Shades of White: Impacts of Population Dynamics and TV Viewership on Available TV Spectrum

Current regulations leave a few television (TV) white spaces in populated urban areas where spectrum shortage is mostly experienced. As TV set feedback becomes essential in the next generation terrestrial TV standard, an opportunistic TV spectrum sharing based on TV receiver activity information and transmit power control is proposed to exploit the underutilized active TV channels. Based on investigation of the spatial–spectral–temporal characteristics of TV receiver activities, analytical models are developed to capture the spatio-temporal distributions of available spectrum and corresponding capacity. The influence of multiple factors, such as feedback delay, spectrum handover overhead, ranking order, and distribution of TV channel popularity are discussed and modeled. The proposed power control mechanism is verified through experiments at representative campus and residential environments. Empirical data-based simulations and geographic analyses are conducted to evaluate the developed models and further profile the spectrum opportunities within a cell, across New York city (NYC) and other 273 cities in the United States. In NYC, the proposed solution provides a <inline-formula><tex-math notation="LaTeX">$3.8$</tex-math></inline-formula>–<inline-formula><tex-math notation="LaTeX">$11.7$</tex-math></inline-formula>-fold increase of average spectrum availability, and <inline-formula><tex-math notation="LaTeX">$2.5$</tex-math></inline-formula>–<inline-formula><tex-math notation="LaTeX">$6.6$</tex-math></inline-formula>-fold increase of capacity from current regulations. By investigating the feasibility and prospects of this approach, this paper intends to motivate further discussions in policy, business, and privacy aspects to reach its significant potential.

[1]  Ning Liu,et al.  Dissecting User Behaviors for a Simultaneous Live and VoD IPTV System , 2014, TOMCCAP.

[2]  Andrea J. Goldsmith,et al.  Breaking Spectrum Gridlock With Cognitive Radios: An Information Theoretic Perspective , 2009, Proceedings of the IEEE.

[3]  Minghua Chen,et al.  WINET: Indoor white space network design , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[4]  Ian F. Akyildiz,et al.  A survey on spectrum management in cognitive radio networks , 2008, IEEE Communications Magazine.

[5]  Marco Zennaro,et al.  A Survey of TV White Space Measurements , 2014, AFRICOMM.

[6]  Yili Hong,et al.  On computing the distribution function for the Poisson binomial distribution , 2013, Comput. Stat. Data Anal..

[7]  Hugh Malcolm Beville,et al.  Audience Ratings: Radio, Television, Cable , 1985 .

[8]  Martin Haenggi,et al.  Stochastic Geometry for Modeling, Analysis, and Design of Multi-Tier and Cognitive Cellular Wireless Networks: A Survey , 2013, IEEE Communications Surveys & Tutorials.

[9]  Anant Sahai,et al.  Seeing the bigger picture: Context-aware regulations , 2012, 2012 IEEE International Symposium on Dynamic Spectrum Access Networks.

[10]  J. Ben Atkinson,et al.  An Introduction to Queueing Networks , 1988 .

[11]  Sumit Roy,et al.  Indoor–Outdoor TV White and Gray Space Availability: A U.S. Case Study , 2018 .

[12]  Andreas Achtzehn,et al.  Performance Assessment and Feasibility Analysis of IEEE 802.15.4m Wireless Sensor Networks in TV Grayspaces , 2017, ACM Trans. Sens. Networks.

[13]  Sumit Roy,et al.  Capacity Considerations for Secondary Networks in TV White Space , 2015, IEEE Transactions on Mobile Computing.

[14]  Seungjoon Lee,et al.  Modeling user activities in a large IPTV system , 2009, IMC '09.

[15]  Eylem Ekici,et al.  Ratings for spectrum: Impacts of TV viewership on TV whitespace , 2014, 2014 IEEE Global Communications Conference.

[16]  Ian F. Akyildiz,et al.  NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey , 2006, Comput. Networks.

[17]  David Gomez-Barquero,et al.  An Overview of the ATSC 3.0 Physical Layer Specification , 2016, IEEE Transactions on Broadcasting.

[18]  Pablo Rodriguez,et al.  Watching television over an IP network , 2008, IMC '08.

[19]  Seungjoon Lee,et al.  Modeling channel popularity dynamics in a large IPTV system , 2009, SIGMETRICS '09.

[20]  Ian F. Akyildiz,et al.  LTE-Advanced and the evolution to Beyond 4G (B4G) systems , 2014, Phys. Commun..

[21]  Edward W. Knightly,et al.  The case for UHF-band MU-MIMO , 2014, MobiCom.

[22]  Dimitrios D. Vergados,et al.  A Survey on the Successive Interference Cancellation Performance for Single-Antenna and Multiple-Antenna OFDM Systems , 2013, IEEE Communications Surveys & Tutorials.

[23]  Ranveer Chandra,et al.  Exploring Indoor White Spaces in Metropolises , 2017, ACM Trans. Intell. Syst. Technol..

[24]  T. Maseng,et al.  Digital Broadcasting: Increasing the Available White Space Spectrum Using TV Receiver Information , 2012, IEEE Vehicular Technology Magazine.

[25]  W. Hoeffding A Combinatorial Central Limit Theorem , 1951 .

[26]  Tanim M. Taher,et al.  Long-term spectral occupancy findings in Chicago , 2011, 2011 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).

[27]  Hein Meling,et al.  A paradigm comparison for collecting TV channel statistics from high-volume channel zap events , 2011, DEBS '11.

[28]  Virgílio A. F. Almeida,et al.  A hierarchical characterization of a live streaming media workload , 2006, TNET.

[29]  Tri Nguyen,et al.  A cognitive radio TV prototype for effective TV spectrum sharing , 2017, 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).

[30]  Kalle Ruttik,et al.  Effect of secondary transmission on primary pilot carriers in overlay cognitive radios , 2013, 8th International Conference on Cognitive Radio Oriented Wireless Networks.

[31]  Eylem Ekici,et al.  Cooperative Spectrum Sensing in Cognitive Radio Networks Using Multidimensional Correlations , 2014, IEEE Transactions on Wireless Communications.

[32]  Bo Gao,et al.  Incentivizing spectrum sensing in database-driven dynamic spectrum sharing , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[33]  Kate Harrison,et al.  How Much White-Space Capacity Is There? , 2010, 2010 IEEE Symposium on New Frontiers in Dynamic Spectrum (DySPAN).

[34]  K. Ramchandran,et al.  Detecting primary receivers for cognitive radio applications , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[35]  Bo Gao,et al.  An Overview of Dynamic Spectrum Sharing: Ongoing Initiatives, Challenges, and a Roadmap for Future Research , 2016, IEEE Transactions on Cognitive Communications and Networking.

[36]  Kang Yong Lee,et al.  Reducing Channel Zapping Time in IPTV Based on User's Channel Selection Behaviors , 2010, IEEE Transactions on Broadcasting.

[37]  Mingyan Liu,et al.  Mining Spectrum Usage Data: A Large-Scale Spectrum Measurement Study , 2009, IEEE Transactions on Mobile Computing.

[38]  Bruce M. Maggs,et al.  An analysis of live streaming workloads on the internet , 2004, IMC '04.

[39]  Edward W. Knightly,et al.  WATCH: WiFi in Active TV Channels , 2016, IEEE Trans. Cogn. Commun. Netw..

[40]  Lei Shi,et al.  CellTV—On the Benefit of TV Distribution Over Cellular Networks: A Case Study , 2013, IEEE Transactions on Broadcasting.

[41]  Dan McCloskey,et al.  Chicago spectrum occupancy measurements & analysis and a long-term studies proposal , 2006, TAPAS '06.

[42]  Long Zhang,et al.  Interference-constrained access opportunity distribution for secondary communication in TV white space , 2015, 2015 IEEE Wireless Communications and Networking Conference (WCNC).

[43]  Angela Sara Cacciapuoti,et al.  Optimal Database Access for TV White Space , 2016, IEEE Transactions on Communications.

[44]  Shridhar Mubaraq Mishra Maximizing available spectrum for cognitive radios , 2009 .

[45]  Soung Chang Liew,et al.  Blind Known Interference Cancellation , 2011, IEEE Journal on Selected Areas in Communications.

[46]  Lin Gao,et al.  Spectrum Reservation Contract Design in TV White Space Networks , 2015, IEEE Transactions on Cognitive Communications and Networking.

[47]  M. Newman Power laws, Pareto distributions and Zipf's law , 2005 .

[48]  Andra Leurdijk,et al.  Interactive TV narratives: Opportunities, progress, and challenges , 2008, TOMCCAP.

[49]  Xianming Qing,et al.  Spectrum Survey in Singapore: Occupancy Measurements and Analyses , 2008, 2008 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom 2008).

[50]  Jean C. Walrand,et al.  An introduction to queueing networks , 1989, Prentice Hall International editions.

[51]  Andreas Achtzehn,et al.  TV White Space in Europe , 2012, IEEE Transactions on Mobile Computing.