self-organised radio and core networks: achieving end-to
TRANSCRIPT
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Self-Organised Radio and Core Networks: Achieving end-to-end optimal resource utilisation
Muhammad Ali Imran
• Professor of Wireless Communication Systems
• Dean – University of Glasgow UESTC
• Head – Communications, Sensing and Imaging Research Group
• Scotland 5G Center Urban Testbed Lead
Radio Access Network Techniques for 6G
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Active International CollaborationsUSA, Middle East, Europe, Italy, France, Holland, China, Brazil, Finland, Nigeria, Pakistan, India, Kazakhstan, Thailand, Singapore, South Africa,
From 5 to 120+ in 5 yearsMultidisciplinary in engineeringDiverse talent from 22 countries
Outputs300+ outcomes (papers, patents, etc.) every year
Funded GrantsInvolved in £25M+ currently funded portfolio
Networking and Self-Organisation
AI, Urban Big Data and
Computing
Physical Layer Communication and RADAR
Hardware and Circuits for
Communication
6G Team @ University of Glasgow
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Outline …
• Meeting 6G challenges/Key Performance Targets • End-to-end RAN, Backhaul and Core
• Existing versus New Spectrum (THz?)• Self-organization becoming even more pertinent as spectrum landscape
widens
• Estimating the holistic impact• Energy/Spectrum/Utilization Efficiency
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RAN and Backhaul –both can be wireless
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https://theconversation.com/wireless-spectrum-is-for-sale-but-what-is-it-11794
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Diversity in 6G
A new dimension to be added:• URLLC
• mMTC
• eMBB
• Ultra Intelligence
• Sustainability and Equitability
• ?
Move further closer to the vertices
6G
Diverse users and QoS
Diverse applications with diverse and challenging requirements
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PtP microwave
Optical fibre
mmWave/THz
xDSL
PtmP microwave/THz
Macro-cells
Small cells
Relays
RRH
HetNet Last mile BH/FHDiverse users
and QoS
BBU
6G End-to-end view
RAN technology and
spectrum choices:
Conventional
sub 6GHz
mm-Wave
THz
….
E 2 E choices determine overall performance
mmWave/THz
xDSL
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Macro-cells
Small cells
Relays
RRH
HetNetDiverse users
and QoS
User centric cell association
Mutifarious
Backhaul
options
Determine Rx = Sx + CREOx
… and decide cell association using modified rank listRSC1≥RSC2>RSC3≥RM
Let Sx be the physical channel quality (e.g. RSRP)
Conventional cell association:Rx=Sx
RM>RSC1≥RSC2>RSC3
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Macro-cells
Small cells
Relays
RRH
HetNetDiverse users
and QoS
Learning based dynamic CREO
Mutifarious
Backhaul
options
𝑹𝒔,𝒖 = 𝑺𝒔 +𝝎𝒊,𝒖 ∙ 𝜷𝒊,𝒔
i ={Throughput (Tput),
Latency (lat),
Energy efficiency (EE)}
𝝎𝒊,𝒖
𝜷𝒊,𝒔
User u’s weightage for ith KPI
AP ‘s’ strength for ith KPI
Composite CREO
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SON Mechanism
BH link status
Radio conditions
RAN load
Optimisation objectives
Dynamic CREOs
settings
β1
β2
βA
RRH/Small Cell 1
β1
β2
βA
RRH/Small Cell N
Dynamic Network
Dynamic CREOs
settings
Measured dynamic NETWORK KPI
Measured dynamic USERs’QoE
KPI= Key Performance IndicatorQoE= Quality of ExperienceCREO= Cell Range Expansion Offset
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SON – Q-learning
Actions are possible settings of CREOsSelected action is the one that gives the minimum cost
Ultra high cost if backhaul capacity constraint is exceeded,
else, cost is the QoE gap weighed by users’ preferences,
else, cost is the difference between backhaul capacity and corresponding constraint
Initialise Q
Choose action from Q
Perform action
Measure COST
Update Q
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User Centric Improvement
• Glasgow + Surrey + BT Collaboration:
M Jaber, M Imran, R Tafazolli and A Tukmanov
IEEE Transactions on Wireless Communications, 17(5), pp. 3095-3110
doi:10.1109/TWC.2018.2806456
IEEE Access, Vol.4, pp.2314-2330;
doi:10.1109/ACCESS.2016.2566958
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Exploring New Spectrum Opportunities
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THz Research areas
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Non-invasive Sensing Setup and
Data Collections
Multi-domain Features Extraction
Experimental Setup Samples
Transmission Response of
fruits
Classification Model
Feature Extraction & Selection
Identificification of TRR
Internal Morphology of Leaves
Selection of
Parameters
Developed a
classifification
Model
Training Data
70%
Testing Data
30%
Classification ResultsClassification Accuracy for leave-one-
observation-out cross-validation
Internal Morphology of Leaves using Terahertz (THz) Sensing at Cellular level
Day4
Day1 Day2
THz Sensing
THz Sensing
THz Sensing THz Sensing
Day3THz
in
THz
out
Loss in moisture content (MC) and other synthesis
at molecular level affects the freshness of leaves
with passage of days
Further loss
observed on
day3Production of air
cavity and some
pesticides
Synthesis (Carbohydrates
or protein)
Water Moisture cells
Pesticides Air Cavity
Sources, Waveguides and Antennas
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Managing Existing Spectrum Better
13Fig. Geographical areas and RF landscape for RF device identification and shared spectrum
management at University of Glasgow’s smart campus
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Spectrum landscape Glasgow testbed
Fig. Licensed and unlicensed RF assets at the University of Glasgow
Fig. RF scanning and spectrum and security management framework
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Dynamic spectrum portal …
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Spectru
m A
cqu
isition
and
Man
agemen
t Platfo
rm
Negotiations with Incumbent
Spectrum Holders
Trading & Acquisition of
Spectrum
Commissioning of Private or Pop up Radio Networks
Centrally Managed Database
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Block-chain for spectrum trading
Our Beep-trace Framework
Fair, reliable and real-time trading platform
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Conclusion Summary
• Self-organized networking to ensure end-to-end mapping of user requirements with wireless link capabilities can ensure better user experience on following KPIs• Throughput – around 20% more users satisfied;
• Latency – around 8% more users satisfied;
• Resilience – around 2% more users satisfied
• Energy efficiency – most energy efficient links selected
• This may be at the cost of sacrificing some global metrics
• Need to optimize conventional spectrum used for wireless comms
• Need to explore and efficiently use the new spectrum e.g. THz/VLC
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Thank [email protected]
Success is a group achievement …Sincere appreciation of contributions from colleagues, students and external supporters!
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