Smart-Radio-Technology-Enable Opportunistic Spectrum Access

1. Smart-Radio-Technology-Enable Opportunistic Spectrum Access Univeristy Of California Davis PI: Xin Liu (CS) 2006@UCLA NSF NeTS Workshop

2. Project 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.

3. 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.

4. Dynamic 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

5. 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

6. Current 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

7. Links 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

8. Links 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

9. Additional Information NSF NeTS Workshop

10. 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

11. A 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.

12. A 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

13. 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

14. … 1 2 3 N ENOB 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

15. A Chain Topology

16. 2 5 1 3 4 Different Schemes • Node 1 interferes with all others • Nodes observe channel availability independently • Objectives: • maxsum • maxmin • maxT1

17. ENOB cont’d

18. ENOB 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

19. Why traffic-aware topology control? • Two traffic patterns • Local: every node sends to its right neighbor • Single-sink: every nodes sends to the nth node Topology at the maximum power Topology with minimum power and interference

20. An Example (cont’d) n-1 n-3 n n-2 n-3 n-1 n-2 Topology with minimum power and interference Topology at the maximum power Observation: Minimizing interference/power is not necessarily optimal.

21. Motivations • 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)

22. 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.

23. Competitive Analysis Congestion Minimum congestion level Competitive ratio Oblivious ratio

24. Formulation Objective Problem formulation Non-linear

25. Formulation Master LP All traffic patterns Infinite #

26. Formulation Slave LP (to check the constraint of the master LP)

27. Formulation • 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

28. 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