Private and truthful aggregative game for large-scale spectrum sharing

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

16 Scopus Citations
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Author(s)

  • Pan Zhou
  • Wenqi Wei
  • Kaigui Bian
  • Yuchong Hu
  • Qian Wang

Detail(s)

Original languageEnglish
Article number7835215
Pages (from-to)463-477
Journal / PublicationIEEE Journal on Selected Areas in Communications
Volume35
Issue number2
Publication statusPublished - 1 Feb 2017
Externally publishedYes

Abstract

Thanks to the rapid development of information technology, the size of a wireless network is becoming larger and larger, which makes spectrum resources more precious than ever before. To improve the efficiency of spectrum utilization, game theory has been applied to study efficient spectrum sharing for a long time. However, the scale of wireless networks in existing studies is relatively small. In this paper, we introduce a novel game called aggregative game and use it to model spectrum sharing in large-scale, heterogeneous, and dynamic networks. Meanwhile, the massive usage of the spectrum leads to easier divulgence of privacy of spectrum users, which calls for privacy and truthfulness guarantees. In a large decentralized scenario, each user has no priori about other users' channel access decisions, which forms an incomplete information game. A 'weak mediator,' e.g., the base station or licensed spectrum regulator, is introduced and it turns the incomplete spectrum sharing game into a complete one. This is essential in reaching a Nash equilibrium (NE). By utilizing past channel access experience, we propose an online learning algorithm to improve the utility of each user. We show that the learning algorithm achieves an NE over time and provides no regret guarantee for each user. Specifically, our mechanism admits an approximate ex-post NE, and is joint differentially private and incentive-compatible. Efficiency of the approximate NE is evaluated, and innovative scaling law results are disclosed. We also provide simulation results to verify our analysis.

Research Area(s)

  • aggregative game, differential privacy, heterogeneous, online learning, Spectrum sharing, truthfulness

Bibliographic Note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to lbscholars@cityu.edu.hk.

Citation Format(s)

Private and truthful aggregative game for large-scale spectrum sharing. / Zhou, Pan; Wei, Wenqi; Bian, Kaigui et al.

In: IEEE Journal on Selected Areas in Communications, Vol. 35, No. 2, 7835215, 01.02.2017, p. 463-477.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review