979 research outputs found
Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning
The ability to intelligently utilize resources to meet the need of growing
diversity in services and user behavior marks the future of wireless
communication systems. Intelligent wireless communications aims at enabling the
system to perceive and assess the available resources, to autonomously learn to
adapt to the perceived wireless environment, and to reconfigure its operating
mode to maximize the utility of the available resources. The perception
capability and reconfigurability are the essential features of cognitive radio
while modern machine learning techniques project great potential in system
adaptation. In this paper, we discuss the development of the cognitive radio
technology and machine learning techniques and emphasize their roles in
improving spectrum and energy utility of wireless communication systems. We
describe the state-of-the-art of relevant techniques, covering spectrum sensing
and access approaches and powerful machine learning algorithms that enable
spectrum- and energy-efficient communications in dynamic wireless environments.
We also present practical applications of these techniques and identify further
research challenges in cognitive radio and machine learning as applied to the
existing and future wireless communication systems
Fair Scheduling Policies Exploiting Multiuser Diversity in Cellular Systems with Device-to-Device Communications
We consider the resource allocation problem in cellular networks which
support Device-to-Device Communications (D2D). For systems that enable D2D via
only orthogonal resource sharing, we propose and analyze two resource
allocation policies that guarantee access fairness among all users, while
taking advantage of multi-user diversity and local D2D communications, to
provide marked improvements over existing cellular-only policies. The first
policy, the Cellular Fairness Scheduling (CFS) Policy, provides the simplest
D2D extension to existing cellular systems, while the second policy, the D2D
Fairness Scheduling (DFS) Policy, harnesses maximal performance from
D2D-enabled systems under the orthogonal sharing setting. For even higher
spectral efficiency, cellular systems with D2D can schedule the same frequency
resource for more than one D2D pairs. Under this non-orthogonal sharing
environment, we propose a novel group scheduling policy, the Group Fairness
Scheduling (GFS) Policy, that exploits both spatial frequency reuse and
multiuser diversity in order to deliver dramatic improvements to system
performance with perfect fairness among the users, regardless of whether they
are cellular or D2D users.Comment: Submitted to IEEE Transactions on Wireless Communication
D2D-Based Grouped Random Access to Mitigate Mobile Access Congestion in 5G Sensor Networks
The Fifth Generation (5G) wireless service of sensor networks involves
significant challenges when dealing with the coordination of ever-increasing
number of devices accessing shared resources. This has drawn major interest
from the research community as many existing works focus on the radio access
network congestion control to efficiently manage resources in the context of
device-to-device (D2D) interaction in huge sensor networks. In this context,
this paper pioneers a study on the impact of D2D link reliability in
group-assisted random access protocols, by shedding the light on beneficial
performance and potential limitations of approaches of this kind against
tunable parameters such as group size, number of sensors and reliability of D2D
links. Additionally, we leverage on the association with a Geolocation Database
(GDB) capability to assist the grouping decisions by drawing parallels with
recent regulatory-driven initiatives around GDBs and arguing benefits of the
suggested proposal. Finally, the proposed method is approved to significantly
reduce the delay over random access channels, by means of an exhaustive
simulation campaign.Comment: First submission to IEEE Communications Magazine on Oct.28.2017.
Accepted on Aug.18.2019. This is the camera-ready versio
Optimal Virtualized Inter-Tenant Resource Sharing for Device-to-Device Communications in 5G Networks
Device-to-Device (D2D) communication is expected to enable a number of new
services and applications in future mobile networks and has attracted
significant research interest over the last few years. Remarkably, little
attention has been placed on the issue of D2D communication for users belonging
to different operators. In this paper, we focus on this aspect for D2D users
that belong to different tenants (virtual network operators), assuming
virtualized and programmable future 5G wireless networks. Under the assumption
of a cross-tenant orchestrator, we show that significant gains can be achieved
in terms of network performance by optimizing resource sharing from the
different tenants, i.e., slices of the substrate physical network topology. To
this end, a sum-rate optimization framework is proposed for optimal sharing of
the virtualized resources. Via a wide site of numerical investigations, we
prove the efficacy of the proposed solution and the achievable gains compared
to legacy approaches.Comment: 10 pages, 7 figure
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues
As a key technique for enabling artificial intelligence, machine learning
(ML) is capable of solving complex problems without explicit programming.
Motivated by its successful applications to many practical tasks like image
recognition, both industry and the research community have advocated the
applications of ML in wireless communication. This paper comprehensively
surveys the recent advances of the applications of ML in wireless
communication, which are classified as: resource management in the MAC layer,
networking and mobility management in the network layer, and localization in
the application layer. The applications in resource management further include
power control, spectrum management, backhaul management, cache management,
beamformer design and computation resource management, while ML based
networking focuses on the applications in clustering, base station switching
control, user association and routing. Moreover, literatures in each aspect is
organized according to the adopted ML techniques. In addition, several
conditions for applying ML to wireless communication are identified to help
readers decide whether to use ML and which kind of ML techniques to use, and
traditional approaches are also summarized together with their performance
comparison with ML based approaches, based on which the motivations of surveyed
literatures to adopt ML are clarified. Given the extensiveness of the research
area, challenges and unresolved issues are presented to facilitate future
studies, where ML based network slicing, infrastructure update to support ML
based paradigms, open data sets and platforms for researchers, theoretical
guidance for ML implementation and so on are discussed.Comment: 34 pages,8 figure
Exploiting Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing
In this article we propose a novel Device-to-Device (D2D) Crowd framework for
5G mobile edge computing, where a massive crowd of devices at the network edge
leverage the network-assisted D2D collaboration for computation and
communication resource sharing among each other. A key objective of this
framework is to achieve energy-efficient collaborative task executions at
network-edge for mobile users. Specifically, we first introduce the D2D Crowd
system model in details, and then formulate the energy-efficient D2D Crowd task
assignment problem by taking into account the necessary constraints. We next
propose a graph matching based optimal task assignment policy, and further
evaluate its performance through extensive numerical study, which shows a
superior performance of more than 50% energy consumption reduction over the
case of local task executions. Finally, we also discuss the directions of
extending the D2D Crowd framework by taking into variety of application
factors.Comment: Xu Chen, Lingjun Pu, Lin Gao, Weigang Wu, and Di Wu, "Exploiting
Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing,"
accepted by IEEE Wireless Communications, 201
V2X Meets NOMA: Non-Orthogonal Multiple Access for 5G Enabled Vehicular Networks
Benefited from the widely deployed infrastructure, the LTE network has
recently been considered as a promising candidate to support the
vehicle-to-everything (V2X) services. However, with a massive number of devices
accessing the V2X network in the future, the conventional OFDM-based LTE
network faces the congestion issues due to its low efficiency of orthogonal
access, resulting in significant access delay and posing a great challenge
especially to safety-critical applications. The non-orthogonal multiple access
(NOMA) technique has been well recognized as an effective solution for the
future 5G cellular networks to provide broadband communications and massive
connectivity. In this article, we investigate the applicability of NOMA in
supporting cellular V2X services to achieve low latency and high reliability.
Starting with a basic V2X unicast system, a novel NOMA-based scheme is proposed
to tackle the technical hurdles in designing high spectral efficient scheduling
and resource allocation schemes in the ultra dense topology. We then extend it
to a more general V2X broadcasting system. Other NOMA-based extended V2X
applications and some open issues are also discussed.Comment: Accepted by IEEE Wireless Communications Magazin
Heterogeneous Services Provisioning in Small Cell Networks with Cache and Mobile Edge Computing
In the area of full duplex (FD)-enabled small cell networks, limited works
have been done on consideration of cache and mobile edge communication (MEC).
In this paper, a virtual FD-enabled small cell network with cache and MEC is
investigated for two heterogeneous services, high-data-rate service and
computation-sensitive service. In our proposed scheme, content caching and FD
communication are closely combined to offer high-data-rate services without the
cost of backhaul resource. Computing offloading is conducted to guarantee the
delay requirement of users. Then we formulate a virtual resource allocation
problem, in which user association, power control, caching and computing
offloading policies and resource allocation are jointly considered. Since the
original problem is a mixed combinatorial problem, necessary variables
relaxation and reformulation are conducted to transfer the original problem to
a convex problem. Furthermore, alternating direction method of multipliers
(ADMM) algorithm is adopted to obtain the optimal solution. Finally, extensive
simulations are conducted with different system configurations to verify the
effectiveness of the proposed scheme
Cloud Computing - Architecture and Applications
In the era of Internet of Things and with the explosive worldwide growth of
electronic data volume, and associated need of processing, analysis, and
storage of such humongous volume of data, it has now become mandatory to
exploit the power of massively parallel architecture for fast computation.
Cloud computing provides a cheap source of such computing framework for large
volume of data for real-time applications. It is, therefore, not surprising to
see that cloud computing has become a buzzword in the computing fraternity over
the last decade. This book presents some critical applications in cloud
frameworks along with some innovation design of algorithms and architecture for
deployment in cloud environment. It is a valuable source of knowledge for
researchers, engineers, practitioners, and graduate and doctoral students
working in the field of cloud computing. It will also be useful for faculty
members of graduate schools and universities.Comment: Edited Volume published by Intech Publishers, Croatia, June 2017. 138
pages. ISBN 978-953-51-3244-8, Print ISBN 978-953-51-3243-1. Link:
https://www.intechopen.com/books/cloud-computing-architecture-and-application
Towards Ultra-Reliable Low-Latency Communications: Typical Scenarios, Possible Solutions, and Open Issues
Ultra-reliable low-latency communications (URLLC) has been considered as one
of the three new application scenarios in the \emph{5th Generation} (5G) \emph
{New Radio} (NR), where the physical layer design aspects have been specified.
With the 5G NR, we can guarantee the reliability and latency in radio access
networks. However, for communication scenarios where the transmission involves
both radio access and wide area core networks, the delay in radio access
networks only contributes to part of the \emph{end-to-end} (E2E) delay. In this
paper, we outline the delay components and packet loss probabilities in typical
communication scenarios of URLLC, and formulate the constraints on E2E delay
and overall packet loss probability. Then, we summarize possible solutions in
the physical layer, the link layer, the network layer, and the cross-layer
design, respectively. Finally, we discuss the open issues in prediction and
communication co-design for URLLC in wide area large scale networks.Comment: 8 pages, 7 figures. Accepted by IEEE Vehicular Technology Magazin
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