Presentation onFuture Mobile Networks

TLT – 6507 Advanced Course on Wireless
Department of Communications Engineering
Communications
Presentation on
Future Mobile Networks
Dipesh Paudel
Huseyin Oge
Pratik Joshi
Semanta Neupane
Tugrul Acikgöz

Flow of presentation
o Introduction
o Current scenario
o Future Mobile Networks
o Cognitive Radio Network (CRN)
o Applications of CRN
o Conclusion
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TLT-6507 Advanced Course on Wireless Communications

Introduction
o Mobile Network is a wireless service, or wireless carrier that
provides network, voice, and data services
o A set of towers which a cell phone can communicate with
 Switch to the nearest possible tower
o Company that operates a mobile network is Mobile Network
Operator (MNO)
3

Introduction
Classified based on the distance they are meant to cover.
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Current Scenario
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Current Scenario
o GSM – the most popular
o Used to carry voice traffic and some data
o Available to almost 90% of world population
o Mobile Data Explosion
o Introduction of 3G Mobile System
 Wideband CDMA, HSPA, HSPA+
o Introduction of HSPA transformed voice dominated to packet
dominated
o LTE (4G): improving end-user throughput, cell capacity and user
plane latency
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Future Mobile Network
o Increasing demand
 Mobile internet access, Social networking, navigation,
location based service and so on
o 3rd Generation Partnership Project (3GPP)
 Brings together a number of telecommunications standards
bodies.
 Produce globally applicable Technical Specifications and
Reports for a 3rd Gen Mobile System based on GSM core
networks and the radio access technologies that they support
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Future Mobile Network scenarios
o Heterogeneous Networks and Homogeneous Networks
 Current wireless cellular networks
 Typically deployed as homogeneous networks.
 Using a macro centric planning process.
 Collection of user terminals, all the base stations have similar
transmit power levels, antenna patterns, receiver noise level
 Similar connectivity to the (packet) data network
o Heterogeneous Networks
o Cloud RAN (C-RAN)
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Future Mobile Network Scenarios
o Current wireless cellular networks
 BSs are deployed for wide area coverage.
 The footprint of a BS varies depending on traffic demand.
 In Het-Nets, existing macrocells and microcells remain,
providing essential coverage.
 Additionally, large macrocells hold advantages in supporting
high-mobility users for reduced handover frequency.
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Heterogeneous Networks
a.k.a "HetNets”
o Expanding mobile
network capacity.
o Composed of multiple
radio access
technologies,
architectures,
transmission solutions,
and base stations of
varying transmission
power.
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o Heterogeneous Networks
 Heterogeneous Network utilizes mix of macro, pico, femto and
relay base stations
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o Heterogeneous Networks: an example

o Heterogeneous Networks: Disadvantages
 Network Management Complexity
 Increasing number and variety of managed cells
 Location of Home eNBs (Femto eNBs)
 Coordination of operation of network domains
 The cost structure of heterogeneous networks is much lower.
Since low power devices serve much smaller areas with
minimal functionality.
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o Cloud RAN
 A new distributed architecture
 Aims to reduce the number of cell sites while increasing the
base station deployment density.
o The concept of the Cloud RAN
 Breaks down the base station into a Base Unit (BU)
 Converts digital signals to analog, amplifies the power, and
sends the actual transmission
14

FUTURE MOBILE NETWORK SCENARIOS
o Network Architecture Evolution: Cloud RAN
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o The advantages of C-RAN over a conventional RAN architecture
o Reduction of the Total Cost of Ownership
o Simplifies operations
o Enables the distribution of traffic load
o Improves load balancing and mobility
o Increased data rates can be achieved
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o Cloud RAN challenges
 Technical and economic challenges due to the required
optical fiber connection between the central location and
antenna sites
 Optical Fiber Connections is very expensive
 Connection imposes challenging requirements concerning
data rates, latency jitter and latency asymmetry
 For redundancy reasons, one antenna will likely be linked to
the central location through several connections in order to
avoid a single point of failure
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o The emergence of C-RAN will pose new requirements on
network management, thus if an operator decides to introduce C-RAN,
this will proceed in a gradual way
o Requirements for future OAM Systems
 Researched Mobile Network Technologies like HetNet or C-RAN
add to the complexity of Mobile Network Management
by increasing network elements
 Complexity is challenging and leads to human-induced errors,
however customers aren’t willing to suffer from poor quality of
service due to these network-internal issues
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o According to foresights, revenue per bit in mobile network will
continue to decline, thus Operation Expenditure (OPEX) will be
more dramatically reduced than with SON
o Due to these reasons, future OAM Systems have to aid humans
by reducing the complexity, reduce the OPEX and improve the
availability of the mobile network through more automation
19

COGNITIVE RADIO NETWORKS
o Fundamentals of Cognitive Radio
o Framework
o Artificial Intelligence (AI)
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Why Cognitive Radio Networks?
o Briefly, SON is a set of automated operations and management
processes for Mobile Networks
o The behaviour of SON operations are configured through high-level
parameters which is determined by human operators.
o Due to depending on human operators, this turns out to be
cumbersome if the context changes regularly.
o From here Cognitive Radio steps in, because mobile networks
must adapt themselves to changes in the operational context
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o Cognitive radios could provide a paradigm shift in the way that
spectra is regulated and used
o Cognitive Radios are able to sense the spectrum to see whether
it is being used by the Primary User.
o Quick look to IEEE 802.22 Working Group
o Frequency reuse is typically done through frequency planning,
resulting in an inefficient utilization of frequencies across different
geographical regions
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Cognitive Radio Network
o SON introduced framework with closed loop automation.
o Automate the management by defining SON functions
o Functions perform their task without human intervention.
o CRN extends this vision by substituting SON functions and
workflows with Cognitive Processes controlled by goals.
o Thus, SON network management loop is replaced by a cognitive
network management loop.
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The cognitive network management loop

Cognitive loop
o Proposed by Fortuna and Mohorcic in 2009.
o Cognitive Process continuously monitors the environment i.e. sense
through Network Status Sensors.
o This information is used to create several strategies how the network
configuration should be changed i.e. plan based on goals
o Sensor Information is used for learning to build up knowledge of the
effects of actions i.e. learn
o The system has to decide which plan to implement based on goal i.e.
decide
o This strategy is then enacted using Network API i.e. act
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o As Cognitive loop is very complex, some steps can be bypassed.
o It is possible to declare a sensor output as critical and ssign an
action which causes the system to skip the plan and decide
phase and act immediately.
o CRN do not assume any specific architecture of the network.
o Thus, Functionality of the process can be distributed same way
as SON.
o i.e. centralised, decentralised and hybrid.
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o CRN relies on sophisticated AI technologies to fulfill the
challenging vision.
o Special Interest for cognitive loop:
 Knowledge Representations
 Planning and decision algorithms
 Learning technology
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Application of CRN
o CRN can sense the surrounding environment and without the
intervention of the user can adapt to the users communication
needs.
o CRN has capability for efficient spectrum utilization since it can
intelligently detect whether any portion of the spectrum is in use
or not.
o The main application of CRN is to solve the shortcomings of SON
and ease the functions of network operators more.
o SON concepts can be improved in areas of self-configuration,
self-optimization, self-healing and operation using CRN concept.
29

Application of CRN
Self-Configuration
o SON involves loading initial configuration parameters to new
base stations.
o This involves hectic and complex manual planning procedures.
o In CRN initial configuration is loaded in the base stations
automatically by OAM systems(operations, administration and
maintenance).
o The configuration parameters are obtained by measurements of
environment from new base station, location of new base station
and operator policies.
o These obtained parameters are loaded into the base-station
using SON algorithms.
o The efficiency of this configuration is continuously evaluated by
the base-station and its neighbors. This knowledge is then used
for loading configurations in other base stations.
30

Application of CRN
Self-optimization
o Self-optimization algorithms in SON are static and do not change
with the change in operational environment.
o These algorithms cannot handle uncertainty problems which
requires probabilistic reasoning.
o CRN improves the self-optimization process by employing
inference based algorithms.
o The network performance is measured from which the current
system state and performance issues are inferred using uncertain
knowledge representation.
o The cognitive process continuously monitors the performance to
gather information for making the self-optimization process more
effective.
31

Application of CRN
Self-healing
o SON self-healing involves two phases.
o 1st phase: problem detection and diagnosis using predefined
knowledge.
o 2nd phase: action for healing done by compensation of a cell
outage by neighboring cells.
 requires extensive manual work which will increase with the
increase in number of nodes.
o CRN self-healing process involves automated detection and
diagnosis of network failures and automated advisory process
determining which recovery actions to execute.
o The automated advisory process should give the most optimal
solution for the problem.
o The whole process is continuously monitored as a part of
learning, so that the solutions for different situations can be used
in the future.
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Application of CRN
Operation
o SON has defined the co-ordination function for controlling
different SON functions.
o The implementation of these functions is time consuming, costly
and error prone.
o Changes in certain parameters forces change in operational
behavior. To address these changes considerable human effort is
needed.
o CRN uses knowledge representation which is used to represent
semantics of operational goals, network properties and network
status.
o The information is now used for automated reasoning for
obtaining coordination function at run time.
o Any change in the goals can be reflected in the knowledge Base
of the system.
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Conclusion
o Future mobile technology has many challenges. The system
should be reliable, cheap and fast with the ability to adapt to the
changing environment and integrating diverse devices.
o To meet these requirements we have to go beyond SON
concept.
o SON has been extended to include automation in the network
leading the way for CRN concept.
o CRN extensively uses theory and algorithms of Artificial
Intelligence.
o CRN is in the initial phase of development. With the integration of
machine intelligence and communication technologies, CRN is
certainly the future of mobile communication.
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THANK YOU
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Presentation onFuture Mobile Networks

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