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HEAD: A Hybrid Spectrum Trading Framework for QoS -aware Secondary Users

HEAD: A Hybrid Spectrum Trading Framework for QoS -aware Secondary Users. Xiaojun Feng , Qian Zhang and Bo Li Hong Kong University of Science and Technology DySPAN 2014. Outline. Background System Model Hybrid Spectrum Trading Framework Simulation Results Conclusions. Background.

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HEAD: A Hybrid Spectrum Trading Framework for QoS -aware Secondary Users

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  1. HEAD: A Hybrid Spectrum Trading Framework for QoS-aware Secondary Users XiaojunFeng, Qian Zhang and Bo Li Hong Kong University of Science and Technology DySPAN 2014

  2. Outline • Background • System Model • Hybrid Spectrum Trading Framework • Simulation Results • Conclusions

  3. Background • Wireless spectrum is one of the most important resources in wireless communications.

  4. Background • Secondary spectrum trading Spectrum owner Secondary users

  5. Existing Spectrum Trading Models • Long-term Leasing • Sell the spectrum to the users for a long duration • Pre-fixed pricing plan • Short-term Rental • Spectrum is traded in a slot-by-slot basis • Different pricing plan in each slot

  6. Existing Spectrum Trading Models • Long-term Leasing • Pros • Guaranteed buyers in a long duration • Stable spectrum supplies for the buyers • Cons • Not flexible • Short-term Rental • Pros • Flexible for the buyers and sellers • Cons • The profit of the seller is hard to predict

  7. Challenges • No existing works consider a hybrid trading scheme with different usage durations • Hard to predict the profit in each pricing duration • Hard to determine resource allocation between both pricing channels.

  8. Our Work • Designing a hybrid pricing model consisting both the long-term and short-term pricing options. • Long-term leasing • The buyers subscribe to the predefined pricing scheme to enjoy stable and guaranteed supplies. • Short-term rental • Serve the buyers’ instantaneous demands in each short duration of time

  9. Outline • Background • System Model and Problem Formulation • Trading Schemes • System Model • Hybrid Spectrum Trading Framework • Simulation Results • Conclusions

  10. Scenario • One spectrum owner (SO) • Multiple secondary users (SU) • Spectrum availability stays the same in each period

  11. System Model - SU • Bandwidth demand: • Actual spectrum usage: • SUs have different QoSrequirements, which is measured by the following satisfaction function: • Utility function for SU :

  12. Trading Schemes • Long-term Leasing • Users are charged with a fee of • DO reserve a fixed ratio of the total available bandwidth for long-term users during the entire trading period • Short-term Rental • SO leverages auctions to distribute the unreserved bandwidth in each short time period • Unregistered SUs shared the unreserved spectrum • Bandwidth divided into channels of bandwidth

  13. Outline • Background • System Model and Problem Formulation • Hybrid Spectrum Trading Framework • Procedure • Short-term Auction Outcome Estimation • Long-term Plan Design • Simulation Results • Conclusions

  14. Spectrum Trading Procedure

  15. Backward Induction Analysis • Target of the spectrum owner: • Optimize revenue by determine the resource allocation to the short-term and long-term schemes. • Backward Induction • Step 1: Estimate the outcome in the short-term auction and the SU’s choices of the long-term scheme • Step 2: design parameters (, ) for the long-term scheme

  16. Auction Outcome Estimation • Key results • The distribution of bids in the auction can be estimated as normal distribution • Winning prices and number of winning channels can be estimated from the normal distribution • Winning prices increase with the number of users in the auction and decreases with the number of channels

  17. Long-term Plan Design • Revenue of the spectrum owner Payment from long-term users Prices changes in the auctions

  18. Outline • Background • System Model and Problem Formulation • Hybrid Spectrum Trading Framework • Simulation Results • Conclusions

  19. Simulation Settings • We assume there are a total of N = 10 − 300 SUs in the network. • The total number of time period is T = 100. • The expected bandwidth is = 36 MHz • The bandwidth of a channel can be = 3 or = 6 MHz

  20. Accuracy of Winning Price Estimation • C: different number of channels for sale • Each line: different distribution of user’s satisfaction parameters

  21. Optimize SO’s Revenue • Optimal spectrum allocated for long-term user:

  22. Optimize SO’s Revenue • Comparison between hybrid and singular pricing schemes

  23. Conclusions • It is challenging to design optimal hybrid schemes for secondary trading • We model the QoS requirements of secondary users with a satisfaction function relating to both the demands and actual usage. • We formulate the hybrid pricing problem and derive optimal trading schemes. • We show with numerical results that SO’s profit under different scenarios

  24. Thanks! HEAD: A Hybrid Spectrum Trading Framework for QoS-aware Secondary Users XiaojunFeng xfeng@cse.ust.hk

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