In pursuit of evolvable, sustainable, and submissive networksFrom Cognitive Radios to Cognitive...
Transcript of In pursuit of evolvable, sustainable, and submissive networksFrom Cognitive Radios to Cognitive...
In pursuit of evolvable, sustainable, and submissive networks
Luiz DaSilva
Stokes Professor in TelecommunicationsTrinity College,University of DublinIreland
About CTVR….
• CTVR is a national centre for telecommunications research. It is head‐quartered in Trinity College but based in six institutions around Ireland
• Our mission is to carry out industry‐informed research in telecommunications to the highest of standards
• We work both in the optical and wireless domains
Wireless & Optical
Technologies
OpticalTechnologies
Wireless Technologies
Wireless & Optical Networks
architecture
architecture
architecturearchite
cture
Other CSETs & SRCs
InternationalCollaborations
Visiting positions
FP7, Future InternetProgrammes & Others Industry
evolvable:Elegant paths to future. Adaptive and flexible where it should be. Understanding of points of in‐flexibility.
submissive:Not biased towards any one model of ownership. No un‐necessary rule‐setting or resource labelling.
sustainable:Persistent awareness of resource constraints e.g. man‐power, energy, bandwidth, processing power, space, storage etc.
interests
transient ownership of resources
ability to learn
decision making under partial information
fundamental principles that will allow the wireless network of the future to evolve into new architectures characterized by increasing autonomy and ubiquity of wireless services
Sustainable, evolvable, submissive: cognitive networks
Cognitive networks – perceive their environment, then decide, learn and act from the results, with network‐wide goals
Cognitiveelement
element goal
cognitive element
element goal
end-to-end goalend-to-end goalend-to-end goal
cognitive element
element goal
cognitiveelement
element goal(s)cognitiveelement
element goal
cognitiveelement
element goal
CSL
RequirementsLayer
CognitiveProcess
SoftwareAdaptableNetworkconfigurable
elementconfigurable
element
configurable element
networkstatussensor
networkstatussensor
cognitive element
element goal(s)
elementstatus
transfer
SAN API
R. W. Thomas, D. H. Friend, L. A. DaSilva, and A. B. MacKenzie, “Cognitive Networks: Adaptation and Learning to Achieve End‐to‐end Performance Objectives,” IEEE Communications Magazine, Dec. 2006
Organization of wireless access networks, including spectrum management, and distributed adaptations by cognitive radios to meet end‐to‐end objectives
Interaction between wireless access and wired networks, including assignment of users to access points, energy efficiency, flexibility to user mobility
Tools
From cognitive radios to cognitive networks
Distributed coordination for heterogeneous networks(collaboration with UCC)
• Spectrum etiquette for distributed channel selection
• Effects of imperfect network information on radio adaptations
• Small / large cell coexistence and resource management
• Analytical (game theory) + experimental
• Sensing and sharing in wireless access and multi‐hop networks
• Heterogeneous handover optimisation• Transport protocols to handle mismatch at wireless/optical boundary
• Analytical (optimisation) + experimental
From Cognitive Radios to Cognitive Networks
Resource management for cognitive radios acting in a network• local adaptations (power, channel, …)• local goals (max SINR, min BER, …)• perfect information (can observe channel, neighbors’ actions)
Current practice
Local adaptations to meet end‐to‐end or network‐wide goals (throughput, network connectivity, …)
Information available to adaptive radios is limited, due to cost of propagating network state information or inaccuracyin observing one’s local environment
Extensions
Coordination for heterogeneous and multi-hop networks
• Distributed spectrum sharing for multi‐hop topologies and HetNets (relays, coexistence between small and large cells)
• Adaptations: channel selection, transmit power
• Goals: network‐wide spectrum efficiency, fairness, network connectivity, coverage
• Cooperative game theory, coalition formation
Types of coalition in equilibrium as a function of link range
Z. Khan, S. Glisic, L. A. DaSilva, and J. Lehtomaki, “Modeling the Dynamics of Coalition Formation Games for Cooperative Spectrum Sharing in an Interference Channel,” IEEE Trans. on Computational Intelligence and AI in Games, 2010
J. E. Suris, L. A. DaSilva, Z. Han, A. B. MacKenzie, and R. S. Komali, “Asymptotic Optimality for Distributed Spectrum Sharing Using Bargaining Solutions,” IEEE Trans. on Wireless Communications, Oct. 2009
Imperfect monitoring
• Impact of incomplete or erroneous information about channel or network conditions or neighbor activity on effectiveness of radio adaptations
• Framework is games of imperfect public/private monitoring
Price of ignorance in topology control adaptations upon departure of one radio
R. S. Komali, R. W. Thomas, L. A. DaSilva, and A. B. MacKenzie, “The Price of Ignorance: Distributed Topology Control in Cognitive Networks,” IEEE Trans. on Wireless Communications, April 2010
Utility ftn: imperfect public monitoring
π is the expected utility
p is the public signal
Need to model the distribution of p, parameterized by the action vector a
The potential for learning
• Learning is a part of the cognitive cycle: but when it is beneficial to apply machine learning ?
• Relationship between effectiveness of learning and the amount of pattern that can be observed: Kolmogorov complexity
• Application to frequency‐agile radios
• Evaluation using spectrum measurements at CTVR and RWTH Aachen
Probability of successfully selecting a channel, as a function of complexity in channel use patterns and primary user activity: ISM (top); GSM900 (bottom)
I. Macaluso, D. Finn, B. Ozgul, and L. DaSilva, “Deciding When to Learn: Kolmogorov Complexity and Dynamic Channel Selection,” under review, 2011.
Bringing DSA to LTE/LTE+
J. Deaton, R. Irwin, and L. DaSilva, “The Effects of a Dynamic Spectrum Access Overlay in LTE+ Networks,” IEEE DySPAN 2011.
HSS
PDG(SGW+PGW)
Internet
MME
UE
E-UTRANeNB
eNB
eNB
eNB
eNB
eNB
eNB
GDB
cUE
SAS
cRAN
S1-U
S1-MME
S1-U
S1-MMES11
Diameter (RFC 3588)
eNB: Evolved Node BE-UTRAN: Evolved Universal Terrestrial Radio AccessHSS: Home Subscriber ServerMME: Mobility Management EntityPDG: Packet Data GatewayPGW: Packet GatewaySGW: Signaling Gateway
LTE Network Elements
cBS: cognitive Base StationcRAN: cognitive Radio Access NetworkcUE: cognitive User EquipmentGDB: Geolocation Data BaseSAS: Spectrum Accountability Server
SA Network Elements
cBS
cBScBS
cBS
cBS
cBS
cBS
FRIDAY, 1:30 PM
Special Issue
Cognitive Radio for LTE Advanced and Beyond
• Taking a broad view of cognitive radio that includes, but is not limited to, DSA
• Letter from the editors / white paper included with CFP
• Deadline: 30 June 20011
FP7 – CREW
Cognitive Radio Experimentation World(2010‐2015)
MODE 1
CREW PORTAL
TCD
TUD
IBBT/IMEC
TUB
CREW PORTAL
MODE 2
RELOCATENODES &
CREATE NEWCOMBINATIONS
CREW PORTAL
MODE 3
STEP 1
STEP 2
REPLAY ONEBEHAVIOURIN OTHER TESTBED
CREW federation modes
CREW open call
• 20% of the budget for the CREW project is set aside to fund new partners who wish to perform experiments using the federated testbeds
http://www.crew-project.eu/
FP7 – COGEU
COGnitive radio systems for efficient sharing of TV white spaces in EUropean context(2010‐2012)
COG-EU objectives
• To design, implement and demonstrate enabling technologies based on cognitive radio to support mobile\portable applications over TVWS with protection of incumbent systems (DVB, PMSE‐wireless microphones)
• To investigate innovative business models and spectrum policies for TVWS exploitation based on secondary market regimes, to increase spectrum utilization enabling innovative wireless services
• COGEU aims to inform EU policy in relation to the enabling of efficient spectrum sharing and usage over TVWS at the European level.
Architecture for using TVWS instantiates different business models
On the interwebs
About CTVR… www.ctvr.ie
On email… [email protected]
About Luiz… luizdasilva.wordpress.com