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 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 chipsets. Small testbed - But focus on: Extensive measurement Real-time behavior

Background - Link categories

Overview 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 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 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