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Practical Conflict Graphs in the Wild
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.
Hardware Requirements:
• System : Pentium IV 2.4 GHz.
• Hard Disk ` : 40 GB.
• Floppy Drive : 1.44 Mb.
• Monitor : 15 VGA Colour.
• Mouse : Logitech.
• RAM : 256 Mb.
Software Requirements:
• Operating system : - Windows XP.
• Front End : - JSP
• Back End : - SQL Server
Software Requirements:
• Operating system : - Windows XP.
• Front End : - .Net
• Back End : - SQL Server

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Practical conflict graphs in the wild

  • 1. Practical Conflict Graphs in the Wild 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
  • 2. reliability by reducing the impact of accumulative interference. Finally, we propose a graph augmentation technique to address remaining accumulative interference. Hardware Requirements: • System : Pentium IV 2.4 GHz. • Hard Disk ` : 40 GB. • Floppy Drive : 1.44 Mb. • Monitor : 15 VGA Colour. • Mouse : Logitech. • RAM : 256 Mb. Software Requirements: • Operating system : - Windows XP. • Front End : - JSP • Back End : - SQL Server Software Requirements: • Operating system : - Windows XP.
  • 3. • Front End : - .Net • Back End : - SQL Server