Measurement and Analysis of Link
Quality in Wireless Networks:
An Application Perspective
V. Kolar, Saquib Razak,
P. Mahonen, N. Abu-Ghazaleh
Carnegie Mellon, Qatar
RWTH Aachen, Germany
Motivation
Designing protocols in Wireless Networks is challenging
• Wireless propagation, link errors, MAC effects, ...
• Small changes in topology and environment -> drastic effects
Wireless Link Quality: A critical property for many higher layer
protocols and applications
Motivation - Link quality
Most efficient protocols are link-quality aware
• Even higher layer apps!
• Rate-adaptation, routing, video encoding, ...
Common Methodology:
• Measure link-quality and act on it
Common metrics:
• Received Signal Strength (RSS)
• Error Rate (PER, BER, ...)
Motivation - Link quality
Simulation, Theory, Data sheets, ...
But, in an operational network,...
• Real-time link quality estimates
Motivation
Many open questions about link-quality
• Statistical properties:
o Distribution: Constant, normal, log-normal?
o Temporal properties: Independent, memory?
• How often should we measure?
Contribution
Statistical analysis of RSS and error-rates
• Distribution and temporal properties
Specific focus on protocols that measure and use link-quality
• Is it feasible to measure these parameters in real-time?
• If so, how often should we measure? (Stale)
• What distribution should we assume in real-time?
Real-time link-quality monitoring framework and applications
Overview
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Motivation
Contributions
Testbed and background
Statistical analysis of link-quality
o Signal Strength
o Error-rate
• Real-time measurement framework
o Example applications
• Conclusions
Testbed
Indoor wireless mesh network
8 Laptops and Soekris boards with 802.11 chipsets.
Small testbed - But focus on:
• Extensive measurement
• Real-time behavior
Background - Link categories
Overview
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Motivation
Contributions
Testbed and background
Statistical analysis of link-quality
o Signal Strength
o Error-rate
• Real-time measurement framework
o Example applications
• Conclusions
Link error rates
Deviation from theoretical models.
Categories have signature patterns
• Strong links - Low and constant
PER, small variance.
• Gray zone - Varies widely (from
0.2 to 0.9).
• Weak links - High with
acceptable variation.
Distribution and independence of RSS
General methodology in models and protocols:
• RSS is constant or follows a statistical distribution
Needs verification
• Which distribution does it follow?
• Does link category affect these statistical properties?
Analysis methodology:
• Record RSS values for various links (with different tx powers)
• Collect in 1.5 second interval
• Perform distribution tests (KS-test, Log-likelihood,...)
• Perform independence tests (Auto-correlation Function)
Distribution and independence of RSS
Results:
• Weak links - Coarsely approximated as log-normal distribution.
• Strong links - Well-approximated as a constant.
Conclusion for application protocols:
• First identify the link category
• Then model link distribution
Distribution of RSS
Distribution and independence of PER
Strong links - Constant
Gray-zone links - Have memory and bi-modally distributed
Weak links - i.i.d. random variable from Log-normal, Beta or Weibull
distributions.
Effect of transmission-rate
Myth: Stronger modulation has
lesser PER
• Basis for many rate-control
application
Our result:
Not for all observed RSS/SNRs
Reason:
Stronger modulation takes longer
time to send same packet ->
Higher chances for fading
Overview
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Motivation
Contributions
Testbed and background
Statistical analysis of link-quality
o Signal Strength
o Error-rate
• Real-time measurement framework
o Example applications
• Conclusions
Real-time monitoring framework
Real-time measurement and estimation poses practical challenges
• Coordination between the nodes
• Measurement overheads.
Contribution: System Architecture and Applications in our testbed
System Architecture
Wireless data plane and wired control plane
Each node runs
• Modified madwifi at kernel
o Real-time collection of lower level packet data
• Control server at user-space
o Executes control and measurement commands
Distributed: Any node can query server for link-statistics
System Architecture
Coordinator
• Polls receiver traces
o Non-intrusive, light-weight.
o Statistical summary of RSS, PER, traffic, etc.
• PER measurement
o Complex and intrusive (night-times, traffic is lesser)
o Broadcast based (and not unicast)
o Lots of room for optimization
Applications
Measurement framework is useful for building many applications
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Power-control
Network monitoring
Rate control
Routing,
Cross-layer video-MAC, etc...
App 1: Power-control protocol
Observation:
PER is stable and constant for a strong link.
• RSS values above the cross-over point does not decrease PER
Idea:
Reduce power till we are in the strong zone.
Reduces the number of exposed terminals.
Methodology:
1. Each link maintains RSS and PER from PER-measurement
2. Instruct sender to decrease power till we are near the cross-over
point.
App 1: Power-control protocol
Exposed terminals are eliminated in scenarios 1,2 and 3.
Does not adversely affect in other cases.
App2: Network monitoring tool
Plots real-time data for link quality graphs
• RSSI, PER time-line
• Their distributions
Visually intuitive and real-time network status
Conclusions and Future Work
Empirical analysis of link quality with focus on measurement-based
models and protocols
• Statistical properties vary with link category
o Bi-modal PER in gray-zone
o Constant RSS for strong links
• Mechanisms to identify link-category
• Modulation vs PER
o Robust modulation does not always reduce PER
Real-time monitoring framework and applications
Future Work
• Detailed analysis using testbed with Software-defined Radios
• Real-time detection of MAC interactions.
o Hidden terminals, Capture effect, ...
• Long-term plan:
o Realistic low-overhead measurement mechanisms
o Applications: Network planning, provisioning, higher layer
protocols