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PI (UNL): Mehmet C. Vuran (CSE, [email protected]) Co-PI (UNL): Demet Batur (Management, [email protected]) PI (OSU): Eylem Ekici (ECE, [email protected]) Cog-TV: Business and Technical Analysis of Cognitive Radio TV Sets for Enhanced Spectrum Access (CNS 1247941/1247914) Motivation Research Goals • Existing argument: “Emerging cognitive radio networks result in a technical and an economical conflict with the TV broadcast companies” • Our view: Conflict Opportunity • Is it economically and technically viable for broadcast companies to utilize TV white spaces for • low-cost Internet provision • web-enabled TV services? • Business Aspects of Cog-TV: Dynamic pricing schemes to balance demand between peak and non- peak periods; infrastructure cost analysis for Cog-TV integrated network. • Neighborhood Watch : Analysis of spectrum sensing accuracy and correlation in the spectrum sensing information; optimal sensing scheduling algorithms to minimize sensing overhead and maximize bandwidth. • Cog-TV-initiated Spectrum Handoff: Methodologies to estimate the opportune times to initiate spectrum handoff; strategies for broadcast companies to address the self-competition challenge that results in serving two types of customers: TV viewers and cognitive Internet users. CORN 2 : Correlation-Based Cooperative Spectrum Sensing in CRNs [4] Results: Energy Consumption / Node References [1] Nielsen Npower, Seasontodate 9/19/2011 to 1/29/2012 and 9/24/2012 to 1/27/2013 (h>p://www.nielsen.com) [2] h>p://www.csun.edu/science/health/docs/tv&health.html#tv_stats [3] Census 2010 [4] D. Xue, E. Ekici, M. C. Vuran, ``(CORN)^2: CorrelaTonbased CooperaTve Spectrum Sensing in CogniTve Radio Networks,’’ in Proc. Symposium on Modeling and OpTmizaTon in Mobile, Ad Hoc, and Wireless Networks (WiOpt'12), Paderborn, Germany, May 2012. Potential Payoffs Cog-TV Network Architecture Available Channel Capacity: Cog-TV vs. FCC TV Ratings (Worst-Case, Static) Daily Dynamics of Available Capacity Results: Available TV Channels Daily Variations of TV Viewership • Local information essential to assess spectral availability • Most observations are highly correlated in Space, Time, and Spectrum • Objective: Leverage correlations for cooperative spectrum sensing to minimize energy consumption Develop (centralized and distributed) sensing scheduling algorithms • Enable transformative and economically viable CRN development and management approaches • Bring affordable Internet service to a large group of American households • Impact consumer market by creating a niche market in new TV sets Cognitive radio-equipped TV sets (Cog-TVs) • TV tuner, integrated CR interface, and Wi-Fi interface • Cog-TV provides three main capabilities • Low-cost access to the Internet in residential and commercial spaces • Interference measurement of TV services for enhanced quality of user experience • Localized collaborative spectrum sensing for fine-grained spectrum management 5 10 15 20 25 30 35 40 45 50 0 0.5 1 1.5 2 2.5 3 3.5 Random Generated TV Rating for Channel 2~51 channel percent Not available publicly AssumpTons: (1) 8% of populaTon are watching broadcast TV (worstcase) (2) Randomly generated raTng data 7.0 ° W 96.9 ° W 96.8 ° W 96.7 ° W 96.6 ° W 96.5 ° W 96.4 ° W N N N Population Density in Lincoln, 2010, in Persons per Square Mile 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 x 10 -3 Lincoln Popula4on Density 74.0 ° W 40.7 ° N 40.8 ° N 0 0.01 0.02 0.03 0.04 0.05 Manha9an Popula4on Density • Spatial distribution of available channels (Lincoln & Manhattan, NY) FCC Cog-TV Virtual Queue Concept • Local Sensing Queue ensures that nodes perform sensing at a rate > R S and do not cheat • Sensing Deficiency Queue ensures a sensing quality > R D by eliminating deficiency at rate M ic (t) • The centralized solution ensures stability of all queues while minimizing total energy consumption • If total contribution of all neighbors is bounded, then a fully distributed algorithm exists • Bounded contribution holds for low SNR cases and when temporal correlation is high • The resulting algorithm can be computed locally R S = 0.05, R D = 0.95, P S = 3.5mJ, P Tx = 0.1125mJ, w i,j (t) = 0.90 0 10 20 30 40 50 60 70 80 90 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 0:00 1:00 2:00 3:00 4:00 Percent (%) Hour U.S TV Usage in a Day 0 5 10 15 20 25 30 35 40 45 50 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 0:00 1:00 2:00 3:00 4:00 Average Number of Available Channels Hour Channel Capacity (Cell 1.0km) Lincoln, NE 0 5 10 15 20 25 30 35 40 45 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 0:00 1:00 2:00 3:00 4:00 Average Number of Available Channels Hour Channel Capacity (Cell 0.5km) Manhattan, NY Cog-TV FCC 2010 • Limited public data on daily variations of TV viewership [1] • Interpolated to hourly intervals • 3.50% - Broadcast only TVs over total TVs [1] • Avg. 2.24 TVs per TV household [2] • 7.84% - Broadcast only TV sets over U.S population [3] 3 am 8 am noon 8 pm
