3,681 research outputs found
Cellular-Broadcast Service Convergence through Caching for CoMP Cloud RANs
Cellular and Broadcast services have been traditionally treated independently
due to the different market requirements, thus resulting in different business
models and orthogonal frequency allocations. However, with the advent of cheap
memory and smart caching, this traditional paradigm can converge into a single
system which can provide both services in an efficient manner. This paper
focuses on multimedia delivery through an integrated network, including both a
cellular (also known as unicast or broadband) and a broadcast last mile
operating over shared spectrum. The subscribers of the network are equipped
with a cache which can effectively create zero perceived latency for multimedia
delivery, assuming that the content has been proactively and intelligently
cached. The main objective of this work is to establish analytically the
optimal content popularity threshold, based on a intuitive cost function. In
other words, the aim is to derive which content should be broadcasted and which
content should be unicasted. To facilitate this, Cooperative Multi- Point
(CoMP) joint processing algorithms are employed for the uni and broad-cast PHY
transmissions. To practically implement this, the integrated network controller
is assumed to have access to traffic statistics in terms of content popularity.
Simulation results are provided to assess the gain in terms of total spectral
efficiency. A conventional system, where the two networks operate
independently, is used as benchmark.Comment: Submitted to IEEE PIMRC 201
An overview of 5G technologies
Since the development of 4G cellular networks is considered to have ended in 2011, the attention of the research community is now focused on innovations in wireless communications technology with the introduction of the fifth-generation (5G) technology. One cycle for each generation of cellular development is generally thought to be about 10 years; so the 5G networks are promising to be deployed around 2020. This chapter will provide an overview and major research directions for the 5G that have been or are being deployed, presenting new challenges as well as recent research results related to the 5G technologies. Through this chapter, readers will have a full picture of the technologies being deployed toward the 5G networks and vendors of hardware devices with various prototypes of the 5G wireless communications systems
An overview of 5G technologies
Since the development of 4G cellular networks is considered to have ended in 2011, the attention of the research community is now focused on innovations in wireless communications technology with the introduction of the fifth-generation (5G) technology. One cycle for each generation of cellular development is generally thought to be about 10 years; so the 5G networks are promising to be deployed around 2020. This chapter will provide an overview and major research directions for the 5G that have been or are being deployed, presenting new challenges as well as recent research results related to the 5G technologies. Through this chapter, readers will have a full picture of the technologies being deployed toward the 5G networks and vendors of hardware devices with various prototypes of the 5G wireless communications systems
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
Iris: Deep Reinforcement Learning Driven Shared Spectrum Access Architecture for Indoor Neutral-Host Small Cells
We consider indoor mobile access, a vital use case for current and future
mobile networks. For this key use case, we outline a vision that combines a
neutral-host based shared small-cell infrastructure with a common pool of
spectrum for dynamic sharing as a way forward to proliferate indoor small-cell
deployments and open up the mobile operator ecosystem. Towards this vision, we
focus on the challenges pertaining to managing access to shared spectrum (e.g.,
3.5GHz US CBRS spectrum). We propose Iris, a practical shared spectrum access
architecture for indoor neutral-host small-cells. At the core of Iris is a deep
reinforcement learning based dynamic pricing mechanism that efficiently
mediates access to shared spectrum for diverse operators in a way that provides
incentives for operators and the neutral-host alike. We then present the Iris
system architecture that embeds this dynamic pricing mechanism alongside
cloud-RAN and RAN slicing design principles in a practical neutral-host design
tailored for the indoor small-cell environment. Using a prototype
implementation of the Iris system, we present extensive experimental evaluation
results that not only offer insight into the Iris dynamic pricing process and
its superiority over alternative approaches but also demonstrate its deployment
feasibility
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
Indoor wireless communications and applications
Chapter 3 addresses challenges in radio link and system design in indoor scenarios. Given the fact that most human activities take place in indoor environments, the need for supporting ubiquitous indoor data connectivity and location/tracking service becomes even more important than in the previous decades. Specific technical challenges addressed in this section are(i), modelling complex indoor radio channels for effective antenna deployment, (ii), potential of millimeter-wave (mm-wave) radios for supporting higher data rates, and (iii), feasible indoor localisation and tracking techniques, which are summarised in three dedicated sections of this chapter
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