3,521 research outputs found
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 cell outage management framework for dense heterogeneous networks
In this paper, we present a novel cell outage management (COM) framework for heterogeneous networks with split control and data planes-a candidate architecture for meeting future capacity, quality-of-service, and energy efficiency demands. In such an architecture, the control and data functionalities are not necessarily handled by the same node. The control base stations (BSs) manage the transmission of control information and user equipment (UE) mobility, whereas the data BSs handle UE data. An implication of this split architecture is that an outage to a BS in one plane has to be compensated by other BSs in the same plane. Our COM framework addresses this challenge by incorporating two distinct cell outage detection (COD) algorithms to cope with the idiosyncrasies of both data and control planes. The COD algorithm for control cells leverages the relatively larger number of UEs in the control cell to gather large-scale minimization-of-drive-test report data and detects an outage by applying machine learning and anomaly detection techniques. To improve outage detection accuracy, we also investigate and compare the performance of two anomaly-detecting algorithms, i.e., k-nearest-neighbor- and local-outlier-factor-based anomaly detectors, within the control COD. On the other hand, for data cell COD, we propose a heuristic Grey-prediction-based approach, which can work with the small number of UE in the data cell, by exploiting the fact that the control BS manages UE-data BS connectivity and by receiving a periodic update of the received signal reference power statistic between the UEs and data BSs in its coverage. The detection accuracy of the heuristic data COD algorithm is further improved by exploiting the Fourier series of the residual error that is inherent to a Grey prediction model. Our COM framework integrates these two COD algorithms with a cell outage compensation (COC) algorithm that can be applied to both planes. Our COC solution utilizes an actor-critic-based reinforcement learning algorithm, which optimizes the capacity and coverage of the identified outage zone in a plane, by adjusting the antenna gain and transmission power of the surrounding BSs in that plane. The simulation results show that the proposed framework can detect both data and control cell outage and compensate for the detected outage in a reliable manner
Improved Handover Through Dual Connectivity in 5G mmWave Mobile Networks
The millimeter wave (mmWave) bands offer the possibility of orders of
magnitude greater throughput for fifth generation (5G) cellular systems.
However, since mmWave signals are highly susceptible to blockage, channel
quality on any one mmWave link can be extremely intermittent. This paper
implements a novel dual connectivity protocol that enables mobile user
equipment (UE) devices to maintain physical layer connections to 4G and 5G
cells simultaneously. A novel uplink control signaling system combined with a
local coordinator enables rapid path switching in the event of failures on any
one link. This paper provides the first comprehensive end-to-end evaluation of
handover mechanisms in mmWave cellular systems. The simulation framework
includes detailed measurement-based channel models to realistically capture
spatial dynamics of blocking events, as well as the full details of MAC, RLC
and transport protocols. Compared to conventional handover mechanisms, the
study reveals significant benefits of the proposed method under several
metrics.Comment: 16 pages, 13 figures, to appear on the 2017 IEEE JSAC Special Issue
on Millimeter Wave Communications for Future Mobile Network
CogCell: Cognitive Interplay between 60GHz Picocells and 2.4/5GHz Hotspots in the 5G Era
Rapid proliferation of wireless communication devices and the emergence of a
variety of new applications have triggered investigations into next-generation
mobile broadband systems, i.e., 5G. Legacy 2G--4G systems covering large areas
were envisioned to serve both indoor and outdoor environments. However, in the
5G-era, 80\% of overall traffic is expected to be generated in indoors. Hence,
the current approach of macro-cell mobile network, where there is no
differentiation between indoors and outdoors, needs to be reconsidered. We
envision 60\,GHz mmWave picocell architecture to support high-speed indoor and
hotspot communications. We envisage the 5G indoor network as a combination of-,
and interplay between, 2.4/5\,GHz having robust coverage and 60\,GHz links
offering high datarate. This requires an intelligent coordination and
cooperation. We propose 60\,GHz picocellular network architecture, called
CogCell, leveraging the ubiquitous WiFi. We propose to use 60\,GHz for the data
plane and 2.4/5GHz for the control plane. The hybrid network architecture
considers an opportunistic fall-back to 2.4/5\,GHz in case of poor connectivity
in the 60\,GHz domain. Further, to avoid the frequent re-beamforming in 60\,GHz
directional links due to mobility, we propose a cognitive module -- a
sensor-assisted intelligent beam switching procedure -- which reduces the
communication overhead. We believe that the CogCell concept will help future
indoor communications and possibly outdoor hotspots, where mobile stations and
access points collaborate with each other to improve the user experience.Comment: 14 PAGES in IEEE Communications Magazine, Special issue on Emerging
Applications, Services and Engineering for Cognitive Cellular Systems
(EASE4CCS), July 201
Context-Awareness Enhances 5G Multi-Access Edge Computing Reliability
The fifth generation (5G) mobile telecommunication network is expected to
support Multi- Access Edge Computing (MEC), which intends to distribute
computation tasks and services from the central cloud to the edge clouds.
Towards ultra-responsive, ultra-reliable and ultra-low-latency MEC services,
the current mobile network security architecture should enable a more
decentralized approach for authentication and authorization processes. This
paper proposes a novel decentralized authentication architecture that supports
flexible and low-cost local authentication with the awareness of context
information of network elements such as user equipment and virtual network
functions. Based on a Markov model for backhaul link quality, as well as a
random walk mobility model with mixed mobility classes and traffic scenarios,
numerical simulations have demonstrated that the proposed approach is able to
achieve a flexible balance between the network operating cost and the MEC
reliability.Comment: Accepted by IEEE Access on Feb. 02, 201
Flat Cellular (UMTS) Networks
Traditionally, cellular systems have been built in a hierarchical manner: many specialized cellular access network elements that collectively form a hierarchical cellular system. When 2G and later 3G systems were designed there was a good reason to make system hierarchical: from a cost-perspective it was better to concentrate traffic and to share the cost of processing equipment over a large set of users while keeping the base stations relatively cheap. However, we believe the economic reasons for designing cellular systems in a hierarchical manner have disappeared: in fact, hierarchical architectures hinder future efficient deployments. In this paper, we argue for completely flat cellular wireless systems, which need just one type of specialized network element to provide radio access network (RAN) functionality, supplemented by standard IP-based network elements to form a cellular network. While the reason for building a cellular system in a hierarchical fashion has disappeared, there are other good reasons to make the system architecture flat: (1) as wireless transmission techniques evolve into hybrid ARQ systems, there is less need for a hierarchical cellular system to support spatial diversity; (2) we foresee that future cellular networks are part of the Internet, while hierarchical systems typically use interfaces between network elements that are specific to cellular standards or proprietary. At best such systems use IP as a transport medium, not as a core component; (3) a flat cellular system can be self scaling while a hierarchical system has inherent scaling issues; (4) moving all access technologies to the edge of the network enables ease of converging access technologies into a common packet core; and (5) using an IP common core makes the cellular network part of the Internet
Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks
The fifth generation of cellular networks (5G) will rely on edge cloud
deployments to satisfy the ultra-low latency demand of future applications. In
this paper, we argue that such deployments can also be used to enable advanced
data-driven and Machine Learning (ML) applications in mobile networks. We
propose an edge-controller-based architecture for cellular networks and
evaluate its performance with real data from hundreds of base stations of a
major U.S. operator. In this regard, we will provide insights on how to
dynamically cluster and associate base stations and controllers, according to
the global mobility patterns of the users. Then, we will describe how the
controllers can be used to run ML algorithms to predict the number of users in
each base station, and a use case in which these predictions are exploited by a
higher-layer application to route vehicular traffic according to network Key
Performance Indicators (KPIs). We show that the prediction accuracy improves
when based on machine learning algorithms that rely on the controllers' view
and, consequently, on the spatial correlation introduced by the user mobility,
with respect to when the prediction is based only on the local data of each
single base station.Comment: 15 pages, 10 figures, 5 tables. IEEE Transactions on Mobile Computin
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