FLUID: Improving Throughputs in Enterprise Wireless LANs through Flexible Channelization

Rayanchu, Shravan; Shrivastava, Vivek; Banerjee, Suman; Chandra, Ranveer

doi:10.1109/tmc.2012.89

Cited by 55 publications

(71 citation statements)

References 21 publications

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“…The first category uses per-link signal measurements to capture interference conditions among individual links, using either active measurements [5,6,26,33,34,37,39,43], or passive measurements [8,21,28,45]. These link-based conflict graphs are for indoor WiFi networks where transmission links are known a priori.…”

Section: Related Workmentioning

confidence: 99%

“…Given the complex nature of RF propagation, it requires detailed measurements covering all combinations of sender/receiver locations. This type of per-link signal measurement is feasible for indoor WLANs [5,6,26,33,39,43,45], but impractical for the outdoor networks targeted by spectrum markets. As a substitute, most current proposals build artificial conflict graphs using a simple distance-based criterion [7,10,48] or from signal strength values generated from simple RF propagation models with rule of thumb parameters [18,32].…”

Section: Introductionmentioning

confidence: 99%

“…Our work differs from existing works on constructing conflict graphs. First, focusing on outdoor environments, our work differs from prior work [5,6,26,33,39,43,45] that build indoor conflict graphs using exhaustive signal measurements. Second, our work targets dynamic spectrum markets where users requesting spectrum are located at unplanned places and the resulting conflict graph can be of arbitrary shape.…”

Section: Introductionmentioning

confidence: 99%

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Practical conflict graphs for dynamic spectrum distribution

ZhouXia¹,

ZhangZengbin²,

WangGang³

et al. 2013

SIGMETRICS Perform. Eval. Rev.

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Most spectrum distribution proposals today develop their allocation algorithms that use conflict graphs to capture interference relationships. The use of conflict graphs, however, is often questioned by the wireless community because of two issues. First, building conflict graphs requires significant overhead and hence generally does not scale to outdoor networks, and second, the resulting conflict graphs do not capture accumulative interference.In this paper, we use large-scale measurement data as ground truth to understand just how severe these issues are in practice, 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. These propagation models are imperfect, and we study the impact of their errors by tracing the impact on multiple steps in the process, from calibrating propagation models to predicting signal strength and 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 generated from measurements. Our work produces several findings. Calibrated propagation models generate location-dependent prediction errors, ultimately producing conservative conflict graphs. While these "estimated conflict graphs" lose some spectrum utilization, their conservative nature improves reliability by reducing the impact of accumulative interference. Finally, we propose a graph augmentation technique that addresses any remaining accumulative interference, the last missing piece in a practical spectrum distribution system using measurement-calibrated conflict graphs.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Practical conflict graphs for dynamic spectrum distribution

ZhouXia¹,

ZhangZengbin²,

WangGang³

et al. 2013

SIGMETRICS Perform. Eval. Rev.

View full text Add to dashboard Cite

show abstract

“…The first category uses conflict graphs that capture interference conditions between link pairs. They build conflict graphs using either a simple distance-based criterion [19], [20] or exhaustive per-link measurements in an active [8], [13]- [17], [24], [49] or passive [18], [50]- [52] manner. These link-based conflict graphs are for indoor WiFi networks where transmission links are known.…”

Section: Conflict Graphs and Interference Modelsmentioning

confidence: 99%

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.

show abstract

“…However, the channel hopping schemes are not effective due to protocol overheads. Also, the protection mechanisms of [14][15][16] require modification of the current standard. In [17,18], the authors proposed interference-aware self-optimizing receiver design, but the solutions are limited in their ability to mitigate the interference effect on the receiver.…”

Section: Introductionmentioning

confidence: 99%