Spectrum Bidding in Wireless Networks and Related Spectrum Bidding in Wireless Networks and Related Ping Xu 1 Advisor: Xiang-Yang Li 1 1 Illinois Institute of Technology Feb. 11th, 2008 university-logo Ping Xu Spectrum Bidding in Wireless Networks and Related
Spectrum Bidding in Wireless Networks and Related Outline Spectrum Bidding in Wireless Networks and Related: ◮ Background ◮ Problem Formulation ◮ Our Approaches ◮ Summary and Future Works university-logo Ping Xu Spectrum Bidding in Wireless Networks and Related
Background Problem Formulation Spectrum Bidding in Wireless Networks and Related Our Approaches Summary and Future Works Background: Spectrum Scarcity Problem Figure: Frequency Allocations of The Radio Spectrum in US. university-logo Ping Xu Spectrum Bidding in Wireless Networks and Related
Background Problem Formulation Spectrum Bidding in Wireless Networks and Related Our Approaches Summary and Future Works Background White Space - Unused Spectrum ◮ To avoid interference ◮ Spectrum utilization with fixed allocation depends strongly on time and place. "In more congested areas, there is still ample space." – www.tvtechnology.com ◮ Dallas – 40 percent ◮ Boston – 38 percent ◮ Seattle – 52 percent ◮ San Francisco – 37 percent. ◮ A result of technical changes. For example, the planned switchover to digital television may free up large areas between 54MHz and 698MHz . ◮ "Battle Heats Up for TV Spectrum "White Space" Use" – university-logo WIMAX.com Ping Xu Spectrum Bidding in Wireless Networks and Related
Background Problem Formulation Spectrum Bidding in Wireless Networks and Related Our Approaches Summary and Future Works Background White Space - Unused Spectrum ◮ To avoid interference ◮ Spectrum utilization with fixed allocation depends strongly on time and place. "In more congested areas, there is still ample space." – www.tvtechnology.com ◮ Dallas – 40 percent ◮ Boston – 38 percent ◮ Seattle – 52 percent ◮ San Francisco – 37 percent. ◮ A result of technical changes. For example, the planned switchover to digital television may free up large areas between 54MHz and 698MHz . ◮ "Battle Heats Up for TV Spectrum "White Space" Use" – university-logo WIMAX.com Ping Xu Spectrum Bidding in Wireless Networks and Related
Background Problem Formulation Spectrum Bidding in Wireless Networks and Related Our Approaches Summary and Future Works Background White Space - Unused Spectrum ◮ To avoid interference ◮ Spectrum utilization with fixed allocation depends strongly on time and place. "In more congested areas, there is still ample space." – www.tvtechnology.com ◮ Dallas – 40 percent ◮ Boston – 38 percent ◮ Seattle – 52 percent ◮ San Francisco – 37 percent. ◮ A result of technical changes. For example, the planned switchover to digital television may free up large areas between 54MHz and 698MHz . ◮ "Battle Heats Up for TV Spectrum "White Space" Use" – university-logo WIMAX.com Ping Xu Spectrum Bidding in Wireless Networks and Related
Background Problem Formulation Spectrum Bidding in Wireless Networks and Related Our Approaches Summary and Future Works Background Opportunistic or Dynamic Spectrum Allocation ◮ Cognitive radio(CR) ◮ Spectrum auction ◮ How to deal with selfish behavior? ◮ Combine game theory with wireless communication modeling university-logo Ping Xu Spectrum Bidding in Wireless Networks and Related
Background Problem Formulation Spectrum Bidding in Wireless Networks and Related Our Approaches Summary and Future Works Background Opportunistic or Dynamic Spectrum Allocation ◮ Cognitive radio(CR) ◮ Spectrum auction ◮ How to deal with selfish behavior? ◮ Combine game theory with wireless communication modeling university-logo Ping Xu Spectrum Bidding in Wireless Networks and Related
Background Problem Formulation Spectrum Bidding in Wireless Networks and Related Our Approaches Summary and Future Works Background Opportunistic or Dynamic Spectrum Allocation ◮ Cognitive radio(CR) ◮ Spectrum auction ◮ How to deal with selfish behavior? ◮ Combine game theory with wireless communication modeling university-logo Ping Xu Spectrum Bidding in Wireless Networks and Related
Background Problem Formulation Spectrum Bidding in Wireless Networks and Related Our Approaches Summary and Future Works Main Idea Construct an auction to assign spectrum ◮ Auctioneer: primary users ◮ Bidders: secondary users, selfish, but rational ◮ Objects ◮ Determine winners and payments ◮ Maximize the social efficiency - total valuation of winners university-logo Ping Xu Spectrum Bidding in Wireless Networks and Related
Background Problem Formulation Spectrum Bidding in Wireless Networks and Related Our Approaches Summary and Future Works Network Model ◮ Primary user U who holds the right of some spectrum channels ◮ secondary users V = { v 1 , v 2 , · · · , v n } who wants to lease the right of some spectrum channels ◮ in some geometry region D ( v i , r i ) ◮ for some time period T i ◮ for some frequencies F i ◮ with bid b i ◮ private valuation w i university-logo Ping Xu Spectrum Bidding in Wireless Networks and Related
Background Problem Formulation Spectrum Bidding in Wireless Networks and Related Our Approaches Summary and Future Works Network Model ◮ Primary user U who holds the right of some spectrum channels ◮ secondary users V = { v 1 , v 2 , · · · , v n } who wants to lease the right of some spectrum channels ◮ in some geometry region D ( v i , r i ) ◮ for some time period T i ◮ for some frequencies F i ◮ with bid b i ◮ private valuation w i university-logo Ping Xu Spectrum Bidding in Wireless Networks and Related
Background Problem Formulation Spectrum Bidding in Wireless Networks and Related Our Approaches Summary and Future Works Network Model A direct viewing graph for a single channel Figure: An illustration of cylinder graph university-logo Ping Xu Spectrum Bidding in Wireless Networks and Related
Background Problem Formulation Spectrum Bidding in Wireless Networks and Related Our Approaches Summary and Future Works Object Find an allocation method, which must be ◮ conflict free in geometry region, time period and frequencies ◮ maximize the social efficiency - total valuation of winners. In most cases, this problem is NP-hard . university-logo Ping Xu Spectrum Bidding in Wireless Networks and Related
Background Problem Formulation Spectrum Bidding in Wireless Networks and Related Our Approaches Summary and Future Works Problem Formulation For notational convenience, we use CRT to denote a version of problem under special assumption, where ◮ C hannel requirement ◮ S (single-minded), F (flexible-minded), Y (single channel) ◮ R egion requirement ◮ O (overlap), U (unit disks), G (general disks) ◮ T ime requirement ◮ I (time interval), D (time duration), M (time interval or duration) university-logo Ping Xu Spectrum Bidding in Wireless Networks and Related
Background Problem Formulation Spectrum Bidding in Wireless Networks and Related Our Approaches Summary and Future Works Example For example, problem SUI represents ◮ C hannel requirement: S ingle-minded ◮ R egion requirement: U nit Disks ◮ T ime requirement: Time I nterval university-logo Ping Xu Spectrum Bidding in Wireless Networks and Related
Background Problem Formulation Spectrum Bidding in Wireless Networks and Related Our Approaches Summary and Future Works Some well-known problems ◮ Knapsack problem: Problem YOD. ◮ Set packing problem: a special case of problem SOI, SUI, SGI. ◮ Maximum weighted independent set problem of a disk graph: a special case of Problem YGI ◮ Multi-knapsack problem: a special case of Problem YUD. university-logo Ping Xu Spectrum Bidding in Wireless Networks and Related
Background Problem Formulation Spectrum Bidding in Wireless Networks and Related Our Approaches Summary and Future Works Our Results Problems we mainly focus on ◮ problem YOM: 1/2 approximation algorithm ◮ problem YUI: PTAS ◮ problem YUD: 1/9 approximation algorithm ◮ problem YUM: 1/10 approximation algorithm ◮ problem SUI: Θ( √ m ) approximation algorithm university-logo Ping Xu Spectrum Bidding in Wireless Networks and Related
Background Problem Formulation Spectrum Bidding in Wireless Networks and Related Our Approaches Summary and Future Works Problem YOM ◮ C hannel requirement: Y (Single channel) ◮ R egion requirement: O verlap ◮ T ime requirement: M ixed(Time interval or duration) university-logo Ping Xu Spectrum Bidding in Wireless Networks and Related
Background Problem Formulation Spectrum Bidding in Wireless Networks and Related Our Approaches Summary and Future Works Algorithm for Problem YOM ◮ Partition bidders into two groups according to their time requirements. ◮ Find the best solution F to the group require time intervals with dynamic programming. ◮ Find the approximated best solution S ′ to the group with the FPTAS for knapsack problem. 1 ◮ max ( F , S ′ ) ≥ 2 ( 1 + ǫ ) · OPT . So we have a simple 2 + ǫ ′ approximation algorithm. university-logo Ping Xu Spectrum Bidding in Wireless Networks and Related
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