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5G Grand Research Challenges
Friday, 17 November 2017 1
Ning Wang
5G Innovation Centre
5G is…
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CONNECTIVITY + INTELLIGENCE
Data to information/knowledge transformation
Blurring boundaries between real and cyber worlds
Connected Devices of small and large sizes and capabilities(robots, cars, sensors, actuators, smart phones ………. driverless cars)
Automation
5G – A Special Generation
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• Previous “G”s: different versions of dummy bit pipe with
evolved bandwidth capacities in transmitting data
• Key features of 5G networks
Network slicing: simultaneously support a wide range of
services with specific requirements
Enhanced mobile broadband
Ultra-reliability and low latency
Massive connectivity
Softwarisation and Virtualisation
Network resources
Network functions
Edge computing
Computing vs. Communication in 5G
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Computing
5G is a complex ecosystem with cooperative computing and
communication operations
Communication
OTT 5G Applications & Services
Service assurancesupport
Network resourceefficiency
5G network resources (radio, bandwidth, storage, CPU…)
Computing for Communication: In-time computing (e.g. signal processing) to boost data transmission rate
Communication for computing: In-time delivery of data across distributed computing elements (edge computing) to be processed
Context-aware Network Operations in 5G
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5G Network Architecture
Virtual network functions (VNFs)
Contextdriving
Contextharvesting
User context
Device context
Network context
Content context
• Networking protocol design with enhanced or bounded performance (under given conditions)
• Addressing fundamental limitations of existing TCP/IP paradigm
• NFV-based network softwarisation/slicing
• Provisioning of virtual network functions (VNFs) for handling different requirements
• Context data harvestingand profiling
• Lightweight online userQoE inference
• Building knowledge to control network functions…
Tuesday, 19 September 2017
Virtual network functions (VNFs)
…
Statistics on:
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• Network traffic patterns
• User mobility patterns
• Mobile content consumption patterns
Examples on Statistics
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Learning Traffic Patterns (Network load)
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Profile 0
Profile 1
Peak-time weekday
Off-Peak-time Peak-time weekend
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Max
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Profile 0
Profile 1
Peak-time weekday
Off-Peak-time Peak-time weekend
(a)
(b)
• 7-day traffic load
dynamicity in a
typical operational
network (Not 5G but
still reflects how
users use the
network on daily
basis!)
• Knowledge can be
derived from traffic
statistics for
optimising network
efficiency
• Use case – putting
network elements to
sleep mode for
energy efficiency
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Learning Traffic Patterns (Application Distribution)
• Network traffic predictions – e.g. Cisco’s Visual Network Index (VNI) tool https://www.cisco.com/c/en/us/solutions/service-provider/visual-networking-index-vni/index.html
• 5G resource management for network slicing
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User Mobility Awareness and Prediction
• Reduction of signalling messages when a user enters a new tracking area
(TA) – up to 80% reduction depending on TA and TA List configurations
• Proactive content downloading against anticipated signal strength
deterioration for on the move content consumers
• Resource discovery in Device-to-device (D2D) communications
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• Motivation: To obtain accurate knowledge about content
consumption pattern both at the regional level and individual
level.
• Application scenarios:
• Content can be cached at the local data centres to be served to
a large number of users with common content interests
• Content can be preloaded at intelligent home gateways even
before the users start to consume
• Context-aware transmission mode switching: unicast
multicast upon detection of increased content popularity
• Research objective
• To design fast and accurate machine learning algorithms in
order to predict crowd/individual-based content consumption
behaviours
Content Profiling and Prediction
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• Scheme overview
• Continuous window-based training according to historical data and
make predictions on near future events
• Key factors to be considered: similarity between users in content
consumption, content popularity dynamicity, correlation between
different dimensions of dynamicity on the content side and the user
side
• Technique under patent consideration
• Key results
• The average of RMSE for all popularity predictions is 0.047
(Normalized by total number of actual number of requests)
• Relative improvement of more than 50% in comparison with three
well-known SoA solutions (Szabo-Huberman, Multivariate Linear
Model and its extension)
Content Profiling and Popularity Prediction
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• Content-level popularity: heavy-tail – top 10% popular content attracted
90+% requests
• The majority of today’s video applications use DASH (Dynamic Adaptive
Streaming over HTTP) in which the whole content is divided into fixed-length
chunks/segments
• Caching Video-on-Demand (VoD) video segments at the 5G mobile edge
Content Popularity and Content Chunk Popularity
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Towards Emerging Augmented / Virtual Reality Applications
From 3 Degree of Freedom (3DoF) to 6DoF
User behaviour patterns in AR/VR applications are much more complex than traditional media – generation of explosive context data on user behaviours
Public
Internet
VR content
cloud
5G mobile
edge
5G mobile
edge
Live streaming content
Embedded 5G network functions
• Prefetching
• Caching
• Transcoding
• Rendering
• User behaviour capturing
• Content popularity analysis
• …
Assuring user experiences
• Low start-up delay
• Guaranteed content
quality (resolution)
• No playback stalling
• Minimum streaming
latency
• …
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Capturing User Behaviours in Immersive Applications
• Selective transmission of content
• In some 360-degree video the whole picture is partitioned into multiple “tiles”
• Only the tiles covering the actual Field of View (FoV) is useful
• Applicable content manipulation techniques
• Caching: cache popular tiles at the mobile edge based on crowd-sourced
knowledge
• Prefetching: Online prediction of the use behaviour and prefetch follow-up tiles
that are most likely to cover the FoV in the next moment
• Network intelligence
• Learning and prediction algorithms
• Enabling 5G technology
• Multi-access edge (Fog) computing
FoV
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• 5G – an ecosystem that can generate rich data
• Highlights on key research challenges
• Data harvesting mechanisms
• Multi-dimensional correlation of context-data produced by
different players in 5G
• Architectures for enabling “in-network” data analytics
• Hierarchical vs. peer-to-peer computing
• Offline/online operations
• Offline: long-term construction of network intelligence
• Online: real-time learning and instantaneous decision-making in
changing network behaviours – Possible in practice?
• Enabling network autonomics
Summary
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Thank You!