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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 Thursday, 11 July 13

TV White Spaces

• “White Spaces” refer to areas

where the spectrum is unused by the licensed user

• TV band: 470-790 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

High Power TV Broadcasts

White Spaces where the spectrum can be reused by low power devices

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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

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Spectrum Sharing among Home Networks

Geolocation Database HWSN Spectrum Manager Broadband Provider

• Secondary users are Home

White Space Networks in our context

• 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

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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

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Why not traditional auction schemes ?

A B C D E

A: 1, 2 A: 1, 2, 3 A: 1, 2, 3 A: 2, 3 A: 2, 3 V: 13, 8, 6 V: 18, 16, 4 V: 8, 6, 2 V: 12, 10, 6 V: 14, 12, 10

• Channel Re-use
• Channel Sharing
• Heterogeneous Channel Availability
• Marginal Valuations

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VERUM: An online multi-unit truthful iterative auction

Geolocation Database Increment Round Price Demand Exists ? Final Assignment Assign Clinched Channels Announce Round Price Start of New Epoch Spectrum Manager A D B C E

Yes No

No of Channels Available Channels

HWSNs

• 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.

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VERUM: An online multi-unit truthful iterative auction

A B C D E

A: 1, 2 A: 1, 2, 3 A: 1, 2, 3 A: 2, 3 A: 2, 3 V: 13, 8, 6 V: 18, 16, 4 V: 8, 6, 2 V: 12, 10, 6 V: 14, 12, 10

8 Thursday, 11 July 13

VERUM: An online multi-unit truthful iterative auction

A B C D E

A: 1, 2 A: 1, 2, 3 A: 1, 2, 3 A: 2, 3 A: 2, 3 V: 13, 8, 6 V: 18, 16, 4 V: 8, 6, 2 V: 12, 10, 6 V: 14, 12, 10

Round Price: 2

3 3 3 2 3

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VERUM: An online multi-unit truthful iterative auction

A B C D E

A: 1, 2 A: 1, 2, 3 A: 1, 2, 3 A: 2, 3 A: 2, 3 V: 13, 8, 6 V: 18, 16, 4 V: 8, 6, 2 V: 12, 10, 6 V: 14, 12, 10

Round Price: 6

2 2 3 1 2

• Since demand of D is one where as two channels are

available at E, a channel is “clinched” by E at price 6

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VERUM: An online multi-unit truthful iterative auction

A B C D E

A: 1, 2 A: 1, 2, 3 A: 1, 2, 3 A: 2, 3 A: 2, 3 V: 13, 8, 6 V: 18, 16, 4 V: 8, 6, 2 V: 12, 10, 6 V: 14, 12, 10

Round Price: 8

1 2 3 2

• E clinches another channel at price 8, as demand of D

reduces to zero.

11 Thursday, 11 July 13

VERUM: An online multi-unit truthful iterative auction

A B C D E

A: 1, 2 A: 1, 2, 3 A: 1, 2, 3 A: 2, 3 A: 2, 3 V: 13, 8, 6 V: 18, 16, 4 V: 8, 6, 2 V: 12, 10, 6 V: 14, 12, 10

Round Price: 12

1 2 1

• 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

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VERUM: An online multi-unit truthful iterative auction

A B C D E

A: 1, 2 A: 1, 2, 3 A: 1, 2, 3 A: 2, 3 A: 2, 3 V: 13, 8, 6 V: 18, 16, 4 V: 8, 6, 2 V: 12, 10, 6 V: 14, 12, 10

Round Price: 13

2 1

• B and C clinch a channel each at price 13

13 Thursday, 11 July 13

VERUM: An online multi-unit truthful iterative auction

A B C D E

A: 1, 2 A: 1, 2, 3 A: 1, 2, 3 A: 2, 3 A: 2, 3 V: 13, 8, 6 V: 18, 16, 4 V: 8, 6, 2 V: 12, 10, 6 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

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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

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VERUM: How is it different from existing schemes ?

2000 100 400 600 800 1000 1200 1400 1600 1800 60 5 10 15 20 25 30 35 40 45 50 55

Number of HWSNs % Reduction in Revenue

• 100

40 45 50 55 60 65 70 75 80 85 90 95 80 10 20 30 40 50 60 70

Average Demand (%) % Reduction in Spectrum Utilization

• 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.

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VERUM: How is it different from existing schemes ?

• The higher revenue in the urban scenario is due to lower density of

HWSNs resulting in higher channel reuse.

2000 100 400 600 800 1000 1200 1400 1600 1800 400,000 50,000 100,000 150,000 200,000 250,000 300,000 350,000

Number of HWSNs Revenue

• 2000

100 400 600 800 1000 1200 1400 1600 1800 400,000 50,000 100,000 150,000 200,000 250,000 300,000 350,000

Number of HWSNs Revenue

• 17

Thursday, 11 July 13

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 Thursday, 11 July 13

• S. Manickam, M. Marina, S. Pediaditaki, and M.
• Nekovee. Auctioning based Coordinated TV White

Space Spectrum Sharing for Home Networks. arXiv CoRR abs/1307.0962

• 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, 2008.

Reference

19 Thursday, 11 July 13

Thank you !

Saravana Manickam, R.S.Manik@ed.ac.uk

20 Thursday, 11 July 13