Physical Interference Driven Dynamic Spectrum Management

Yang, Liwei; Cao, Lili; Zheng, Heather

doi:10.1109/dyspan.2008.47

Cited by 45 publications

(38 citation statements)

References 17 publications

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“…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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Section: Extension To Physical Interference Modelmentioning

confidence: 99%

Spatial Spectrum Access Game

Chen

Huang

2017

Handbook of Cognitive Radio

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“…The second category builds coverage-based conflict graphs based on propagation models, either with rule-of-thumb parameters [6], [21], [22] or calibrated by on-site measurements [53]. However, no one has used real-world measurements to evaluate the conflict graph accuracy.…”

Section: Conflict Graphs and Interference Modelsmentioning

confidence: 99%

“…Here, represents the percentage of coverage holes that a spectrum user is willing to tolerate to maximize capacity [42]. When , (1) reduces to the minimal SINR-based criterion [10], [12], [21].…”

Section: Constructing Conflict Graphsmentioning

confidence: 99%

“…This type of per-link signal measurement is feasible for indoor WLANs [8], [13]- [18], but impractical for outdoor networks targeted by spectrum markets. Existing proposals build artificial conflict graphs using a simple distance-based criterion [3], [19]- [21] or using signal strengths generated by simple RF propagation models with rule-of-thumb parameters [6], [22]. These simplifications, however, produce incorrect interference estimates that lead to poor performance [23], [24].…”

Section: Introductionmentioning

confidence: 99%

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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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“…Also, the vast amount of measurement data can be used as input for DSA enabled radios to facilitate the search for the appropriate white space. There exists already quite some literature that describes how to allocate [2], [3], [4], [5], [6], [7], manage [8], [9], [10], [11], standardize [12], [13], [14], [15], and use the spectrum as secondary user [16], [17], [18], [19] (e. g. Spectrum User Rights (SUR), Dynamic Spectrum Access (DSA), Dynamic Spectrum Management (DSM)) in an efficient way. Moreover, how it affects current regulation [20], [21], [22], [23], [24], [25], [26], [27] that argue the need for a monitoring network to assure compliance of (DSA) radio equipment with regulatory rules [20], [23].…”

Section: Introductionmentioning

confidence: 99%

A Monitoring Network for Spectrum Governance

Schiphorst¹,

Slump²

2010

Frequenz

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-Dynamic Spectrum Access (DSA) is an exciting new technology, which has introduced a paradigm shift in spectrum access. As a result it also changes the role of the regulator. On one hand the scarce radio spectrum should be used in an optimal way, so that society is best served. On the other hand interference between users and between networks should be avoided. For that reason rules have to be defined for spectrum use. This topic is called spectrum governance. For evaluation and to check whether devices obey the rules, a monitoring system is needed. In this paper, we propose to use a fleet of mobile monitoring vehicles for this purpose.

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Physical Interference Driven Dynamic Spectrum Management

Cited by 45 publications

References 17 publications

Spatial Spectrum Access Game

Spatial Spectrum Access Game

Practical Conflict Graphs in the Wild

A Monitoring Network for Spectrum Governance

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