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