Smart-Radio-Technology-Enable Opportunistic Spectrum Access
description
Transcript of Smart-Radio-Technology-Enable Opportunistic Spectrum Access
NSF NeTS Workshop
Smart-Radio-Technology-Enable Opportunistic Spectrum Access
Smart-Radio-Technology-Enable Opportunistic Spectrum Access
Univeristy Of California Davis
PI: Xin Liu (CS)
Univeristy Of California Davis
PI: Xin Liu (CS)
2006@UCLA
Project Goals and ScopeProject Goals and Scope
What are the impacts and properties of the white space and how can we quantify them?
Q: one experiment shows 62% of white space in spectrum under 3GHz at a certain location. Is exploiting this white space equivalent to gaining 0.63*3GHz bandwidth?
A: It depends.
How should secondary users share the white space dynamically and efficiently?
To develop a framework and performance metrics to evaluate sharing mechanisms
To study new protocols and to identify the suitable solutions for different application scenarios.
What are the impacts and properties of the white space and how can we quantify them?
Q: one experiment shows 62% of white space in spectrum under 3GHz at a certain location. Is exploiting this white space equivalent to gaining 0.63*3GHz bandwidth?
A: It depends.
How should secondary users share the white space dynamically and efficiently?
To develop a framework and performance metrics to evaluate sharing mechanisms
To study new protocols and to identify the suitable solutions for different application scenarios.
Characterizing Spectrum-Agile Networks
Characterizing Spectrum-Agile Networks
A new metric, Equivalent Non-Opportunistic Bandwidth, to quantify Spatial diversity gain Statistical multiplexing gain
The effects of spectrum availability pattern, network topologies, and other factors are being studied
Inherent benefits of heterogeneity between primary and secondary users TV stations and WLAN devices
if we allow WLAN to operate in TV service contour when TV station is silent , statistical multiplexing gain
If not, we still have spatial diversity gain! Investigating analytical models to capture the spatial and temporal
characteristics of white space and their impact on spectrum-agile networks X. Liu and W. Wang, "On the Characteristics of Spectrum-Agile
Communication Networks", IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), Baltimore, MD, Nov. 8-11, 2005.
X. Liu, “Characterizing Spectrum-Agile Networks”, under submission.
A new metric, Equivalent Non-Opportunistic Bandwidth, to quantify Spatial diversity gain Statistical multiplexing gain
The effects of spectrum availability pattern, network topologies, and other factors are being studied
Inherent benefits of heterogeneity between primary and secondary users TV stations and WLAN devices
if we allow WLAN to operate in TV service contour when TV station is silent , statistical multiplexing gain
If not, we still have spatial diversity gain! Investigating analytical models to capture the spatial and temporal
characteristics of white space and their impact on spectrum-agile networks X. Liu and W. Wang, "On the Characteristics of Spectrum-Agile
Communication Networks", IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), Baltimore, MD, Nov. 8-11, 2005.
X. Liu, “Characterizing Spectrum-Agile Networks”, under submission.
Dynamic Spectrum SharingDynamic Spectrum Sharing
Two unique characteristics: location-dependency and time-variance Location-dependency: list-coloring Time-variance: allocation algorithms have to work under scenarios with limited
information exchange from neighbors due to time-variance Channel allocation formulated as list-coloring problem Algorithms proposed:
Optimal Solutions: Centralized brute force search, served as Benchmark Distributed Greedy: Assign channel one by one, maximize allocation for each
channel Distributed Fair: To achieve max-min fairness by taking the link degree and
channel degree into account Distributed Randomized: Balanced between utilization and fairness, smallest
complexity W. Wang, X. Liu, and Hong Xiao, "Exploring Opportunistic Spectrum
Availability in Wireless Communication Networks", IEEE VTC Fall 2005, Dallas, TX, September 25-28, 2005
Two unique characteristics: location-dependency and time-variance Location-dependency: list-coloring Time-variance: allocation algorithms have to work under scenarios with limited
information exchange from neighbors due to time-variance Channel allocation formulated as list-coloring problem Algorithms proposed:
Optimal Solutions: Centralized brute force search, served as Benchmark Distributed Greedy: Assign channel one by one, maximize allocation for each
channel Distributed Fair: To achieve max-min fairness by taking the link degree and
channel degree into account Distributed Randomized: Balanced between utilization and fairness, smallest
complexity W. Wang, X. Liu, and Hong Xiao, "Exploring Opportunistic Spectrum
Availability in Wireless Communication Networks", IEEE VTC Fall 2005, Dallas, TX, September 25-28, 2005
Traffic Information Uncertainty & Robust Resource Allocation
Traffic Information Uncertainty & Robust Resource Allocation
Accurate traffic information is hardly available Traffic varies over time and difficult to measure Dissemination of traffic information may incur delay and
overhead On the other hand, coarse estimation is possible
Source-destination pairs & range of the traffic demands Developed a routing and scheduling scheme that works well for a
range of traffic conditions Achieve the best worst-case performance
Extended to topology control – topology control must take into account traffic demand and be performed infrequently
To study uncertainty in Spectrum-Agile networks. W. Wang and X. Liu, “Robust routing-scheduling in multihop
wireless networks”, under submission
Accurate traffic information is hardly available Traffic varies over time and difficult to measure Dissemination of traffic information may incur delay and
overhead On the other hand, coarse estimation is possible
Source-destination pairs & range of the traffic demands Developed a routing and scheduling scheme that works well for a
range of traffic conditions Achieve the best worst-case performance
Extended to topology control – topology control must take into account traffic demand and be performed infrequently
To study uncertainty in Spectrum-Agile networks. W. Wang and X. Liu, “Robust routing-scheduling in multihop
wireless networks”, under submission
Current and Future Research EmphasisCurrent and Future Research Emphasis
To capture the spatial and temporal characteristics of white space and to quantify their impact on spectrum-agile networks
To develop centralized and decentralized algorithms with different degrees of information exchange among primary and secondary users
To consider fairness and power/interference constraints
To study the impact of dynamic spectrum utilization on QoS and to propose appropriate admission control schemes
To capture the spatial and temporal characteristics of white space and to quantify their impact on spectrum-agile networks
To develop centralized and decentralized algorithms with different degrees of information exchange among primary and secondary users
To consider fairness and power/interference constraints
To study the impact of dynamic spectrum utilization on QoS and to propose appropriate admission control schemes
Links to other projectsLinks to other projects Xin Liu (University of California, Davis) CAREER: Smart-Radio-Technology-Enabled Opportunistic
Spectrum Utilization Dirk Grunwald, Doug Sicker, John Black (University of Colorado), NeTS-ProWIN: Topology And
Routing With Steerable Antennas Uf Turelli, Kevin Ryan (Stevens Institute of Tech), Milind M. Buddhikot, Scott Miller (Lucent Bell Lab),
Dynamic Intelligent Management of Spectrum for Ubiquitous Mobile Network (DIMSUMnet) Kang G. Shin, University of Michigan, Efficient Wireless Spectrum Utilization with Adaptive Sensing
and Spectral Agility Qing Zhao, UC Davis, An Integrated Approach to Opportunistic Spectrum Access Randall Berry, Michael Honig and Rakesh Vohra, Northwestern University, Smart Markets for Smart
Radios Mario Gerla, Stefano Soatto, Michael Fitz, Giovanni Pau, UCLA, Emergency Ad Hoc Networking
Using Programmable Radios and Intelligent Swarms Saswati Sarkar, University of Pennsylvania, Dynamic Spectrum MAC with Multiparty Support in
Adhoc Networks Marwan Krunz, Shuguang Cui, University of Arizona Resource Management and Distributed
Protocols for Heterogeneous Cognitive-Radio Networks Dennis Roberson, Cindy Hood, Joe LoCicero, Don Ucci (Illionis Institute of Technology), Uf Tureli
(Stevens Institute of Technology) Wireless Interference and Characterization on Network Performance
Narayan Mandayam, Christopher Rose, Predrag Spasojevic, Roy Yates, WINLAB Rutgers University, Cognitive Radios for Open Access to Spectrum
Xin Liu (University of California, Davis) CAREER: Smart-Radio-Technology-Enabled Opportunistic Spectrum Utilization
Dirk Grunwald, Doug Sicker, John Black (University of Colorado), NeTS-ProWIN: Topology And Routing With Steerable Antennas
Uf Turelli, Kevin Ryan (Stevens Institute of Tech), Milind M. Buddhikot, Scott Miller (Lucent Bell Lab), Dynamic Intelligent Management of Spectrum for Ubiquitous Mobile Network (DIMSUMnet)
Kang G. Shin, University of Michigan, Efficient Wireless Spectrum Utilization with Adaptive Sensing and Spectral Agility
Qing Zhao, UC Davis, An Integrated Approach to Opportunistic Spectrum Access Randall Berry, Michael Honig and Rakesh Vohra, Northwestern University, Smart Markets for Smart
Radios Mario Gerla, Stefano Soatto, Michael Fitz, Giovanni Pau, UCLA, Emergency Ad Hoc Networking
Using Programmable Radios and Intelligent Swarms Saswati Sarkar, University of Pennsylvania, Dynamic Spectrum MAC with Multiparty Support in
Adhoc Networks Marwan Krunz, Shuguang Cui, University of Arizona Resource Management and Distributed
Protocols for Heterogeneous Cognitive-Radio Networks Dennis Roberson, Cindy Hood, Joe LoCicero, Don Ucci (Illionis Institute of Technology), Uf Tureli
(Stevens Institute of Technology) Wireless Interference and Characterization on Network Performance
Narayan Mandayam, Christopher Rose, Predrag Spasojevic, Roy Yates, WINLAB Rutgers University, Cognitive Radios for Open Access to Spectrum
Links to other projectsLinks to other projects Platform/Testbed projects
Dirk Grunwald (U. Colorado), John Chapin (Vanu, Inc), Joe Carey (Fidelity Comtech) A Programmable Wireless Platform For Spectral, Temporal and Spatial Spectrum Management
Jeffrey H. Reed, William H. Tranter, and R. Michael Buehrer, Virginia Tech, An Open Systems Approach for Rapid Prototyping Waveforms for Software Defined Radio
D. Raychaudhuri (WINLAB, Rutgers University) ORBIT: Open Access Research Testbed for Next-Generation Wireless Networks
B. Ackland, I. Seskar & D. Raychaudhuri, (WINLAB, Rutgers University), T. Sizer (Lucent Technologies), J. Laskar(GA Tech) High Performance Cognitive Radio Platform with Integrated Physical and Network Layer Capabilities
Babak Daneshrad, University of California, Los Angeles, Programmable/Versatile Radio Platforms for the Networking Research Community
Prasant Mohapatra, University of California, Davis, Quail Ridge Wireless Mesh Networks: A Wide Area Test-bed
Platform/Testbed projects Dirk Grunwald (U. Colorado), John Chapin (Vanu, Inc), Joe Carey (Fidelity
Comtech) A Programmable Wireless Platform For Spectral, Temporal and Spatial Spectrum Management
Jeffrey H. Reed, William H. Tranter, and R. Michael Buehrer, Virginia Tech, An Open Systems Approach for Rapid Prototyping Waveforms for Software Defined Radio
D. Raychaudhuri (WINLAB, Rutgers University) ORBIT: Open Access Research Testbed for Next-Generation Wireless Networks
B. Ackland, I. Seskar & D. Raychaudhuri, (WINLAB, Rutgers University), T. Sizer (Lucent Technologies), J. Laskar(GA Tech) High Performance Cognitive Radio Platform with Integrated Physical and Network Layer Capabilities
Babak Daneshrad, University of California, Los Angeles, Programmable/Versatile Radio Platforms for the Networking Research Community
Prasant Mohapatra, University of California, Davis, Quail Ridge Wireless Mesh Networks: A Wide Area Test-bed
NSF NeTS Workshop
Additional InformationAdditional Information
ENOB: Effective Non-Opportunistic Bandwidth
ENOB: Effective Non-Opportunistic Bandwidth
Equivalent non-opportunistic bandwidth required to achieve the same throughput vector as in the case of opportunistic spectrum availability.
Non-opportunistic band: always available to the users as in the traditional command-and-control manner.
Depends on channel availability correlations of secondary users
A metric to quantify the impact of diversity
Equivalent non-opportunistic bandwidth required to achieve the same throughput vector as in the case of opportunistic spectrum availability.
Non-opportunistic band: always available to the users as in the traditional command-and-control manner.
Depends on channel availability correlations of secondary users
A metric to quantify the impact of diversity
A Naïve ExampleA Naïve Example
Two secondary nodes opportunistically access a primary channel
Observes independent channel availability with prob. p.
They interfere with each other Assume one unit of throughput per unit of bw.
Two secondary nodes opportunistically access a primary channel
Observes independent channel availability with prob. p.
They interfere with each other Assume one unit of throughput per unit of bw.
A Naïve Example Cont’dA Naïve Example Cont’d
Total throughput: W(p*p*1+2p(1-p)*1+(1-p)(1-p)*0)=Wp(2-p)
ENOB = Wp(2-p) 62% white space under 3G
W= 3GHz, p= 0.62
ENOB = 2.76 GHz Instead of Wp=3*0.62=1.86GHz
Total throughput: W(p*p*1+2p(1-p)*1+(1-p)(1-p)*0)=Wp(2-p)
ENOB = Wp(2-p) 62% white space under 3G
W= 3GHz, p= 0.62
ENOB = 2.76 GHz Instead of Wp=3*0.62=1.86GHz
Intuitions Intuitions
Spectrum is not being “created” by secondary users. Exploit spectrum holes created by primary users.
Different secondary users have diff. availability
Spectrum opportunity and its properties are determined by primary users
ENOB: a metric to quantify the degree of spatial reuse and statistical multiplexing between primary and secondary users. Analogy: effective bandwidth used to capture statistic
multiplexing gain. Depends on correlations of channel availability among users Depends on sharing criterion
Spectrum is not being “created” by secondary users. Exploit spectrum holes created by primary users.
Different secondary users have diff. availability
Spectrum opportunity and its properties are determined by primary users
ENOB: a metric to quantify the degree of spatial reuse and statistical multiplexing between primary and secondary users. Analogy: effective bandwidth used to capture statistic
multiplexing gain. Depends on correlations of channel availability among users Depends on sharing criterion
ENOB of a Chain TopologyENOB of a Chain Topology
Consider the dependency of channel availability among users
Evenly spaced nodes p0: prob. a node observes the channel avail.
pc: prob. node i observes given a neighbor does
Consider the dependency of channel availability among users
Evenly spaced nodes p0: prob. a node observes the channel avail.
pc: prob. node i observes given a neighbor does
…1 2 3 N
A Chain TopologyA Chain Topology
Different SchemesDifferent Schemes Node 1 interferes with all
others Nodes observe channel
availability independently Objectives:
maxsum maxmin maxT1
Node 1 interferes with all others
Nodes observe channel availability independently
Objectives: maxsum maxmin maxT1
1
2
35
4
ENOB cont’dENOB cont’d
ENOB SummaryENOB Summary
A metric to quantify the effect of opportunistic channel availability
Its value depends on Topology, traffic pattern of primary, etc. Channel availability dependency Channel allocation algorithm/objective
Heterogeneous network Implications on resource management
A metric to quantify the effect of opportunistic channel availability
Its value depends on Topology, traffic pattern of primary, etc. Channel availability dependency Channel allocation algorithm/objective
Heterogeneous network Implications on resource management
Why traffic-aware topology control? Why traffic-aware topology control?
Topology at the maximum power Topology with minimum power and interference
Two traffic patterns Local: every node sends to its right neighbor Single-sink: every nodes sends to the nth node
Two traffic patterns Local: every node sends to its right neighbor Single-sink: every nodes sends to the nth node
An Example (cont’d)An Example (cont’d)
Local Single-sink
Clique 1/(n-1) 1/(n-1)
Chain 1/3 < 1/(3n-6)
Topology at the maximum powerTopology with minimum power and interference
n-3 n-2 n-1 n
n-1n-2n-3
Observation: Minimizing interference/power is not necessarily optimal.
MotivationsMotivations Topology control must take into account traffic. Accurate traffic information is hardly available
Traffic varies over time Difficult to measure Dissemination of traffic information may incur excessive
overhead Topology control should be infrequent to avoid frequent service
disruptions On the other hand, coarse estimation on the traffic
pattern/demand is possible Source-destination pairs (e.g., single-sink) Range of the traffic demands (e.g., 200K – 1Mbps)
Topology control must take into account traffic. Accurate traffic information is hardly available
Traffic varies over time Difficult to measure Dissemination of traffic information may incur excessive
overhead Topology control should be infrequent to avoid frequent service
disruptions On the other hand, coarse estimation on the traffic
pattern/demand is possible Source-destination pairs (e.g., single-sink) Range of the traffic demands (e.g., 200K – 1Mbps)
Traffic-Oblivious Routing and Scheduling
Traffic-Oblivious Routing and Scheduling
Objective: to design a routing and scheduling that works well for a range of traffic conditions To achieve the optimal worst-case performance in the
range of traffic conditions being considered
The problem can be solved using a single LP with an infinite number of constraints.
Objective: to design a routing and scheduling that works well for a range of traffic conditions To achieve the optimal worst-case performance in the
range of traffic conditions being considered
The problem can be solved using a single LP with an infinite number of constraints.
Competitive AnalysisCompetitive AnalysisCongestion
Minimum congestion level
Competitive ratio
Oblivious ratio
FormulationFormulation
ObjectiveObjective
Problem formulation
Non-linear
FormulationFormulationMaster LPMaster LP
All traffic patternsInfinite #
FormulationFormulation
Slave LP (to check the constraint of the master LP)
Slave LP (to check the constraint of the master LP)
FormulationFormulation The above formulation has finite number of
variables, but infinite number of constraints. To further reduce the complexity
Convert the slave LP to its dual form Combine the master and the dual of the slave to form
a single LP
The above formulation has finite number of variables, but infinite number of constraints.
To further reduce the complexity Convert the slave LP to its dual form Combine the master and the dual of the slave to form
a single LP
What have we learned?What have we learned?
Well-designed multipath is desirable. Spatial reuse Load balancing
Robust performance Low oblivious ratio Close to ideal performance with perfect
information Robust even under faulty information
Well-designed multipath is desirable. Spatial reuse Load balancing
Robust performance Low oblivious ratio Close to ideal performance with perfect
information Robust even under faulty information