dc.contributor.author | Li, William Wei-Liang | |
dc.contributor.author | Zhang, Ying Jun (Angela) | |
dc.contributor.author | So, Anthony Man-Cho | |
dc.contributor.author | Win, Moe Z. | |
dc.date.accessioned | 2011-11-29T19:33:53Z | |
dc.date.available | 2011-11-29T19:33:53Z | |
dc.date.issued | 2010-07 | |
dc.date.submitted | 2009-07 | |
dc.identifier.issn | 1053-587X | |
dc.identifier.uri | http://hdl.handle.net/1721.1/67306 | |
dc.description.abstract | Adaptive orthogonal frequency division multiple access (OFDMA) has recently been recognized as a promising technique for providing high spectral efficiency in future broadband wireless systems. The research over the last decade on adaptive OFDMA systems has focused on adapting the allocation of radio resources, such as subcarriers and power, to the instantaneous channel conditions of all users. However, such “fast” adaptation requires high computational complexity and excessive signaling overhead. This hinders the deployment of adaptive OFDMA systems worldwide. This paper proposes a slow adaptive OFDMA scheme, in which the subcarrier allocation is updated on a much slower timescale than that of the fluctuation of instantaneous channel conditions. Meanwhile, the data rate requirements of individual users are accommodated on the fast timescale with high probability, thereby meeting the requirements except occasional outage. Such an objective has a natural chance constrained programming formulation, which is known to be intractable. To circumvent this difficulty, we formulate safe tractable constraints for the problem based on recent advances in chance constrained programming. We then develop a polynomial-time algorithm for computing an optimal solution to the reformulated problem. Our results show that the proposed slow adaptation scheme drastically reduces both computational cost and control signaling overhead when compared with the conventional fast adaptive OFDMA. Our work can be viewed as an initial attempt to apply the chance constrained programming methodology to wireless system designs. Given that most wireless systems can tolerate an occasional dip in the quality of service, we hope that the proposed methodology will find further applications in wireless communications. | en_US |
dc.description.sponsorship | University Grants Committee (Hong Kong, China) (Competitive Earmarked Research Grant (Project number 418707)) | en_US |
dc.description.sponsorship | University Grants Committee (Hong Kong, China) (Competitive Earmarked Research Grant (Project number 419509)) | en_US |
dc.description.sponsorship | Shun Hing Institute of Advanced Engineering (Hong Kong, China) | en_US |
dc.description.sponsorship | Chinese University of Hong Kong | en_US |
dc.description.sponsorship | National Science Foundation (U.S.) (Grant ECCS-0636519) | en_US |
dc.description.sponsorship | National Science Foundation (U.S.) (grant ECCS-0901034) | en_US |
dc.description.sponsorship | United States. Office of Naval Research (Presidential Early Career Award for Scientists and engineers (PECASE) N00014-09-1-0435) | en_US |
dc.description.sponsorship | Massachusetts Institute of Technology. Institute for Soldier Nanotechnologies | en_US |
dc.language.iso | en_US | |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1109/tsp.2010.2046434 | en_US |
dc.rights | Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. | en_US |
dc.source | IEEE | en_US |
dc.title | Slow Adaptive OFDMA Systems Through Chance Constrained Programming | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Li, William Wei-Liang et al. “Slow Adaptive OFDMA Systems Through Chance Constrained Programming.” IEEE Transactions on Signal Processing 58 (2010): 3858-3869. ©2011 IEEE. | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Laboratory for Information and Decision Systems | en_US |
dc.contributor.approver | Win, Moe Z. | |
dc.contributor.mitauthor | Win, Moe Z. | |
dc.relation.journal | IEEE Transactions on Signal Processing | en_US |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
dspace.orderedauthors | Li, William Wei-Liang; Zhang, Ying Jun (Angela); So, Anthony Man-Cho; Win, Moe Z. | en |
dc.identifier.orcid | https://orcid.org/0000-0002-8573-0488 | |
mit.license | PUBLISHER_POLICY | en_US |
mit.metadata.status | Complete | |