Cog-TV Business and Technical Analysis of Cognitive Radio TV Sets for Enhanced Spectrum Access (CNS 1247941/1247914) • PI (UNL): Mehmet C. Vuran (CSE, [email protected]) • Co-PI (UNL): Demet Batur (Management, [email protected]) • PI (OSU): Eylem Ekici (ECE, [email protected])
• 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? NSF EARS: Collaborative Research: Cog-TV: Business and Technical Analysis of Cognitive Radio TV Sets for Enhanced Spectrum Access 1 Research Goals of Cog-TV
Research Goals: • Business Aspects of Cog-TV: Dynamic pricing scheme 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.
Potential Payoffs: • 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
NSF EARS: Collaborative Research: Cog-TV: Business and Technical Analysis of Cognitive Radio TV Sets for Enhanced Spectrum Access 2 Business Aspects of Cog-TV
• Dynamic pricing: – The capacity of CRN varies based on factors such as the availability of TV white space, choice of TV channel view, and population density. – The TV broadcasting company will post hourly prices in real time based on changing capacity and predicted demand, especially higher prices at periods of peak demand. – Changing prices will motivate customers to shift their Internet activity to periods of high capacity but low demand. • As a result, customers will minimize their connection cost. TV broadcast companies will maximize their profit and increase customer satisfaction by providing Internet to more customers at even lower prices
• Cost of Cog-TV integrated network: Cost of building the Cog-TV infrastructure in the urban and rural areas will be analyzed separately to determine economic feasibility.
NSF EARS: Collaborative Research: Cog-TV: Business and Technical Analysis of Cognitive Radio TV Sets for Enhanced Spectrum Access 3 Cog-TV • TV spectrum ownership gives TV broadcast companies a major advantage in extending their business to become an Internet service provider using the TV white spaces • 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
• Broader Impacts: Interdisciplinary research in operations research, decision sciences, wireless communication, networking, and optimization • Dynamic Pricing: Change price dynamically to manage demand – Provide real-time pricing information to customers – Customers can access Internet strategically to lower their bills • Neighborhood Watch: Utilize network of Cog-TVs to improve unlicensed band utilization through collaborative sensing mechanism
NSF EARS: Collaborative Research: Cog-TV: Business and Technical Analysis of Cognitive Radio TV Sets for Enhanced Spectrum Access 4 Main Threads of the Project • Correlation-based Collaborative Spectrum Sensing: Provably optimal cooperative algorithms through a novel sensing deficiency virtual queue [1] concept – Exploit the four dimensions of correlation (spatial, temporal, spectral) – Explore resource cost functions based on price paid by the customer – Develop distributed algorithms • Cog-TV-Initiated Spectrum Handoff: Account for TV tune-in events and predictions to enable proactive spectrum handoff decisions – TV viewers and cognitive Internet users are both customers – Evaluate the costs and benefits of staying on a given channel despite TV tune-in on the channel – Self-competition for Cog-TV-initiated spectrum handoff • Evaluation Testbed: Utilizes distinct properties of Nebraska (sparse population) and Ohio (dense population) • Education and Outreach: Attract undergraduate students by UCARE (UNL) and UROP (OSU) programs. Reach underrepresented groups and high schools, e.g., Ohio State Women in Engineering Program, UNL 4-H program.
[1] D. Xue, E. Ekici, M. C. Vuran, ``(CORN)^2: Correlation-based Cooperative Spectrum Sensing in Cognitive Radio Networks,’’ in Proc. Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt'12), Paderborn, Germany, May 2012. NSF EARS: Collaborative Research: Cog-TV: Business and Technical Analysis of Cognitive Radio TV Sets for Enhanced Spectrum Access 5 Case Study:
Available Channel Capacity with Cog-TV vs. current FCC Rules Lincoln, NE & New York, NY
or “Is it really worth it?”
NSF EARS: Collaborative Research: Cog-TV: Business and Technical Analysis of Cognitive Radio TV Sets for Enhanced Spectrum Access 6 TV Rating Assumption Random Generated TV Rating for Channel 2~51 3.5
3
2.5
2 percent 1.5
1
0.5
0 5 10 15 20 25 30 35 40 45 50 channel Assumptions: (1) 8% of population are watching TV, (2) Randomly generated rating data
NSF EARS: Collaborative Research: Cog-TV: Business and Technical Analysis of Cognitive Radio TV Sets for Enhanced Spectrum Access Population Density of Lincoln
-3 Population Density in Lincoln, 2010, in Persons per Square Mile x 10
2
1.8
40.9 N 1.6
1.4
1.2 40.8 N
1
0.8
0.6 40.7 N
0.4
97.0 W 96.9 W 96.8 W 96.7 W 96.6 W 96.5 W 96.4 W 0.2
NSF EARS: Collaborative Research: Cog-TV: Business and Technical Analysis of Cognitive Radio TV Sets for Enhanced Spectrum Access Population and TV Tower Coverage Lincoln, NE
NSF EARS: Collaborative Research: Cog-TV: Business and Technical Analysis of Cognitive Radio TV Sets for Enhanced Spectrum Access TV Channel Viewership in Lincoln
NSF EARS: Collaborative Research: Cog-TV: Business and Technical Analysis of Cognitive Radio TV Sets for Enhanced Spectrum Access -3 Population Density in Lincoln, 2010, in Persons per Square Mile x 10
2
1.8
40.9 N 1.6
1.4
1.2 40.8 N Available TV Channels in Lincoln 1 0.8
0.6 40.7 N FCC vs Cog-TV (1km Cell) 0.4 97.0 W 96.9 W 96.8 W 96.7 W 96.6 W 96.5 W 96.4 W 0.2
FCC Cog-TV
NSF EARS: Collaborative Research: Cog-TV: Business and Technical Analysis of Cognitive Radio TV Sets for Enhanced Spectrum Access Available Channels in Lincoln (FCC vs Cog-TV) Capacity Distribution of FCC rules and Cog-TV 600 Capacity 1: FCC 2010 rule Capacity 2: Cog-TV 500
400
300 Number of Cells Number 200
100
0 0 5 10 15 20 25 30 35 40 45 50 Number of Available Channels Cell Size: 1 km
NSF EARS: Collaborative Research: Cog-TV: Business and Technical Analysis of Cognitive Radio TV Sets for Enhanced Spectrum Access Available Channels in Lincoln (FCC vs Cog-TV) Capacity Distribution of FCC rules and Cog-TV Capacity Distribution of FCC rules and Cog-TV Capacity Distribution of FCC rules and Cog-TV Capacity Distribution of FCC rules and Cog-TV 600 140 60 25 Capacity 1: FCC 2010 rule Capacity 1: FCC 2010 rule Capacity 1: FCC 2010 rule Capacity 1: FCC 2010 rule Capacity 2: Cog-TV Capacity 2: Cog-TV 120 Capacity 2: Cog-TV Capacity 2: Cog-TV 500 50 20
100 400 40 15 80 300 30 60
10
Number of Cells Number of Cells Number of Cells Number of Cells Number 200 20 40
5 100 10 20
0 0 0 0 0 5 10 15 20 25 30 35 40 45 50 0 5 10 15 20 25 30 35 40 45 50 0 5 10 15 20 25 30 35 40 45 50 0 5 10 15 20 25 30 35 40 45 50 Number of Available Channels Number of Available Channels Number of Available Channels Number of Available Channels Cell Size: 1 km 2 km 3 km 5 km
Capacity Distribution of FCC rules and Cog-TV Capacity Distribution of FCC rules and Cog-TV 12 9 Capacity 1: FCC 2010 rule Capacity 2: Cog-TV 8 10 7 Capacity 1: FCC 2010 rule Capacity 2: Cog-TV 8 6
5 6
4
Number of Cells Number of Cells Number 4 3
2 2 1
0 0 0 5 10 15 20 25 30 35 40 45 50 0 5 10 15 20 25 30 35 40 45 50 Number of Available Channels Number of Available Channels 7 km 10 km NSF EARS: Collaborative Research: Cog-TV: Business and Technical Analysis of Cognitive Radio TV Sets for Enhanced Spectrum Access Capacity vs Cell size Lincoln, NE
Capacity vs Cell Size in Lincoln, NE 42 FCC 2010 mean Cog−TV mean 40
38
36
y
t i
c 34
a
p
a c
32
l
e
n n
a 30
h c
28
26
24
22 0 1 2 3 4 5 6 7 8 9 10 cell size in km NSF EARS: Collaborative Research: Cog-TV: Business and Technical Analysis of Cognitive Radio TV Sets for Enhanced Spectrum Access Manhattan, New York
Population Density in Manhattan, 2010
0.05
0.04
40.8 N 0.03
0.02
40.7 N 0.01
74.0 W
0
Population Density Channel selection
NSF EARS: Collaborative Research: Cog-TV: Business and Technical Analysis of Cognitive Radio TV Sets for Enhanced Spectrum Access Population, TV Signal Coverage, NY
NSF EARS: Collaborative Research: Cog-TV: Business and Technical Analysis of Cognitive Radio TV Sets for Enhanced Spectrum Access Manhattan, NY, 0.2km cell
FCC Cog-TV
NSF EARS: Collaborative Research: Cog-TV: Business and Technical Analysis of Cognitive Radio TV Sets for Enhanced Spectrum Access Manhattan, NY, 0.3km cell
FCC Cog-TV
NSF EARS: Collaborative Research: Cog-TV: Business and Technical Analysis of Cognitive Radio TV Sets for Enhanced Spectrum Access Manhattan, NY, 0.5km cell
FCC Cog-TV
NSF EARS: Collaborative Research: Cog-TV: Business and Technical Analysis of Cognitive Radio TV Sets for Enhanced Spectrum Access Available Channels in Manhattan, NY (FCC vs Cog-TV)
Cell Size: 200 m 300 m 500 m
1 km 3 km
NSF EARS: Collaborative Research: Cog-TV: Business and Technical Analysis of Cognitive Radio TV Sets for Enhanced Spectrum Access 20 Capacity vs Cell size Manhattan, NY
Capacity vs Cell Size in Manhattan, NY 30 FCC 2010 mean Cog−TV mean
25
20
y
t
i
c
a
p
a c
15
l
e
n
n
a
h c 10
5
0 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 cell size in km
NSF EARS: Collaborative Research: Cog-TV: Business and Technical Analysis of Cognitive Radio TV Sets for Enhanced Spectrum Access CORN2: Correlation-Based Cooperative Spectrum Sensing in CRNs [D. Xue, E. Ekici, M. C. Vuran, ``(CORN)^2: Correlation-based Cooperative Spectrum Sensing in Cognitive Radio Networks,’’ in Proc. Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt'12), Paderborn, Germany, May 2012. • Local information essential to assess spectral availability • Most observations are highly correlated in – Space – Time – Spectrum • Objective: Leverage correlations for cooperative spectrum sensing to minimize energy consumption Develop (centralized and distributed) sensing scheduling algorithms • Nodes that perform local sensing broadcast their results • Past and current sensing results as well as results obtained from nearby nodes are combined • A long-term target sensing information quality is maintained
NSF EARS: Collaborative Research: Cog-TV: Business and Technical Analysis of Cognitive Radio TV Sets for Enhanced Spectrum Access 22 CORN2: Correlation-Based Cooperative Spectrum Sensing in CRNs
• Algorithm design involves two virtual queues:
– Local Sensing Queue ensures that nodes perform sensing at a rate > RS and do not cheat
– Sensing Deficiency Queue ensures a sensing quality > RD by eliminating deficiency at rate Mic(t)
– Mic(t) is the accumulation of all local sensing and information obtained from nearby nodes – The centralized solution ensures stability of all queues while minimizing total energy consumption • Distributed Algorithm: – 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 is a solution to a MWM problem on a bipartite graph with local weights and constraints Can be computed locally
NSF EARS: Collaborative Research: Cog-TV: Business and Technical Analysis of Cognitive Radio TV Sets for Enhanced Spectrum Access 23 CORN2: Correlation-Based Cooperative Spectrum Sensing in CRNs
RS = 0.05, RD = 0.95, PS = 3.5mJ, PTx= 0.1125mJ, wi,j(t) = 0.90
NSF EARS: Collaborative Research: Cog-TV: Business and Technical Analysis of Cognitive Radio TV Sets for Enhanced Spectrum Access 24