Optimal Spectrum Auction Design with 2-D Truthful Revelations under Uncertain Spectrum Availability
Abstract
In this paper, we propose a novel sealed-bid auction framework to address the problem of dynamic spectrum allocation in cognitive radio (CR) networks. We design an optimal auction mechanism that maximizes the moderator's expected utility, when the spectrum is not available with certainty. We assume that the moderator employs collaborative spectrum sensing in order to make a reliable inference about spectrum availability. Due to the presence of a collision cost whenever the moderator makes an erroneous inference, and a sensing cost at each CR, we investigate feasibility conditions that guarantee a non-negative utility at the moderator. Since the moderator fuses CRs' sensing decisions to obtain a global inference regarding spectrum availability, we propose a novel strategy-proof fusion rule that encourages the CRs to simultaneously reveal truthful sensing decisions, along with truthful valuations to the moderator. We also present tight theoretical bounds on instantaneous network throughput achieved by our auction mechanism. Numerical examples are presented to provide insights into the performance of the proposed auction under different scenarios.
Recommended Citation
V. S. Nadendla et al., "Optimal Spectrum Auction Design with 2-D Truthful Revelations under Uncertain Spectrum Availability," IEEE/ACM Transactions on Networking, vol. 25, no. 1, pp. 420 - 433, Institute of Electrical and Electronics Engineers (IEEE), Feb 2017.
The definitive version is available at https://doi.org/10.1109/TNET.2016.2589278
Department(s)
Computer Science
Keywords and Phrases
Auctions; Cognitive Radio Networks; Spectrum Allocation; Spectrum Availability Uncertainty; Spectrum Sensing
International Standard Serial Number (ISSN)
1063-6692; 1558-2566
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Feb 2017