NDLI: Spectrum-efficient stochastic channel assignment for opportunistic networks

Content Provider	IEEE Xplore Digital Library
Author	Abdel-Rahman, M.J. Fujun Lan Krunz, M.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Arizona, Tucson, AZ, USA (Abdel-Rahman, M.J.; Krunz, M.) \|\| Dept. of Syst. & Ind. Eng., Univ. of Arizona, Tucson, AZ, USA (Fujun Lan)
Abstract	The uncertainty in channel quality due to fading and shadowing along with the unpredictability of primary user (PU) activity make channel assignment in opportunistic spectrum access (OSA) networks quite challenging. In this paper, we propose two per-link channel assignment models under channel uncertainty: a static single-stage and an adaptive two-stage. In the static model, channel assignment is performed once, such that the rate demands are met with a probability greater than a certain threshold. This model is appropriate for a distributed network with no centralized spectrum manager. The adaptive model is a two-stage assignment model, where the initial assignment may be corrected once the uncertainties are partially revealed, such that the excess spectrum is returned back to the spectrum manager. This adaptive model is more appropriate when a centralized spectrum manager is available. Our channel assignment algorithms account for adjacent channel interference (ACI) by introducing guard-bands between adjacent channels that are assigned to different links. These algorithms aim at maximizing the spectral efficiency, considering the impact of guard-bands. The static ACI-aware channel assignment problem is formulated as a chance-constrained stochastic subset-sum problem (CSSP), and the adaptive assignment problem is formulated as a two-stage chance-constrained stochastic subset-sum problem with recourse (CSSPR). We develop heuristic algorithms for both models and test their performance. Preliminary results demonstrate that the proposed heuristic algorithms are highly efficient.
Sponsorship	IEEE Commun. Soc.
Starting Page	1272
Ending Page	1277
File Size	330341
Page Count	6
File Format	PDF
ISBN	9781479913534
DOI	10.1109/GLOCOM.2013.6831249
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Channel allocation Uncertainty Adaptation models Stochastic processes Heuristic algorithms Throughput Cognitive radio subset-sum problem Channel assignment opportunistic spectrum access spectrum efficiency stochastic optimization
Content Type	Text
Resource Type	Article

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