Practical conflict graphs for dynamic spectrum distribution

ZhouXia,; ZhangZengbin,; WangGang,; YuXiaoxiao,; ZhaoBen, Y; ZhengHaitao,

doi:10.1145/2494232.2465545

Cited by 26 publications

(24 citation statements)

References 45 publications

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“…3 shows the probability density function (PDF) of prediction errors and its Gaussian approximation using the GoogleWiFi dataset and the Street model. The same trend holds for other datasets and models [46], and we omit those results for brevity. Table II lists the standard deviation of the prediction error under each model and dataset.…”

Section: B Signal Prediction Resultssupporting

confidence: 64%

Practical Conflict Graphs in the Wild

Zhou

Zhang

Wang

et al. 2015

IEEE/ACM Trans. Networking

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Abstract-Today, most spectrum allocation algorithms use conflict graphs to capture interference conditions. The use of conflict graphs, however, is often questioned by the wireless community for two reasons. First, building accurate conflict graphs requires significant overhead, and hence does not scale to outdoor networks. Second, conflict graphs cannot properly capture accumulative interference. In this paper, we use large-scale measurement data as ground truth to understand how severe these problems are and whether they can be overcome. We build "practical" conflict graphs using measurement-calibrated propagation models, which remove the need for exhaustive signal measurements by interpolating signal strengths using calibrated models. Calibrated models are imperfect, and we study the impact of their errors on multiple steps in the process, from calibrating propagation models, predicting signal strengths, to building conflict graphs.At each step, we analyze the introduction, propagation, and final impact of errors by comparing each intermediate result to its ground-truth counterpart. Our work produces several findings. Calibrated propagation models generate location-dependent prediction errors, ultimately producing conservative conflict graphs. While these "estimated conflict graphs" lower spectrum utilization, their conservative nature improves reliability by reducing the impact of accumulative interference. Finally, we propose a graph augmentation technique to address remaining accumulative interference.

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Section: B Signal Prediction Resultssupporting

confidence: 64%

Practical Conflict Graphs in the Wild

Zhou

Zhang

Wang

et al. 2015

IEEE/ACM Trans. Networking

Self Cite

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show abstract

“…Moreover, by carefully constructing the interference edges via the reality check approach in [32] or the measurement-calibrated propagation scheme in [33], the protocol interference model can provide a good approximation to the physical interference model that captures the continuous nature of interference and takes into account the accumulated interference from multiple concurrent transmitters [34], [35].…”

Section: Extension To Physical Interference Modelmentioning

confidence: 99%

Spatial Spectrum Access Game

Chen

Huang

2017

Handbook of Cognitive Radio

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Abstract-A key feature of wireless communications is the spatial reuse. However, the spatial aspect is not yet well understood for the purpose of designing efficient spectrum sharing mechanisms. In this paper, we propose a framework of spatial spectrum access games on directed interference graphs, which can model quite general interference relationship with spatial reuse in wireless networks. We show that a pure Nash equilibrium exists for the two classes of games: (1) any spatial spectrum access games on directed acyclic graphs, and (2) any games satisfying the congestion property on directed trees and directed forests. Under mild technical conditions, the spatial spectrum access games with random backoff and Aloha channel contention mechanisms on undirected graphs also have a pure Nash equilibrium. We also quantify the price of anarchy of the spatial spectrum access game. We then propose a distributed learning algorithm, which only utilizes users' local observations to adaptively adjust the spectrum access strategies. We show that the distributed learning algorithm can converge to an approximate mixed-strategy Nash equilibrium for any spatial spectrum access games. Numerical results demonstrate that the distributed learning algorithm achieves up to 100% performance improvement over a random access algorithm.

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“…The wireless signal of the channel used by each SU covers a certain area, and two SUs i and j interfere with each other if they use the same channel k and (i, j) ∈ E k . Conflict graphs can be built using measurement-calibrated propagation models [26].…”

Section: Network Model and Preliminariesmentioning

confidence: 99%

Core-Selecting Secondary Spectrum Auctions

Zhu¹,

et al. 2014

IEEE J. Select. Areas Commun.

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Abstract-In a secondary spectrum market, the utility of a secondary user often depends on not only whether it wins, but also which channels it wins. Combinatorial auctions are a natural fit here to allow secondary users to bid for combinations of channels. In this context, the VCG mechanism constitutes a generic auction that uniquely guarantees both truthfulness and efficiency. There also exists related auction design that relaxes efficiency due to perceived complexity issues, and focuses on truthfulness. Starting with new empirical evidences on the complexity issue, we propose to design core-selecting auctions instead, which resolve VCG's vulnerability to collusion and shill bidding, and improve seller revenue. While the VCG type of auctions are unique in guaranteeing both efficiency and truthfulness, we prove that our core-selecting auctions are unique in guaranteeing both efficiency and shill-proofness, and always outperform VCG auctions in terms of seller revenue generated. Employing linear programming and quadratic programming techniques, we design two payment rules for minimizing the incentives of bidders to deviate from truth telling.

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Practical conflict graphs for dynamic spectrum distribution

Cited by 26 publications

References 45 publications

Practical Conflict Graphs in the Wild

Practical Conflict Graphs in the Wild

Spatial Spectrum Access Game

Core-Selecting Secondary Spectrum Auctions

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