Cognitive radio, which promises higher spectrum efficiency, found its first realizations in the form of TV white spectrum (TVWS) communications. With the switch-over from analog to digital TV, VHF (54-216 MHz) and UHF bands (470 MHz-698 MHz in US and 470-790 MHz in Europe) have become available for opportunistic use.  White space devices (WSDs) after identifying that these bands are not used by the licensed services (e.g., Digital Terrestrial TV) can transmit in these bands.

Rather than a distributed sensing approach, current regulations require WSDs to be location-aware, connect to an authorized white spectrum database (WSDB) and acquire all operation parameters (a list of idle channels and corresponding maximum transmission power) from the WSDB. In US, there are several WDBS, which also provide APIs to access their data.  Some of these WSDB providers are:

• Google, 
• Spectrum Bridge, 
• Microsoft, 
• iconectiv, 
• Keybridge, 

The aim of this thesis is :

(1) to find out the complete list of these databases in US, in Europe, or elsewhere,
(2) to survey the design principles and operation of these databases, 
(3) to explore the capabilities of the APIs they provide, and
(4) to collect some spectrum data using the APIs from these WSDBs for several purposes.

This thesis includes both literature survey and programming.

Reading:

• W. Edwards, J. Gebauer, B. Reinicke. White Space Networks: Architecture, Application, and Opportunity, IEEE Computer, December 2014.
• Rohan Murty, R. Chandra, T. Moscibroda, and P. Bahl, SenseLess: A Database-Driven White Spaces Network, in IEEE/ACM Transactions of Mobile Computing, ACM/IEEE, February 2012.
• H.Birkan Yilmaz, S. Bayhan, T. Tugcu, and F. Alagöz, Radio Environment Map As Enabler for Practical Cognitive Radio Network, IEEE Communications, vol.51, no.12, Dec. 2013.
• Ayon Chakraborty and Samir R. Das, Measurement-Augmented Spectrum Databases for White Space Spectrum, the 10th ACM International on Conference on emerging Networking Experiments and Technologies (CONEXT 2014), pp. 67-74, December 2014.
• Chunxiao Jiang, Yan Chen, and K. J. Ray Liu, Data-Driven Optimal Throughput Analysis for Route Selection in Cognitive Vehicular Networks, IEEE Journal on Selected Areas in Communications, 32(11):2149-2162, November 2014.