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Repeated Auctions with Learning for Spectrum Access in Cognitive Radio Networks

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Zhu Han, Rong Zheng, Vincent H. Poor

Information Theory · Submitted 12 Oct 2009

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim As demonstrated through extensive simulations, the proposed distributed scheme outperforms a myopic one-stage algorithm, and can achieve a good balance between efficiency and fairness.

how it is stated · asserted 1.00 read the record →

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arXiv ID0910.2240 (version 1)
Pith ID6OUWSXYI, Pith's permanent record of this paper and its review
CitationRong Zheng, Vincent H. Poor, Zhu Han. "Repeated Auctions with Learning for Spectrum Access in Cognitive Radio Networks." arXiv:0910.2240 (2009). https://pith.science/paper/6OUWSXYI
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,362 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.28 · human participants 0.02 · controlled comparison 0.90 · randomized assignment 0.02 · states quantitative result 0.05 · compares with prior results 0.94 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

In this paper, spectrum access in cognitive radio networks is modeled as a repeated auction game subject to monitoring and entry costs. For secondary users, sensing costs are incurred as the result of primary users' activity. Furthermore, each secondary user pays the cost of transmissions upon successful bidding for a channel. Knowledge regarding other secondary users' activity is limited due to the distributed nature of the network. The resulting formulation is thus a dynamic game with incomplete information. In this paper, an efficient bidding learning algorithm is proposed based on the outcome of past transactions. As demonstrated through extensive simulations, the proposed distributed scheme outperforms a myopic one-stage algorithm, and can achieve a good balance between efficiency and fairness.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 In this paper, spectrum access in cognitive radio networks is modeled as a repeated auction game subject to monitoring and entry costs. background 0.46 0.56 0.04 asserted 1.00
s1 For secondary users, sensing costs are incurred as the result of primary users' activity. background 0.98 0.16 0.02 asserted 1.00
s2 Furthermore, each secondary user pays the cost of transmissions upon successful bidding for a channel. background 0.95 0.12 0.03 asserted 1.00
s3 Knowledge regarding other secondary users' activity is limited due to the distributed nature of the network. background 0.98 0.15 0.02 asserted 1.00
s4 The resulting formulation is thus a dynamic game with incomplete information. background 0.87 0.48 0.03 asserted 1.00
s5 In this paper, an efficient bidding learning algorithm is proposed based on the outcome of past transactions. contribution 0.78 0.92 0.14 asserted 1.00
s6 As demonstrated through extensive simulations, the proposed distributed scheme outperforms a myopic one-stage algorithm, and can achieve a good balance between efficiency and fairness. result 0.98 0.95 0.83 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 1.00.

What was examined, and what was not

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  • full text
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  • references and citing sentences
  • relations to other papers
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  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

In this paper, spectrum access in cognitive radio networks is modeled as a repeated auction game subject to monitoring and entry costs. For secondary users, sensing costs are incurred as the result of primary users' activity. Furthermore, each secondary user pays the cost of transmissions upon successful bidding for a channel. Knowledge regarding other secondary users' activity is limited due to the distributed nature of the network. The resulting formulation is thus a dynamic game with incomplete information. In this paper, an efficient bidding learning algorithm is proposed based on the outcome of past transactions. As demonstrated through extensive simulations, the proposed distributed scheme outperforms a myopic one-stage algorithm, and can achieve a good balance between efficiency and fairness.

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