1,407 research outputs found
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
Antenna Array Enabled Space/Air/Ground Communications and Networking for 6G
Antenna arrays have a long history of more than 100 years and have evolved
closely with the development of electronic and information technologies,
playing an indispensable role in wireless communications and radar. With the
rapid development of electronic and information technologies, the demand for
all-time, all-domain, and full-space network services has exploded, and new
communication requirements have been put forward on various space/air/ground
platforms. To meet the ever increasing requirements of the future sixth
generation (6G) wireless communications, such as high capacity, wide coverage,
low latency, and strong robustness, it is promising to employ different types
of antenna arrays with various beamforming technologies in space/air/ground
communication networks, bringing in advantages such as considerable antenna
gains, multiplexing gains, and diversity gains. However, enabling antenna array
for space/air/ground communication networks poses specific, distinctive and
tricky challenges, which has aroused extensive research attention. This paper
aims to overview the field of antenna array enabled space/air/ground
communications and networking. The technical potentials and challenges of
antenna array enabled space/air/ground communications and networking are
presented first. Subsequently, the antenna array structures and designs are
discussed. We then discuss various emerging technologies facilitated by antenna
arrays to meet the new communication requirements of space/air/ground
communication systems. Enabled by these emerging technologies, the distinct
characteristics, challenges, and solutions for space communications, airborne
communications, and ground communications are reviewed. Finally, we present
promising directions for future research in antenna array enabled
space/air/ground communications and networking
Self-Evolving Integrated Vertical Heterogeneous Networks
6G and beyond networks tend towards fully intelligent and adaptive design in
order to provide better operational agility in maintaining universal wireless
access and supporting a wide range of services and use cases while dealing with
network complexity efficiently. Such enhanced network agility will require
developing a self-evolving capability in designing both the network
architecture and resource management to intelligently utilize resources, reduce
operational costs, and achieve the coveted quality of service (QoS). To enable
this capability, the necessity of considering an integrated vertical
heterogeneous network (VHetNet) architecture appears to be inevitable due to
its high inherent agility. Moreover, employing an intelligent framework is
another crucial requirement for self-evolving networks to deal with real-time
network optimization problems. Hence, in this work, to provide a better insight
on network architecture design in support of self-evolving networks, we
highlight the merits of integrated VHetNet architecture while proposing an
intelligent framework for self-evolving integrated vertical heterogeneous
networks (SEI-VHetNets). The impact of the challenges associated with
SEI-VHetNet architecture, on network management is also studied considering a
generalized network model. Furthermore, the current literature on network
management of integrated VHetNets along with the recent advancements in
artificial intelligence (AI)/machine learning (ML) solutions are discussed.
Accordingly, the core challenges of integrating AI/ML in SEI-VHetNets are
identified. Finally, the potential future research directions for advancing the
autonomous and self-evolving capabilities of SEI-VHetNets are discussed.Comment: 25 pages, 5 figures, 2 table
Mobile and Wireless Communications
Mobile and Wireless Communications have been one of the major revolutions of the late twentieth century. We are witnessing a very fast growth in these technologies where mobile and wireless communications have become so ubiquitous in our society and indispensable for our daily lives. The relentless demand for higher data rates with better quality of services to comply with state-of-the art applications has revolutionized the wireless communication field and led to the emergence of new technologies such as Bluetooth, WiFi, Wimax, Ultra wideband, OFDMA. Moreover, the market tendency confirms that this revolution is not ready to stop in the foreseen future. Mobile and wireless communications applications cover diverse areas including entertainment, industrialist, biomedical, medicine, safety and security, and others, which definitely are improving our daily life. Wireless communication network is a multidisciplinary field addressing different aspects raging from theoretical analysis, system architecture design, and hardware and software implementations. While different new applications are requiring higher data rates and better quality of service and prolonging the mobile battery life, new development and advanced research studies and systems and circuits designs are necessary to keep pace with the market requirements. This book covers the most advanced research and development topics in mobile and wireless communication networks. It is divided into two parts with a total of thirty-four stand-alone chapters covering various areas of wireless communications of special topics including: physical layer and network layer, access methods and scheduling, techniques and technologies, antenna and amplifier design, integrated circuit design, applications and systems. These chapters present advanced novel and cutting-edge results and development related to wireless communication offering the readers the opportunity to enrich their knowledge in specific topics as well as to explore the whole field of rapidly emerging mobile and wireless networks. We hope that this book will be useful for students, researchers and practitioners in their research studies
Adaptive Beamforming for Target Detection and Surveillance Based on Distributed Unmanned Aerial Vehicle Platforms
OAPA A distributed sensor array network for target detection and surveillance is studied with sub-arrays placed on unmanned aerial vehicle (UAV) platforms, where arbitrary locations and rotation angles are allocated to each UAV-based sub-array in the predefined Cartesian coordinate system. In this model, one transmitter sends out a single signal and it is then reflected back from the targets and received by the distributed sensor array system. A joint reference signal based beamformer (JRSB) is proposed for the static/slowly moving targets and UAV platforms where the Doppler effects can be ignored, leading to improved performance by exploiting the information collected by all the sub-arrays simultaneously. Then, the developed beamformer is extended to the dynamic case considering the Doppler effects, referred to as the frequency extended joint reference signal based beamformer (FE-JRSB), achieving the potential maximum output signal to interference plus noise ratio (SINR) by exploiting the information across the potential frequencies of interest jointly. The output signal of the beamformer with increased SINR can be used to assist the extended target detection in the following processing. Simulation results show that both are able to extract the signals of interest while suppressing interfering signals, and a lower mean square error and higher output SINR are achieved compared with a regular reference signal based beamformer using a single sub-array. One unique feature of the provided solutions is that, although the signals involved are narrowband, the employed beamforming structure has to be wideband for it to be effective
A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future
A High Altitude Platform Station (HAPS) is a network node that operates in
the stratosphere at an of altitude around 20 km and is instrumental for
providing communication services. Precipitated by technological innovations in
the areas of autonomous avionics, array antennas, solar panel efficiency
levels, and battery energy densities, and fueled by flourishing industry
ecosystems, the HAPS has emerged as an indispensable component of
next-generations of wireless networks. In this article, we provide a vision and
framework for the HAPS networks of the future supported by a comprehensive and
state-of-the-art literature review. We highlight the unrealized potential of
HAPS systems and elaborate on their unique ability to serve metropolitan areas.
The latest advancements and promising technologies in the HAPS energy and
payload systems are discussed. The integration of the emerging Reconfigurable
Smart Surface (RSS) technology in the communications payload of HAPS systems
for providing a cost-effective deployment is proposed. A detailed overview of
the radio resource management in HAPS systems is presented along with
synergistic physical layer techniques, including Faster-Than-Nyquist (FTN)
signaling. Numerous aspects of handoff management in HAPS systems are
described. The notable contributions of Artificial Intelligence (AI) in HAPS,
including machine learning in the design, topology management, handoff, and
resource allocation aspects are emphasized. The extensive overview of the
literature we provide is crucial for substantiating our vision that depicts the
expected deployment opportunities and challenges in the next 10 years
(next-generation networks), as well as in the subsequent 10 years
(next-next-generation networks).Comment: To appear in IEEE Communications Surveys & Tutorial
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