4,692 research outputs found
Dynamic Base Station Repositioning to Improve Spectral Efficiency of Drone Small Cells
With recent advancements in drone technology, researchers are now considering
the possibility of deploying small cells served by base stations mounted on
flying drones. A major advantage of such drone small cells is that the
operators can quickly provide cellular services in areas of urgent demand
without having to pre-install any infrastructure. Since the base station is
attached to the drone, technically it is feasible for the base station to
dynamic reposition itself in response to the changing locations of users for
reducing the communication distance, decreasing the probability of signal
blocking, and ultimately increasing the spectral efficiency. In this paper, we
first propose distributed algorithms for autonomous control of drone movements,
and then model and analyse the spectral efficiency performance of a drone small
cell to shed new light on the fundamental benefits of dynamic repositioning. We
show that, with dynamic repositioning, the spectral efficiency of drone small
cells can be increased by nearly 100\% for realistic drone speed, height, and
user traffic model and without incurring any major increase in drone energy
consumption.Comment: Accepted at IEEE WoWMoM 2017 - 9 pages, 2 tables, 4 figure
Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks
Soaring capacity and coverage demands dictate that future cellular networks
need to soon migrate towards ultra-dense networks. However, network
densification comes with a host of challenges that include compromised energy
efficiency, complex interference management, cumbersome mobility management,
burdensome signaling overheads and higher backhaul costs. Interestingly, most
of the problems, that beleaguer network densification, stem from legacy
networks' one common feature i.e., tight coupling between the control and data
planes regardless of their degree of heterogeneity and cell density.
Consequently, in wake of 5G, control and data planes separation architecture
(SARC) has recently been conceived as a promising paradigm that has potential
to address most of aforementioned challenges. In this article, we review
various proposals that have been presented in literature so far to enable SARC.
More specifically, we analyze how and to what degree various SARC proposals
address the four main challenges in network densification namely: energy
efficiency, system level capacity maximization, interference management and
mobility management. We then focus on two salient features of future cellular
networks that have not yet been adapted in legacy networks at wide scale and
thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and
device-to-device (D2D) communications. After providing necessary background on
CoMP and D2D, we analyze how SARC can particularly act as a major enabler for
CoMP and D2D in context of 5G. This article thus serves as both a tutorial as
well as an up to date survey on SARC, CoMP and D2D. Most importantly, the
article provides an extensive outlook of challenges and opportunities that lie
at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201
A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques
A growing trend for information technology is to not just react to changes, but anticipate them as much as possible. This paradigm made modern solutions, such as recommendation systems, a ubiquitous presence in today's digital transactions. Anticipatory networking extends the idea to communication technologies by studying patterns and periodicity in human behavior and network dynamics to optimize network performance. This survey collects and analyzes recent papers leveraging context information to forecast the evolution of network conditions and, in turn, to improve network performance. In particular, we identify the main prediction and optimization tools adopted in this body of work and link them with objectives and constraints of the typical applications and scenarios. Finally, we consider open challenges and research directions to make anticipatory networking part of next generation networks
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
A Comprehensive Survey on Moving Networks
The unprecedented increase in the demand for mobile data, fuelled by new
emerging applications such as HD video streaming and heightened online
activities has caused massive strain on the existing cellular networks. As a
solution, the 5G technology has been introduced to improve network performance
through various innovative features such as mmWave spectrum and HetNets. In
essence, HetNets include several small cells underlaid within macro-cell to
serve densely populated regions. Recently, a mobile layer of HetNet has been
under consideration by the researchers and is often referred to as moving
networks. Moving networks comprise of mobile cells that are primarily
introduced to improve QoS for commuting users inside public transport because
the QoS is deteriorated due to vehicular penetration losses. Furthermore, the
users inside fast moving public transport also exert excessive load on the core
network due to large group handovers. To this end, mobile cells will play a
crucial role in reducing overall handover count and will help in alleviating
these problems by decoupling in-vehicle users from the core network.
To date, remarkable research results have been achieved by the research
community in addressing challenges linked to moving networks. However, to the
best of our knowledge, a discussion on moving networks in a holistic way is
missing in the current literature. To fill the gap, in this paper, we
comprehensively survey moving networks. We cover the technological aspects and
their applications in the futuristic applications. We also discuss the
use-cases and value additions that moving networks may bring to future cellular
architecture and identify the challenges associated with them. Based on the
identified challenges we discuss the future research directions.Comment: This survey has been submitted to IEEE Communications Surveys &
Tutorial
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