creator Xu, Ping advisor Li, Xiang-Yang All wireless devices rely on access to the radio frequency spectrum, which has been chronically regulated by static spectrum allocation policies. With the recent fast growing of spectrum-based services and devices, the remaining spectrum available for future wireless services is being exhausted, known as the spectrum scarcity problem. The current fixed spectrum allocation scheme leads to significant spectrum white spaces (including spectral, temporal, and geographic), where many allocated spectrum blocks are used only in certain geographical areas and/or in brief periods of time. In this work, we design and analyze variant spectrum allocation algorithms for better spectrum utilization and study some fundamental performance bounds for networks with opportunistic spectrum utilization. We first propose spectrum allocation algorithms for offline model, in which all spectrum requests are known when allocation decision is made. Then we also addresse the problems in online model, where allocation decision should be made when only a few spectrum requests are known. In the online model, we focus on two different cases. The first one assumes no statistic of future spectrum requests are known, and the second one assumes some statistic is known or can be learned. For all these models, we design efficient spectrum allocation methods and analytically prove most of them are asymptotically optimal. Our extensive simulation results also verify our theoretical conclusion. Submitted by Dana Lamparello (dlampare@iit.edu) on 2011-11-30T22:23:42Z No. of bitstreams: 2 Ping Xu_10422395.pdf: 738327 bytes, checksum: 9032a5fcb6f9210353e4943904a612e7 (MD5) Advisor's signature.pdf: 268870 bytes, checksum: d196e2a9f6dcdb8213dbeedf0b4e74e1 (MD5) Made available in DSpace on 2011-11-30T22:23:42Z (GMT). No. of bitstreams: 2 Ping Xu_10422395.pdf: 738327 bytes, checksum: 9032a5fcb6f9210353e4943904a612e7 (MD5) Advisor's signature.pdf: 268870 bytes, checksum: d196e2a9f6dcdb8213dbeedf0b4e74e1 (MD5) Previous issue date: 2011-05 Ph.D. in Computer Science, May 2011 2011-04-26 2011-05 http://hdl.handle.net/10560/2351 en Dissertation born digital application/pdf In Copyright http://rightsstatements.org/page/InC/1.0/ Restricted Access CS / Computer Science Illinois Institute of Technology Affiliated department