KLMS) has
been proposed in the literature for such non-linearities. In this paper, we
propose kernelised version of diffusion-LMS for non-linear distributed
environments. Simulations show that the proposed approach has superior
convergence as compared to algorithms of the same genre. We also introduce a
technique to predict the transient and steady-state behaviour of the proposed
algorithm. The techniques proposed in this work (or algorithms of same genre)
can be easily extended to distributed parameter estimation applications like
cooperative spectrum sensing and massive multiple input multiple output (MIMO)
receiver design which are potential components for 5G communication systems
Artificial Intelligence-Defined 5G Radio Access Networks
Massive multiple-input multiple-output antenna systems, millimeter wave
communications, and ultra-dense networks have been widely perceived as the
three key enablers that facilitate the development and deployment of 5G
systems. This article discusses the intelligent agent in 5G base station which
combines sensing, learning, understanding and optimizing to facilitate these
enablers. We present a flexible, rapidly deployable, and cross-layer artificial
intelligence (AI)-based framework to enable the imminent and future demands on
5G and beyond infrastructure. We present example AI-enabled 5G use cases that
accommodate important 5G-specific capabilities and discuss the value of AI for
enabling beyond 5G network evolution
Exploiting Map Topology Knowledge for Context-predictive Multi-interface Car-to-cloud Communication
While the automotive industry is currently facing a contest among different
communication technologies and paradigms about predominance in the connected
vehicles sector, the diversity of the various application requirements makes it
unlikely that a single technology will be able to fulfill all given demands.
Instead, the joint usage of multiple communication technologies seems to be a
promising candidate that allows benefiting from characteristical strengths
(e.g., using low latency direct communication for safety-related messaging).
Consequently, dynamic network interface selection has become a field of
scientific interest. In this paper, we present a cross-layer approach for
context-aware transmission of vehicular sensor data that exploits mobility
control knowledge for scheduling the transmission time with respect to the
anticipated channel conditions for the corresponding communication technology.
The proposed multi-interface transmission scheme is evaluated in a
comprehensive simulation study, where it is able to achieve significant
improvements in data rate and reliability
Machine Intelligence Techniques for Next-Generation Context-Aware Wireless Networks
The next generation wireless networks (i.e. 5G and beyond), which would be
extremely dynamic and complex due to the ultra-dense deployment of
heterogeneous networks (HetNets), poses many critical challenges for network
planning, operation, management and troubleshooting. At the same time,
generation and consumption of wireless data are becoming increasingly
distributed with ongoing paradigm shift from people-centric to machine-oriented
communications, making the operation of future wireless networks even more
complex. In mitigating the complexity of future network operation, new
approaches of intelligently utilizing distributed computational resources with
improved context-awareness becomes extremely important. In this regard, the
emerging fog (edge) computing architecture aiming to distribute computing,
storage, control, communication, and networking functions closer to end users,
have a great potential for enabling efficient operation of future wireless
networks. These promising architectures make the adoption of artificial
intelligence (AI) principles which incorporate learning, reasoning and
decision-making mechanism, as natural choices for designing a tightly
integrated network. Towards this end, this article provides a comprehensive
survey on the utilization of AI integrating machine learning, data analytics
and natural language processing (NLP) techniques for enhancing the efficiency
of wireless network operation. In particular, we provide comprehensive
discussion on the utilization of these techniques for efficient data
acquisition, knowledge discovery, network planning, operation and management of
the next generation wireless networks. A brief case study utilizing the AI
techniques for this network has also been provided.Comment: ITU Special Issue N.1 The impact of Artificial Intelligence (AI) on
communication networks and services, (To appear
Boosting Vehicle-to-cloud Communication by Machine Learning-enabled Context Prediction
The exploitation of vehicles as mobile sensors acts as a catalyst for novel
crowdsensing-based applications such as intelligent traffic control and
distributed weather forecast. However, the massive increases in Machine-type
Communication (MTC) highly stress the capacities of the network infrastructure.
With the system-immanent limitation of resources in cellular networks and the
resource competition between human cell users and MTC, more resource-efficient
channel access methods are required in order to improve the coexistence of the
different communicating entities. In this paper, we present a machine
learning-enabled transmission scheme for client-side opportunistic data
transmission. By considering the measured channel state as well as the
predicted future channel behavior, delay-tolerant MTC is performed with respect
to the anticipated resource-efficiency. The proposed mechanism is evaluated in
comprehensive field evaluations in public Long Term Evolution (LTE) networks,
where it is able to increase the mean data rate by 194% while simultaneously
reducing the average power consumption by up to 54%
Efficient Machine-type Communication using Multi-metric Context-awareness for Cars used as Mobile Sensors in Upcoming 5G Networks
Upcoming 5G-based communication networks will be confronted with huge
increases in the amount of transmitted sensor data related to massive
deployments of static and mobile Internet of Things (IoT) systems. Cars acting
as mobile sensors will become important data sources for cloud-based
applications like predictive maintenance and dynamic traffic forecast. Due to
the limitation of available communication resources, it is expected that the
grows in Machine-Type Communication (MTC) will cause severe interference with
Human-to-human (H2H) communication. Consequently, more efficient transmission
methods are highly required. In this paper, we present a probabilistic scheme
for efficient transmission of vehicular sensor data which leverages favorable
channel conditions and avoids transmissions when they are expected to be highly
resource-consuming. Multiple variants of the proposed scheme are evaluated in
comprehensive realworld experiments. Through machine learning based combination
of multiple context metrics, the proposed scheme is able to achieve up to 164%
higher average data rate values for sensor applications with soft deadline
requirements compared to regular periodic transmission.Comment: Best Student Paper Awar
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