3,092 research outputs found
A Case for Time Slotted Channel Hopping for ICN in the IoT
Recent proposals to simplify the operation of the IoT include the use of
Information Centric Networking (ICN) paradigms. While this is promising,
several challenges remain. In this paper, our core contributions (a) leverage
ICN communication patterns to dynamically optimize the use of TSCH (Time
Slotted Channel Hopping), a wireless link layer technology increasingly popular
in the IoT, and (b) make IoT-style routing adaptive to names, resources, and
traffic patterns throughout the network--both without cross-layering. Through a
series of experiments on the FIT IoT-LAB interconnecting typical IoT hardware,
we find that our approach is fully robust against wireless interference, and
almost halves the energy consumed for transmission when compared to CSMA. Most
importantly, our adaptive scheduling prevents the time-slotted MAC layer from
sacrificing throughput and delay
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
Scalable RAN Virtualization in Multi-Tenant LTE-A Heterogeneous Networks (Extended version)
Cellular communications are evolving to facilitate the current and expected
increasing needs of Quality of Service (QoS), high data rates and diversity of
offered services. Towards this direction, Radio Access Network (RAN)
virtualization aims at providing solutions of mapping virtual network elements
onto radio resources of the existing physical network. This paper proposes the
Resources nEgotiation for NEtwork Virtualization (RENEV) algorithm, suitable
for application in Heterogeneous Networks (HetNets) in Long Term
Evolution-Advanced (LTE-A) environments, consisting of a macro evolved NodeB
(eNB) overlaid with small cells. By exploiting Radio Resource Management (RRM)
principles, RENEV achieves slicing and on demand delivery of resources.
Leveraging the multi-tenancy approach, radio resources are transferred in terms
of physical radio Resource Blocks (RBs) among multiple heterogeneous base
stations, interconnected via the X2 interface. The main target is to deal with
traffic variations in geographical dimension. All signaling design
considerations under the current Third Generation Partnership Project (3GPP)
LTE-A architecture are also investigated. Analytical studies and simulation
experiments are conducted to evaluate RENEV in terms of network's throughput as
well as its additional signaling overhead. Moreover we show that RENEV can be
applied independently on top of already proposed schemes for RAN virtualization
to improve their performance. The results indicate that significant merits are
achieved both from network's and users' perspective as well as that it is a
scalable solution for different number of small cells.Comment: 40 pages (including Appendices), Accepted for publication in the IEEE
Transactions on Vehicular Technolog
Hierarchical Resource Allocation Framework for Hyper-Dense Small Cell Networks
This paper considers joint power control and subchannel allocation for co-tier interference
mitigation in extremely dense small cell networks, which is formulated as a combinatorial optimization problem. Since it is intractable to obtain the globally optimum assignment policy for existing techniques due to the huge computation and communication overheads in ultra-dense scenario, in this paper, we propose a hierarchical resource allocation framework to achieve a desirable solution. Speci cally, the solution is obtained by dividing the original optimization problem into four stages in partially distributed manner. First, we propose a divide-and-conquer strategy by invoking clustering technique to decompose
the dense network into smaller disjoint clusters. Then, within each cluster, one of the small cell access points is elected as a cluster head to carry out intra-cluster subchannel allocation with a low-complexity algorithm. To tackle the issue of inter-cluster interference, we further develop a distributed learning-base coordination mechanism. Moreover, a local power adjustment scheme is also presented to improve the system performance. Numerical results verify the ef ciency of the proposed hierarchical scheme, and demonstrate that our solution outperforms the state-of-the-art methods, especially for hyper-dense networks
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