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In the name of God, the most compassionate, the most merciful
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5G ARCHITECTURE
VISION
Mohammad Movahhedian, Ph.D., MIET, MIEEE
www.mci.ir
5G Seminar at ITRC
14 Jun. 2015
Outline
Introduction, Motivations & Current Trends of Mobile Networks
5G Challenges & Design Parameters
The Vision for 5G Architecture
Logical Network Layers: Radio Network & Network Cloud
Use of SDN & NFV to Dynamically Deploy & Scale Network Functions
Lean Protocol Stack
C/U-Plane Split for Independent Coverage & Capacity
Support for Multiple Devices & Group Mobility
Data-Driven Network Intelligence
Issues
D2D
User-Centric IP Mobility Management
A New Proposition for Control Plane
Conclusions
2
Introduction & Current Trends of
Mobile Networks
Introduction
Unlike successive evolutionary releases of 3GPP starting from 3GPP-R99 all the way to LTE-A
(i.e. 3GPP Rel.10) and even above, 5G is a REVOLUTION in the ICT filed
Overall IP traffic is expected to grow 23% annually between 2012-2017; this growth is 66%
over the same period for mobile IP traffic
With this growth in mobile IP traffic, the primary objective of 5G architecture is to bring a
high-capacity, agile, low-cost solution to ensure both user-satisfaction and MSP1 profitability
5G will efficiently enable new ultra reliable, dependable, secure, privacy-preserving, and
delay-critical services to everyone & everything
5G is necessitated by new & emerging use-cases, e.g., high-resolution video streaming,
tactile Internet, road safety, remote monitoring and real-time control
The above use-cases place new requirements on the network related to throughput, E2E
latency, reliability and robustness of the network
At the same time, ultra-dense small cell deployments and new technologies such as mMIMO2,
SDN3, and NFV4 provide a motivation to rethink the fundamental design principles toward 5G
1
Mobile service provider
2 Massive MIMO
3
4
Software defined networking
Network function virtualization
4
Current Trends (1/2)
A call for a new generation of mobile networks seems necessary due to
increased penetration of smart devices,
better hardware,
better user interface design,
compelling services (e.g., video streaming) and
desire for anywhere, anytime, anything high-speed connectivity.
More than 70% of this data consumption occurs indoors
The signaling traffic is increasing 50% faster than data traffic
OTT1 players provide services and apps with much higher agility, some of which
compete directly with core operator services (e.g., voice, SMS, and MMS).
1
Over-the-top
5
Current Trends (2/2)
The battery life of devices and a seamless experience across multiple devices (or a device
ecosystem) is also an important issue in end-users’ perspective
IoT1 brings a 3rd dimension, i.e. anything, to the 2D connectivity plane (i.e., anytime &
anywhere)
Smart wearable devices, smart home appliances, smart cars and cognitive mobile objects
promise a hyper-connected smart world
On the other hand, many of the envisaged applications impose requirements, such as, very low
latency and high reliability, that are not easily supported by current networks
To cope with such evolving demands, operators are deploying more localized capacity, in the
form of small cells to improve capacity and traffic offloading to fixed networks, e.g, via WiFi
To optimize network usage for better QoE in a fair manner, mobile networks are equipped with
DPI2, caching & transcoding, all of which could increase CAPEX3 & OPEX4 significantly.
With the increasing complexity and associated costs, several concepts and technologies that
have proved useful to the IT sector are proposed such as NFV & SDN
1
Internet-of-things
2 Deep packet inspection
3
4
Capital expenditure
Operational expenditure
6
5G Challenges &
Design Parameters
Challenge #1: Higher Capacity
Spectrum
Massive/3D MIMO
Enabler
New Air-Interface
Capacity
Use high frequencies and
other spectrum options
(e.g., pooling, aggregation)
Design new air interface, new multiple
access scheme and L1/L2 techniques
that
can be optimized for high frequencies,
latency and massive connectivity
All optical networks
Small cells
Optical transmission and switching
wherever possible.
Local offload
Bring communicating endpoints closer
together.
C/U plane split
3rd party/user
deployment models
Design principle
in 5G context
Challenge
x 1000
> 70% indoor
Address coverage & capacity separately
Minimize functionalities performed by
access points
8
Challenge #2: Higher Data-Rate
Spectrum
Massive/3D MIMO
Enabler
New Air-Interface
Data-rate
x 10-100
Use high frequencies and
other spectrum options
(e.g., pooling, aggregation)
Design new air interface, new multiple
access scheme and L1/L2 techniques
that
can be optimized for high frequencies,
latency and massive connectivity
All optical networks
Small cells
Optical transmission and switching
wherever possible.
Local offload
Bring communicating endpoints closer
together.
C/U plane split
Design principle
in 5G context
Challenge
Address coverage & capacity separately
9
Challenge #3: E2E Latency
E2E Latency
< 5 ms
New Air-Interface
Design new air interface, new multiple
access scheme and L1/L2 techniques
that
can be optimized for high frequencies,
latency and massive connectivity
All optical networks
Optical transmission and switching
wherever possible.
Local offload
Caching/prefetching/CDN
Design principle
in 5G context
Enabler
Challenge
Bring communicating endpoints closer
together.
10
Challenge #4: Massive No. of Connections
Challenge
Massive No. of
Connections
x 10-100
Use high frequencies and
other spectrum options
(e.g., pooling, aggregation)
Enabler
New Air-Interface
Local offload
NFV/SDN/Cloud
Energy Efficient
Hardware
Energy Management
Techniques
Design new air interface, new multiple
access scheme and L1/L2 techniques
that
can be optimized for high frequencies,
latency and massive connectivity
Bring communicating endpoints closer
together.
Design principle
in 5G context
Spectrum
Minimize number of network layers
and pool resources as much as
possible
Maximize energy efficiency across all
network entities
11
Challenge #5: Cost
Challenge
Local offload
C/U plane split
Cost
Sustainable
Bring communicating endpoints closer
together.
Address coverage & capacity separately
Enabler
3rd party/user
deployment models
Simple access points
Energy Efficient
Hardware
Energy Management
Techniques
SON
Traffic Management
Big data-driven NI
Minimize number of network layers
and pool resources as much as
possible
Minimize functionalities performed by
access points
Minimize functionalities performed by
access points.
Design principle
in 5G context
NFV/SDN/Cloud
Maximize energy efficiency across all
network entities
Use an intelligent agent to manage
QoE, routing, mobility and resource
allocation. Redesign NAS protocols,
services and service complexity.
12
Challenge #6: QoE
Enabler
Caching/prefetching/CDN
Energy Management
Techniques
Traffic Management
Big data-driven NI
QoE
Consistent
Bring communicating endpoints closer
together.
Use an intelligent agent to manage
QoE, routing, mobility and resource
allocation. Redesign NAS protocols,
services and service complexity.
Design principle
in 5G context
Challenge
13
The Vision for
5G Architecture
5G Architecture Vision (A Candidate)
Operator
Services
API1
C-plane path
U-plane path
Radio-access link
Backhaul link (wired)
Fronthaul link (wireless)
OTT2
Data-Driven NI
NFV-enabled NW7 Cloud
• Extraction of actionable insights
from big data
• Orchestration of required services
and functionalities (e.g., traffic
optimization, context-aware QoE8
provisioning, caching, ...)
Internet
NI3
Lean Protocol Stack
CPE4
• Authentication
• Mobility management
• Radio resource control
• NAS11-AS12 integration
UPE5
• Gateway
• U-plane mobility anchor
• OTA10 security provisioning
Resource Pooling
RRU1
• L1/L2 functions
• Low CF14 with NOMA15
• High CF with mMIMO16
RRU2
• L1/L2 functions
• High CF with mMIMO for capacity
• Connectionless, contentionbased access with new
waveforms for MTC13
asynchronous access
Macro Cell
Small Cell
• L1/L2 functions
• Super high CF &
unlicensed spectrum
for local capacity
• Dual Connectivity
• Switched on, on
• Independent C/U
demand
plane mobility
Area Controller
C/U Plane Split
• Nesting & Relaying to support low-power
devices, nomadic cells & group mobility
1
5
9
13
2
6
10
14
Application programming interface
Over-the-top player
3 Network intelligence
4 C-plane entity
U-plane entity
Device-to-device
7Network
8 Quality of experience
Anything as a service
Over the air
11 Non-Access Stratum
12 Access Stratum
• D2D6 under
Network Control
Machine-type communication
Carrier frequency
15 Non-orthogonal multiple-access
16 Massive MIMO
15
5G Architecture Vision
Logical Network Layers (RN1 vs. NC2)
The proposed architecture consists of 2 logical sub-networks, RN & NC.
At RN, different types of BSs and RRUs perform only Layers 1 & 2 functionalities for
complexity reduction, thereby making dense deployments affordable to deploy and operate
At NC, higher layer functionalities are performed through UPE & CPE, which allows for resource
pooling, reducing over-provisioning & under-utilization of network resources
The NC is a logical entity with physical realization that can be tailored to meet specific
needs.
1
2
Radio Network
Network Cloud
16
5G Architecture Vision
NI responsible for:
•
Data collection
•
Analysis and control over
network entities
Logical Network Layers (RN vs. NC)
Realization of a 5G NC
Data Centre 4
NFV-enabled NW Cloud
Data Centre 3
Data Centre 1
RRU3
C-plane path
U-plane path
Backhaul link (wired)
Fronthaul link (wireless)
Data Centre 2
Small Cell
RRU1
CPE
Control-plane entity
UPE
User-plane entity
Macro Cell
17
5G Architecture Vision
RN Layer
Impact of New AirInterface on Data-rate
Achievable Rate
𝑅=
𝐵𝑖
𝑖
• More spectrum (esp. at higher
freq.)
• Carrier aggregation
• Full-duplex radio
• Cognitive Radio
• Dynamic spectrum sharing
• NOMA transmission
log 1 +
𝑗
𝑘 𝑃𝑗,𝑘
𝑃interference + 𝑁0
• Coordinated scheduling
• 3D Beamforming
• Interference
suppression/utilization
• Higher order Modulation
• Massive MIMO
• Multi-cell cooperation
18
5G Architecture Vision
NFV & SDN
Exploitation of NFV & SDN concepts allows for the CPE & UPE functions to be deployed
quickly as well as network orchestration and scaling to be performed on demand
For example in the incidence of a “flash-crowd”, additional resources can be borrowed from
other data-centers within the cloud.
Spare cloud resources can be lent out when demand is low, whereas additional resources can
be rented through IaaS1 business models during peak hours.
Furthermore, a broad range of “as a service” business models based on providing specific
network functionalities as a service (i.e., XaaS2) could also be envisioned
The complete or specific parts of the network could be provided to customers (e.g.,
network operators, OTT players, enterprises) that have specific requirements, e.g., in a
“MNaaS3”, “RNaaS4”, “UPEaaS5”, “CPEaaS6”, “NIaaS7”, etc. model
Besides the XaaS business models that could be facilitated, the flexibility of a cloud, coupled
with SDN and NFV technologies, also makes the network easier, faster, and cheaper to
deploy and manage
1
4
2
5
infrastructure as a service
Anything as a service
3 Mobile Network as a service
Radio Network as a service
UPE as a service
6 CPE as a service
7
NI as a service
19
5G Architecture Vision
Lean Protocol Stack
Virtualization converts the interfaces between network functionalities into interfaces
between software units
As one indication of NFV, the AS & NAS protocols within the C-plane can be integrated
As another implication, the connection establishment procedure can be significantly simplified
by requiring a handshake only between the peer entities of a single protocol.
As a 3rd indication, the Bearer-based QoS management could also be replaced by simple IP
marking
On the U-plane side, however, virtualization is more difficult due to the sheer volume of
data handled on this plane, but mostly feasible by 2020 via advancements in technology
The U-plane stack can potentially become leaner by removing ciphering as this is
implemented by TLSoIP1.
Generally E2E security solutions are more efficient than encryption of segments over a path,
but make some intelligent content-centric mechanisms (e.g., DPI2 & Caching) dysfunctional.
1
2
Transport layer security over IP
Deep packet inspection
20
5G Architecture Vision
C/U Planes Split
Coverage & capacity are provided separately in the proposed architecture
Macro-cells are responsible for the provisioning of coverage on the licensed lower
frequencies , integrating NOMA1 and SIC2 for capacity enhancement
Small cells offers localized capacity on licensed & unlicensed bands on both low
and high frequencies, both at indoors and as outdoor hotspots, using mMIMO
To save energy, small cells can dynamically be switched on and off, inspired by the
highly variable traffic loads served by small cells
Separation of coverage & capacity will enable independent mobility of C & U planes which
in turn requires simultaneous connectivity support to multiple BSs at the UE side.
1
2
Non-orthogonal multiple access
Successive (Serial) interference cancelation
21
5G Architecture Vision
Support for Multiple Devices & Group
Mobility via Relaying & Nesting
To support group mobility, e.g. in moving vehicles, all transmissions within the group are
aggregated at one or more entities (that have higher capabilities and resources) and relayed
to the NC via wireless backhaul
On the other hand, small wearable devices (with limited resource and battery) connect to
the network non-transparently, via one or more devices with higher resource/capability
In this way, huge number of devices with a diverse range of capabilities can be
connected to the network in a scalable and efficient manner
22
5G Architecture Vision
Data-Driven NI
The 5G candidate architecture could allow the NC to collect various data types, i.e., usercentric, NW-centric and context-centric data
The NC then is able to use intelligent algorithms for efficient resource management,
mobility management, local offload decisions (e.g. via D2D), QoE management, traffic
routing, etc.
Moreover, providing APIs to the NC can be used for commercial purposes such as
selling knowledge on “NCaaS1 ” to OTT players to optimize their QoS to the end users
1
Network conditions as a service
23
Issues
Current NW
Intermediate step
Legacy
CN
Legacy
RAN
MME
S/P-GW
CN Cloud
Migration via
appropriate
interfaces
between
legacy and
new clouds
Future NW
Operator
Services
API
OTT
NFV-enabled NW Cloud
BBU
RAN Cloud
Internet
NI
CPE
UPE
• Traffic
management
• OTA security
• Seamless
mobility
• Device/ID management
• Device discovery
• Finding the right
balance between
network cloudification/
distribution
RRU1
Minimum
functionalities?
• Deployment & spectrum
• Discovery & measurement
• Network synchronization
C-plane path
U-plane path
Radio-access link
Backhaul link (wired)
Fronthaul link (wireless)
RRU2
Support different
radio interfaces
Macro Cell
Small Cell
Support for V2X
communication
Support cognitive
mobile objects
24
5G Architecture Vision
Modeling Parameters
Simulation parameter
Area under simulation
# of Macrocells
Value
750m x750m (dense urban area)
7
Inter-cite distance
500m
# of sectors per cell/users per sector
3 -- 30
Users' speed
3 Km/h
Operating freq./BW for macro-cells
# of transmit antennas at macro-cells
(TX power/antenna gain) for macro-cells
# of small cells per sector
Operating freq./BW for small-cells
# of TX antennas per small cell
(TX power/antenna gain) for small-cells
# of antennas at user terminal side
2 GHz-- 20 MHz
2
(49 dBm, 14 dBi)
12
20 GHz -- 1 GHz
64
(30 dBm, 5dBi)
4
25
5G Architecture Vision
Modeling Results
Throughput vs. time
(Source: IEEE)
Classified UE ratio (in terms of data-rate)
vs. time (Source: IEEE)
26
5G Architecture Vision
Network-Controlled D2D
D2D techniques aiming at increasing the coverage & capacity have been existing for decades
e.g., in ad hoc mode in IEEE 802.11, but have never become mainstream
The main purpose of considering them in the context of 5G is to clarify whether & to what
extent D2D transfers can be integrated into 5G networks
The adoption of D2D in 5G networks is driven by 4 main use-cases:
1. Safety applications & disaster scenarios
2. Novel commercial ProSe1 scenarios
3. Network traffic offloading
4. Industrial automation & M2M2 communication
2 important questions: 1) why should we expect D2D to be successfully ushered into cellular
networks? 2) Are small-cells & D2D alternative or complementary to each other?
Here, in the context of 5G, we consider a network-controlled, in-band and underlay D2D
1
2
Proximity service
Machine to machine
27
5G Architecture Vision
Network-Controlled D2D
Proposed System
Model
AC1
User #1
eNB
User #2
1- Status report:
Propagation is good
2- Policy: Use I2D
3- Content request:
I need content c1
4- Indication:
Use I2D for this user
6- Policy:
From now on, D2D
can be used for c1
8- Indication:
Use D2D for this user
1
Area Controller
5- Resource decision:
Use resource r1 in I2D mode
7- Content request:
I need content c1
9- Resource decision:
Use resource r2 in D2D mode
28
5G Architecture Vision
Network-Controlled D2D
Scheduling Through MMPF1
No
START
Calculate the priority score for user 𝑢(𝑘)
for all k:
𝐴𝑐ℎ𝑖𝑒𝑣𝑎𝑏𝑙𝑒 𝑟𝑎𝑡𝑒 𝑜𝑓 𝑢 (𝑘)
𝑆𝑢(𝑘) =
𝑑𝑎𝑡𝑎 𝑑𝑜𝑤𝑛𝑙𝑜𝑎𝑑𝑒𝑑 𝑏𝑦 𝑢(𝑘)
Result
𝑢(𝑘) has the
highest score
Content needed by
𝑢(𝑘) is available via
D2D?
Yes
Schedule RB “𝑟𝑗 ” for transferring
𝑏 → 𝑢(𝑘) via I2D where 𝑏 is a
macro/micro BS covering 𝑢(𝑘)
with the best RSSI2
Schedule RB “𝑟𝑖 ” for transferring 𝑢(𝑘) → 𝑢(𝑘)
Where 𝑢(𝑘) is the closest user to 𝑢(𝑘)
END
1
2
Multi-modal proportional fairness
Received signal strength indicator
29
5G Architecture Vision
Network-Controlled D2D
Modeling Parameters
Simulation parameter
Value
Service Area (Km2)
# of Macrocells
# of Microcells
# of macro-eNodeBs
Macro eNB inter-sire distance (m)
# of users in the area
12.34
57
228
19
500
3420
# of users uniformly distributed
within 50 meters of each micro-eNB
(power/antenna height) for macroeNB
(power/antenna height) for microeNB
(power/antenna height) for UE
(Centre frequency/BW)
# PRBs per subframe
MIMO size
Peopagation loss for Macrocells
Peopagation loss for Microcells
Peopagation loss for D2D
# of total content items
# of content categories
Total simulation time
max. allowable distance for D2D
Content Class
Software updates
Video
Viral
10
(43 dBm, 25m)
(30 dBm, 10m)
(23 dBm,1.5 m)
(2.6GHz, 10MHz)
50
2x2
13.5 + 20𝑙𝑜𝑔10 𝑓𝑐 + 39𝑙𝑜𝑔10 𝑑
22.7 + 26𝑙𝑜𝑔10 𝑓𝑐 + 36.7𝑙𝑜𝑔10 𝑑
27 + 20𝑙𝑜𝑔10 𝑓𝑐 + 22.7𝑙𝑜𝑔10 𝑑
21
3 (S/W updates, Video, Viral)
30 sec.
Variable between 10-100 m
# of content items Size (Mb) Deadline (sec.) Request Rate (Copies/ms)
10
10
1
12
3
3
4
1
1
0.1
0.1
50
30
5G Architecture Vision
Network-Controlled D2D
Modeling Results
Result 1: Amount of transferred data through each paradigm.
(Source: IEEE)
Result 2: Amount of transferred data for each paradigm when
the number of microcells is reduced by 50%. (Source: IEEE)
Result 3: Data transferred through each paradigm for different 31
content classes. (Source: IEEE)
5G Architecture Vision
Terminal-Centric IP
Mobility Management
One of the architecture pillars of mobile network is IP mobility management,
currently based on CENTRALIZED data-path management
The considerable amount of IP traffic growth on mobile terminals (66% every
year over 2012-17), calls for a suitable IP mobility architecture
In the legacy network architectures, a mobile terminal’s traffic is routed via a
centralized node in the core network (e.g. the PDN-GW in LTE architecture)
The architecture for the next generation of mobile networks needs to take into account the
distributed nature of IP routing via provision of an intelligent mobile data-path management
32
5G Architecture Vision
IP Anchor
Terminal-Centric IP Mobility Management
Actual data-path
Legacy vs. Optimum IP
Routing Architecture
Peer 2
Optimum data-path
Peer 3
Core
NW cloud
Peer 1
PDN-GW/S-GW
Internet Cloud
Access NW Cloud
Base Stations
User Terminal
33
5G Architecture Vision
IP Anchor
Terminal-Centric IP Mobility Management
Data-path
IP Mobility Distribution over
Location of IP Anchors
Peer 2
Peer 3
Core
NW cloud
RGW
PDN-GW/S-GW
Remote
Network/ISP
Peer 1
Internet Cloud
XGW1
XGW2
Access NW Cloud
Base Stations
34
5G Architecture Vision
IP Anchor
Terminal-Centric IP Mobility Management
Data-path
Per-flow Based IP Mobility
Management
Peer 3
Peer 2
Core
NW cloud
PDN-GW/S-GW
Peer 1
Internet Cloud
XGW1
XGW2
Access NW Cloud
Base Stations
Flow 1 using IP1
Flow 2 using IP2
Flow 3 using IP3
35
5G Architecture Vision
IP Anchor
Terminal-Centric IP Mobility Management
Data-path
IP Mobility Management Based on
Communication Layers
Peer 3
Peer 2
Using MPTCP
Using SIP
Peer 1
Internet Cloud
Using Access
Anchoring
XGW1
XGW2
Access NW Cloud
Base Stations
Flow 1 using IP1
Flow 1 using IP2
Flow 1 using IP3
36
5G Architecture Vision
Terminal-Centric IP Mobility Management
UE-Centric IP Mobility
Management
START
Mobility support
required?
No
No mobility solution chosen
Yes
Fixed IP required?
Yes
Go for core anchoring
No
L4+ solution
available?
Yes
Go for L4+ solution
No
Remote anchoring
available?
Yes
Go for RGW
No
Access anchoring
available?
Yes
Go for XGW
END
No
37
5G Architecture Vision
A New Proposition for
Control-Plane
Another bottleneck of today’s mobile network architectures is its complex & non-agile
control-plane
NFV (i.e., Cloudification) & SDN (i.e. programmability) based network architecture with
hierarchical network control capabilities are proposed to allow for different grades of
performance and complexity in offering CN services and provide service differentiation
NFV guarantees lower CAPEX1 & OPEX2 and creates the capabilities for MSPs to launch new
services in the time scales that an OTT3 player would do.
In an SDN-based architecture, complex CPFs4 are removed from forwarding elements and
placed behind a logically centralized controller, enabling control differentiation of different
flows
The hierarchy of controllers allow for locally optimized control decisions and acts as a new
dimension in service provisioning
1
2
Capital expenditure
Operational expenditure
3
Over-the-top
4 Control-plane
functions
38
5G Architecture Vision
A New proposition for
Control-Plane
A clarifying
example
Coherence time
@ 2 GHz
Control functionality:
Scheduling of a wireless
resource based on CQIs1
received from users
90 ms for 3 Km/h
1.1 ms for 250 Km/h
Coherence time
@ 5GHz
36 ms for 3 Km/h
0.43 ms for 250 Km/h
Considering 5-10 ms
one-way delay
of backhaul links
The CQIs become outdated for high-mobility users when
using centralized control for scheduling
An interworking set of hierarchical controllers is required
1
Channel quality indicators
39
5G Architecture Vision
CA: Control Application
NF: Network Function
Control Interface
A New Proposition for Control-Plane
Hierarchical Control in 4 Layers
CA
•
•
•
•
•
NF
CA
L4: Network Controller
NF
CA
E2E QoS provisioning
Application-aware route establishment
Service chaining
Mobility management
Policy & charging
NF
CA
L3: RAN Controller
NF
•
•
C-RRM
HetNet management
NF
CA
CA
L2: BS Controller
•
•
CA
Wireless resource
management & scheduling
Adaptive PHY-layer
packet creation
Higher Hierarchical Layer
NF
CA
L1: UE Controller
•
D2X discovery &
connection control
40
5G Architecture Vision
A New Proposition for
Control-Plane
CMaaS1
CMaaS: In the proposed hierarchical architecture, the MSPs have the option to perform
handoff and route management together and deploy different CAs for different flows/users
CMaaS may be offered by MSPs in one of the 3 forms:
1- For MSP’s services: The flows that have more stringent delay and packet-loss requirements
will be given a CM-CPF2 with a higher operational cost; other flows will have cheaper CPFs
2- A paying user will always have a higher-grade CM-CPF regardless of its flow-type
3- For OTT services, only the paying OTTs will have a higher grade CM-CPF
Therefore CMaas allows MSPs to adaptively adjust their operational costs with respect to
flow/user type and hence introducing new/agile revenue paths
1
2
Connectivity management as a service
Connectivity management control-plane function
41
Conclusions
Conclusions
The primary objective of 5G architecture is to bring a high-capacity, agile, low-cost solution
through a number of innovations in RAN, Core and Transport sub-networks
Ultra-dense small cell deployment on licensed/unlicensed spectrum and under split C/Uplane architecture can tackle capacity concerns
NFV & DSN offer flexible network deployment & operation with integrated AS & NAS
For QoE & network planning purposes, the network data can intelligently be facilitated for
optimized use of network resources
D2D can profitably be integrated within cellular networks and has a complementary role
alongside the small cells, particularly when the I2D infrastructure deployment is reduced
Due to the accelerating increase of mobile access in the overall IP transport, the centralized
mobility approach should be replaced by a terminal-centric distributed approach
An all-SDN network architecture with hierarchical network capabilities can be used to allow
for different grades of performance & complexity and hence offer 5G service differentiation
43
Contact:
[email protected]