Frame error model in rural Wi-Fi networks

1. CNR - ISTI - Frame error model in rural Wi-Fi networks Frame error model in rural Wi-Fi networks Paolo Barsocchi Gabriele Oligeri Francesco Potortì ISTI C.N.R. – Pisa Research Area Via G. Moruzzi 1, San Cataldo, 56124 Pisa (Italy) Phone: +39-050-315-2053/3070, Fax: +39-050-313-8091 Email: {paolo.barsocchi, gabriele.oligeri, Potorti}@isti.cnr.it

2. CNR - ISTI - Frame error model in rural Wi-Fi networks Related work • Other measurement campaigns have usually been conducted on : • complex network setups hiding the relationship between frame error probability and transmitter-receiver distance • simple scenarios where ARQ algorithm was always used, hiding the underlying frame error process details • Commonly used frame loss models for simulations (ns-2) assume a simple double regression model. This model is widely used, but we do not know of any measurements that try to validate it.

3. CNR - ISTI - Frame error model in rural Wi-Fi networks The goal • Starting from frame-level measurements done in outdoor environment by using IEEE 802.11 channel we: • Study the relationship between power level and distance • Investigate the frame error process. • Examine the relationship between receive power level and frame error process.

4. CNR - ISTI - Frame error model in rural Wi-Fi networks Wi-Fi link characteristics • Nominal rate of 2, 5.5 and 11 Mb/s, for a unidirectional flow of 500, 1000 and 1500-data byte packets. • Long preambles, no fragmentation, RTS/CTS disabled. • Latency produced by the driver and the hardware around 1 ms. • MAC retransmissions limited to 0.

5. . T . R CNR - ISTI - Frame error model in rural Wi-Fi networks Measurements environment • The rural environment was a wide uncultivated field with an unobstructed line of sight, far from buildings, cell phone antennas and power lines. • Transmitter and Receiver positions in our experiments

6. CNR - ISTI - Frame error model in rural Wi-Fi networks Traffic type • CBR flow • frame transmitted every 5 ms • fixed 2, 5.5, and 11 Mb/s rate with fragmentation and retransmission disabled • The receiver checks a sequence number inside the frames and creates a trace of the lost ones. • 200000 frames transmitted for each measure. • We performed several tens of measures in three different locations.

7. CNR - ISTI - Frame error model in rural Wi-Fi networks Propagation model • Differences between propagation models for predicting the power level at the receiver • two-ray propagation model (2RM) • double regression approximation • GSM frequency: the maximum error is 14 dB. • Wi-Fi frequency: the maximum error is 24dB.

8. CNR - ISTI - Frame error model in rural Wi-Fi networks Propagation model • We computed the measured signal level in dB by fitting the observed RSSI values with a -40dB/dec slope for distances greater than the breakpoint • The unit value of the RSSI level scale provided by our cards equals 0.6 dB

9. CNR - ISTI - Frame error model in rural Wi-Fi networks Propagation model • Nodes at 1 m from the ground, 2RM predicts a dip at 16m where: • The received power = power received at 160m • The error with respect to the double regression model is about 24dB.

10. CNR - ISTI - Frame error model in rural Wi-Fi networks Frame error process • Statistical tests aimed at characterising the frame error process in the time domain. • We evaluated the stationarity of the errored frame sequences using the Mann-Kendall test on the traces split into equal length segments. • We found that, at 0.05 significance level, all the traces pass the stationarity test with a segment length of 1000 samples corresponding to 5 seconds. • We considered the autocorrelation of the samples, the burst and gap length distribution, the coefficient of variation of burst and gap lengths and found that all are consistent with a Bernoulli process.

11. CNR - ISTI - Frame error model in rural Wi-Fi networks Frame error process • We tested this conclusion by using a chi-square goodness-of-fit test to test the null hypothesis that the burst and gap lengths are geometrically distributed. • We verified that the null hypothesis is not rejected 90% of times at significance level 5% with a segment length of 1000, which is consistent with the Mann-Kendall test.

12. CNR - ISTI - Frame error model in rural Wi-Fi networks AWGN model • Modelling the propagation channel as a simple AWGN channel provides a good fit with observed results.

13. CNR - ISTI - Frame error model in rural Wi-Fi networks Practical usage • The three horizontal lines show the 8% FER threshold at the receiver at 54, 11 and 1 Mb/s.

14. CNR - ISTI - Frame error model in rural Wi-Fi networks Conclusions • We performed a measurement campaign for measuring Wi-Fi RSSI and packet loss in a rural environment. • We obtained a frame error model that is more accurate than commonly used models. • We suggest that the two-ray CMU Monarch model (used in ns-2) should be substituted with the 2RM we propose. • We found that modelling the propagation channel as a simple AWGN provides a good fit with the observed results.

15. CNR - ISTI - Frame error model in rural Wi-Fi networks Future work • We plan to investigate indoor environments using the same measurement procedure. • First results indicate that the indoor channel is far from Bernoullian, nor it is adequately represented by a Gilbert-Elliott on-off model. • Running some outdoor scenarios using different propagation models, specifically the ns-2 models, a simple on-off model, and the one we propose.