Slow adaptive OFDMA via stochastic programming
Name
Wei-2009-Slow adaptive OFDMA via stochastic programming.pdf
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212.42 KB
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Checksum (MD5)
10ce31a5f455c2aa0cbcee10e5388ad6
Author(s)
Li, Wei Liang (William)
Zhang, Ying Jun (Angela)
Win, Moe Z
Date Issued
June 2009
Journal
IEEE International Conference on Communications 2009.
Publisher
Institute of Electrical and Electronics Engineers / IEEE Communications Society
Citation
Li, W. L., Y. J. Zhang, and M. Z. Win. “Slow Adaptive OFDMA via Stochastic Programming.” 2009 IEEE International Conference on Communications. Dresden, Germany, 2009. 1-6. WCopyright © 2009, IEEE
Version
Final published version
Abstract
Fueled by the promises of high spectral efficiency, adaptive OFDMA has attracted enormous research interests over the last decade. The significant capacity gain of adaptive OFDMA comes from fast adaptation of resource allocation in response to instantaneous channel conditions. Despite years of efforts to improve the practicality of adaptive OFDMA, such promising technique is still far from real implementation due to the prohibitively high computational complexity and excessive control overhead. This paper is an endeavor to address the problem by proposing a slow adaptation scheme, where resource allocation is adapted on a much slower time scale than the fluctuation of wireless channel fading. Specifically, the slow adaptive OFDMA is formulated into a stochastic programming problem, which adapts resource allocation according to the channel statistics within an adaptation window rather than according to instantaneous channel conditions. By tuning the length of the adaptation window, we could engineer a desirable tradeoff between spectral efficiency and computational complexity. Furthermore, the proposed scheme can be modified to accommodate inelastic traffics. The modification, referred to as "safe" slow adaptation, ensures worst-case data rates to all users. In this work, safe slow adaptation is formulated into a conic linear program, which is efficiently solved via interior-point methods. Through extensive simulations, we show that the proposed schemes drastically reduce the computational complexity and control overheads, while achieving satisfactorily high spectral efficiency and QoS provisioning as their fast-adaptation counterpart does with a much higher cost.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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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.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICC.2009.5199464
