Auctioning based Coordinated TV White Space Spectrum Sharing for Home Networks

Transcription

1 Auctioning based Coordinated TV White Space Spectrum Sharing for Home Networks Saravana Manickam, Mahesh K. Marina University of Edinburgh Sofia Pediaditaki Maziar Nekovee Intel Labs Samsung 1

2 TV White Spaces White Spaces refer to areas where the spectrum is unused by the licensed user High Power TV Broadcasts TV band: MHz Protection of incumbent users is of utmost importance Favorable propagation characteristics motivates several use cases Rural Broadband Hot-spot Coverage In-home Broadband In-home Multimedia Machine to Machine White Spaces where the spectrum can be reused by low power devices 2

3 White Space Database Assisted Access Database discovery Receives channel usage parameters based on its location from a WSDB it chooses. Leverage the WSDB for interference aware coordinated TVWS sharing among secondary users 3

4 Spectrum Sharing among Home Networks Secondary users are Home White Space Networks in our context Geolocation Database HWSN Spectrum Manager Broadband Provider TVWS access point obtains spectrum on behalf of in-home WSDs Spectrum Manager allocates TVWS channels to HWSNs considering availability, usage by other WSDs, spectrum demand, and interference among WSDs 4

5 Micro Auction based Spectrum Sharing We propose the use of short term auctions for coordinating spectrum among secondary users Key Considerations Primary objective: Efficient Outcome Truthful/Strategy-Proof High Revenue Low Computational Complexity Efficient Spectrum Utilization 5

6 Why not traditional auction schemes? V: 8, 6, 2 A: 1, 2 A: 1, 2, 3 V: 13, 8, 6 V: 18, 16, 4 D V: 12, 10, 6 A C E B Channel Re-use A: 1, 2, 3 V: 14, 12, 10 Channel Sharing Heterogeneous Channel Availability Marginal Valuations 6

7 VERUM: An online multi-unit truthful iterative auction Geolocation Database Available Channels Increment Round Price Yes Demand Exists? No Start of New Epoch Spectrum Manager Assign Clinched Channels No of Channels Announce Round Price HWSNs A B C D E Final Assignment If the aggregate demand of a HWSN s neighbors is less than the number of channels available at that HWSN, then the difference is clinched by the HWSN. 7

8 VERUM: An online multi-unit truthful iterative auction V: 8, 6, 2 A: 1, 2 A: 1, 2, 3 V: 13, 8, 6 V: 18, 16, 4 D V: 12, 10, 6 A C E B A: 1, 2, 3 V: 14, 12, 10 8

9 VERUM: An online multi-unit truthful iterative auction Round Price: 2 V: 8, 6, A: 1, 2 A: 1, 2, 3 V: 13, 8, 6 V: 18, 16, 4 D V: 12, 10, 6 A C E B A: 1, 2, 3 V: 14, 12, 10 9

10 VERUM: An online multi-unit truthful iterative auction Round Price: 6 V: 8, 6, A: 1, 2 A: 1, 2, 3 V: 13, 8, 6 V: 18, 16, 4 D V: 12, 10, 6 A C E B A: 1, 2, 3 V: 14, 12, 10 Since demand of D is one where as two channels are available at E, a channel is clinched by E at price 6 10

11 VERUM: An online multi-unit truthful iterative auction Round Price: 8 V: 8, 6, A: 1, 2 A: 1, 2, 3 V: 13, 8, 6 V: 18, 16, 4 D V: 12, 10, 6 A C E B A: 1, 2, 3 V: 14, 12, 10 E clinches another channel at price 8, as demand of D reduces to zero. 11

12 VERUM: An online multi-unit truthful iterative auction Round Price: 12 V: 8, 6, A: 1, 2 A: 1, 2, 3 V: 13, 8, 6 V: 18, 16, 4 D V: 12, 10, 6 A C E B A: 1, 2, 3 V: 14, 12, 10 C clinches a channel as the aggregate demand of C s neighbors (A, B, and D) is two, where as three channels are available at C 12

13 VERUM: An online multi-unit truthful iterative auction Round Price: 13 V: 8, 6, A: 1, 2 A: 1, 2, 3 V: 13, 8, 6 V: 18, 16, 4 D V: 12, 10, 6 A C E B A: 1, 2, 3 V: 14, 12, 10 B and C clinch a channel each at price 13 13

14 VERUM: An online multi-unit truthful iterative auction V: 8, 6, 2 A: 1, 2 A: 1, 2, 3 V: 13, 8, 6 V: 18, 16, 4 D V: 12, 10, 6 A C E B A: 1, 2, 3 V: 14, 12, 10 Final allocations are, HWSN A wins one channel, B and C win two channels each. The channels are allocated using a greedy algorithm 14

15 VERUM: How is it different from existing schemes? We compare VERUM against VERITAS and SATYA, two existing truthful, efficient auction schemes To preserve truthfulness, they employ complex pricing schemes that realize Vickrey pricing VERITAS does not support channel sharing, heterogeneous channel availability, and marginal valuations SATYA supports channel sharing, heterogeneous channel availability and marginal valuation, but is only polynomial under certain restrictions 15

16 VERUM: How is it different from existing schemes? % Reduction in Revenue % Reduction in Spectrum Utilization Number of HWSNs Average Demand (%) We formulate the revenue maximizing spectrum allocation problem for both exclusive use and shared use as an integer linear program and solve it using Gurobi solver for comparison SATYA has a lower revenue due to channel sharing opportunities lost due to bucketing and ironing. 16

17 VERUM: How is it different from existing schemes? Revenue 400, , , , , ,000 Revenue 400, , , , , , , ,000 50,000 50, Number of HWSNs Number of HWSNs The higher revenue in the urban scenario is due to lower density of HWSNs resulting in higher channel reuse. 17

18 Conclusions Interference-aware coordinated TVWS spectrum sharing framework for home networks that relies on short-term auctions and leverages the geolocation database to additionally keep track of secondary use of TVWS spectrum. We have developed an online multi-unit auction mechanism VERUM that is truthful and efficient. 18

19 Reference S. Manickam, M. Marina, S. Pediaditaki, and M. Nekovee. Auctioning based Coordinated TV White Space Spectrum Sharing for Home Networks. arxiv CoRR abs/ R. Murty, R. Chandra, T. Moscibroda, and P. Bahl. SenseLess: A Database-Driven White Spaces Network. IEEE Transactions on Mobile Computing, 11(2), Feb 2012 X. Zhou, S. Gandhi, S. Suri, and H. Zheng. ebay in the Sky: Strategy-Proof Wireless Spectrum Auctions. In Proc. ACM MobiCom,

20 Thank you! Saravana Manickam, 20